Category: personalized learning

435. Insights From The Field

Posted in access, edtech, educational technology, engagement, equity, higher education, inclusion, learning, online learning, pedagogy, personalized learning, social presence, teaching
March 4, 2026

This podcast is a recording of a panel session featuring three online learning leaders, that was presented at the SUNY Online Learning Summit on Wednesday, February 25, 2026. The panelists are Yakut Gazi, Marina Amini, and Van Davis. Yakut is the Vice Provost for Learning Innovation and Digital Education at Duke University. Marina is the Executive Director of the California Virtual Campus, and Van is the Executive Director of WCET and the Vice President of Digital Learning at WICHE.

Show Notes

Transcript

John: Today’s Tea for Teaching podcast is a recording of a panel session, featuring three online learning leaders, that was presented at the SUNY Online Learning Summit on Wednesday, February 25, 2026.

[MUSIC]

John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

[MUSIC]

John: Welcome to: “Insights from the Field,” a special Tea for Teaching podcast on policy, data, and inclusive collaboration. I’m John Kane, an economist and the director of the Center for Excellence in Learning and Teaching at the State University of New York at Oswego.

Rebecca: I’m Rebecca Mushtare, a designer and the Deputy Dean of Graduate Studies at SUNY Oswego. We’re the hosts of the Tea for Teaching podcast and the moderators for this session. Our guests today are Yakut Gazi, Marina Amini, and Van Davis. Yakut is the Vice Provost for Learning Innovation and Digital Education at Duke University. Marina is the Executive Director of the California Virtual Campus, and Van is the Executive Director of WCET and the Vice President of Digital Learning at WICHE. Welcome Yakut, Marina, and Van.

John: Today’s teas are: …and we’ll start with Yakut.

Yakut: So I’m Turkish originally, and Turkish tea is the big thing. We always have Turkish tea brewing in our houses. So that’s my tea.

John: Very good. And Marina?

Marina: And I’m from Afghanistan originally, so I’m drinking green tea with cardamom.

John: Excellent. And Van?

Van: Well, I’ve already switched over to water today, but my favorite tea is lapsang souchong. I love a nice smoked tea.

John: I do too. I have to keep it separate from my others so they don’t all become smoked flavored tea, but it’s a very nice flavor. And Rebecca?

Rebecca: I have a Pekoe tea, Edinburgh’s Gin Tea and Roses today.

John: And in honor of this recording time in January in upstate New York, I am drinking a spring cherry green tea, [LAUGHTER]…looking forward to somewhat warmer and less snowy weather. So we’ve invited you here today to discuss the future of online education. We’ve seen rapid proliferation and evolution in generative AI platforms since they were publicly introduced just a little over three years ago, leading to concerns about the integrity of online assessments, especially recently given the development and use of AI agents. How should online learning leaders address the risks that this poses to the perceived value of online courses and degrees?

Rebecca: Yakut?

Yakut: It is true that advances in AI created risk for the way traditional education is delivered, and also assessed. The concern about AI undermining validity isn’t unique to online education, though, it affects all of higher education. If you remember, COVID has created a pedagogical emergency. Generative AI created a new one, but I’m not sure if you’re treating it the way it needs to be treated like we treated in the COVID period as an emergency. Online learners, I think, may be actually better positioned to respond, because we have already been engaging in academic innovation and advocating for active learning and authentic assessment for many years. So this may be an opportunity to implement better approaches in a full-scale manner, where the focus becomes the process of learning rather than the product of learning, because that now can be created through the use of AI, and it’s actually being done in all of our classrooms, whether or not we like it. So we, as the learning leaders, need to push the conversation from preventing AI use to designing learning experiences where the use of AI is transparent, ethical, and skill building. So this means creating policies that allow for AI use, like when and how, rather than ban AI use, and also focusing on authentic assessments, project-based work, reflective practice, collaboration, things that AI can support but cannot replace. But I think the challenge is not necessarily modality, like online as a modality is a challenge. We know how to do these things. The challenge is doing these pedagogically sound things at scale. In my mind, this is a moment that poses a risk for online education at scale, more than online education broadly. But then for us to implement this approach where focus is not on the outcomes but the process, we have to work with employers, we have to be able to articulate our competencies to employers that AI is not a threat to credibility, it’s the part of our formula in higher education and how we how we train people. For example, imagine every course in our institutions have an opportunity for hands-on authentic learning, where at the end there is a skill that’s visible to future employers. There are 8.2 million learners in higher education who wants an internship, but only 3.6 million of those get one. There’s a huge gap already, so hands-on internship opportunities during our course of higher education should not be for those who want it, or it should not be postponed to last year of education. It should be an integrated part of the equation. These are, to me, pedagogically possible, but the challenge is to be able to figure out the logistics and systems to do this at scale.

Marina: So we are talking a little bit about the word academic integrity. You know that really resonated from your description, John, and I’m from the California Virtual Campus, which represents the California Community Colleges. We’re 116 colleges serving about 2.2 million students, and this is an issue that we’ve been grappling with around academic integrity, especially with agentic AI. And just to kind of put it in perspective of what do we mean by agentic AI? Because I think it could have a couple of different definitions. When I’m talking about agentic AI, I’m talking about these autonomous agents that can go and log on into an LMS, a learning management system on behalf of a student, scan for assessments or assignments or discussion posts to complete them and submit them on behalf of the student within seconds. And we already have analytics in our learning management system that indicate that a student can go in, and has gone in, because we have the data and completed a 50-item quiz, short answer quiz, not multiple choice, and submitted it within about four seconds, so we know that the student is not searching for these as prompts on like ChatGPT, because that would actually involve some interactivity with the student. They’re actually having an agent go in and submit it on behalf of themselves. That’s a very big concern that we take very, very seriously. Obviously, the consequence is that our degrees and our programs and our transcripts will lose its value and that they will become meaningless. And so I think it’s similar in the case of being like other crises, like COVID 19, for example, but it’s also uniquely alarming in kind of the destruction that it can have on the value of our grades and our courses and our programs. And the question of, “How do we address the concern?” I think we do handle it in the way that we handle these other risks. We have to be deeply engaging with our faculty. We have to pull together our technology partners. And I think a mistake or a pitfall of this can be if technology says, “Well, the solution lies only in faculty changing the way they assess, adopting project-based learning or authentic assessments.” And if faculty turn around and say, “Well, the problem can only be solved technically if we create some kind of barrier or blocker for these agentic browsers.” And I think those are both equally flawed in terms of its approach, because we’re expecting the answer or the solution to be around the silver bullet, when in actuality, I think the solution lies in a deep partnership where we sit down with each other, with our partners, we look at both programmatic and technical solutions to making sure that the academic integrity of our institutions remains intact.

Rebecca: AI enables more efficient adaptive learning and provides increasingly personalized pathways in support of student success. How do we strike a balance between personalized learning and the formation of human connections essential to creating a sense of belonging? You want to start, Van?

Van: Sure. So this year marks my 30th anniversary of being in higher education, and I started my career as a faculty member, I spent a decade in the classroom as a history professor. And one of the things that I learned very quickly during that time is whether I wanted to or not, you end up teaching to the middle. You run the risk of losing the students that are ahead. You run the risk of losing the students that, for whatever reason, are behind. As much as I wanted to personalize learning, I couldn’t do that, at least not at scale. I think that AI does give us the opportunity eventually to be able to personalize at scale. That does not mean that the human piece loses out. In fact, that human piece, I think, becomes even more critical. We know that relationships matter. We know that, beyond a shadow of a doubt, a student can succeed when they have a relationship with somebody on that campus, ideally, their professor. I’m a big fan of Parker Palmer’s work, and specifically, I’m a really big fan of To Know As We Are Known. And if you’re familiar with it, Palmer talks about relational epistemology, which is really just a fancy way of saying that, at the end of the day, what matters is a faculty member’s ability to create a relation with a student, and for students to create relations with each other, and that is the moment at which learning takes place, not the transmission of knowledge, but real learning takes place in the midst of those relationships, and a skilled faculty member turns the classroom into an opportunity to develop relationships and for people to co-create knowledge together. I don’t think AI changes our ability to do that. I don’t think it takes away our ability to do that. I think what AI may be able to do is to take some of the burden off of faculty when it comes to trying to personalize learning, so that faculty can focus on what a human does best and what faculty do best. And that’s the creation of relationships, and that’s where I think the true learning takes place. And I have to say, when I was a faculty member, that was the piece that excited me the most, and probably the piece that, unfortunately, I got to do the least.

Yakut: If I can jump in here, plus one to everything Van said, I mean, so insightful, and I’m not going to repeat because that’s exactly where I was going. But one thing that, when I was listening to you Van, I was reminded of the statistics. Khan Academy’s, Khanmigo, last year, they published their self-efficacy study results within the school districts that they worked with. If you’re familiar, Khanmigo is basically personalized learning using AI. Within the school districts that they worked with, where the tool was available to all teachers and all students at no cost, only 9% of students, 0, it’s not 19%, it’s not 90%. 9% of students used the tool to the level that Kahn Academy deemed standard use, which is two hours a week. So where I’m going with this is provision of the technology, provision of the platform or the tool, does not necessarily lead to the opportunity to learn. So we think these technologies are going to make these available for everybody, which they will, but then there is still a missing piece that makes the student use the technology to learn, and that’s, I think, the inspiration to learn, and that’s the human thing. So as Van mentioned, it takes a faculty member or a parent or an uncle somewhere to inspire people to do something. So if we relieve people of the busy work that takes place to prepare for teaching, or actual teaching in the classroom, we may give them time to actually do this human element of inspiring to learn. And I think the two are hand in hand.

Rebecca: So Marina was kind of hinting at some of the data that’s available in our LMSs. And one thing that online learning leaders increasingly rely on is this use of data analytics, based on institutional and LMS data, which provides more information on student learning practice. AI tools allow for more efficient analysis of this data. So how can this data be leveraged to improve the online learning ecosystem? This could be faculty workload, course offerings, student engagement, and anything else that you can imagine.

Yakut: There are a few things I can mention. First, predictive analytics for early intervention, identifying student interest much earlier, not just those who are failing, but those disengaging. And there was an example about this earlier, struggling with specific concepts and provide targeted support before they fall behind. We have been pursuing these kinds of systems for a long time. I have 32 years in this profession now, and probably half of it was all about predictive analytics. The challenge has been the siloed nature of our data systems at our institutions that create only a partial picture of student success, unless you invest in data lakes, integrated data solutions and whatnot. And then there’s also the governance issue around data. AI can create a leapfrogging effect here. An ability to do this predictive work can also reduce faculty workload. This way, the human is in the loop and is the one doing the empathetic work and empathetic outreach, asking, rather than finding out who needs this kind of outreach, doing the work of outreach, saying, “Are you okay?” Apparently it’s one of the most powerful things you can reach out to students about, are you okay? The heavy lifting is done elsewhere, and then we become more human. Another example could be curriculum optimization. By analyzing patterns across courses, we can identify where students consistently struggle, what courses are synergetic to take together, for example, what courses are toxic to take together and then provide information to both faculty and students around those. Where are the gaps that exist in our program pathways? So if there are skills that are needed, and then that analysis of curriculum and skills matching can be done by AI, and then this can impact our course redesign and sequencing decisions. And also our institutions can allocate resources a little bit more effectively. Analytics can help us understand the actual demand patterns, optimal cohort sizes. We usually say, “Oh, 30 students,” but then we don’t really base it on I mean, it’s a habit that’s telling us about the cohort size to be. It may be classroom size. We can actually do deeper analyses, especially when we’re facing enrollment pressures and financial pressures. But I have to also emphasize two things here. One, this data should inform our decisions, so it’s data-informed decisions, not even data driven, because sometimes we may have to make decisions that go against data, it can be political, it can be governance reasons. We need governance structures that will take this data and generate insights from it, and then transparent communications with students about how their data is used. And this is crucially important. We need to democratize this data and allow students to have access to it, along with institutional access or faculty access. So transparency, ethical use, shared governance around analytics are very important as we become more excited about AI’s potentials in this analytics stage.

Marina: I love all of those things around predictive analytics, early intervention, and then optimization as well. So I’ll just try to add to that conversation a little bit. I’m really interested in how we’re serving our students in online modalities, and who it is that’s succeeding in those courses, because I think we know that traditional education has served a certain group and demographic and gender over time, and only in recent years are we sort of aware of some of the biases in the design of our curriculum and our courses that serve certain people, and how can we analyze our data more efficiently to address what exactly is it that we’re doing that’s helping some students fail and helping some students succeed? And I’m saying that intentionally, because I think we are agents of that success and failure in many ways, as our institutions are structured, and so I’m very interested in sort of decolonizing online education through the use of data analytics and institutional research is notoriously slow. You have to wait a year to get your IR office to work on some data set. It’s a lot of intensive, hard work, and then a year later, you get this sort of lagging indicator of, like, well, like two years ago, this was the thing, and it’s not even relevant now, or things have changed. So I’m excited that AI and data visualization and optimization can allow us to have this in a more timely manner, like just-in-time data. It can help faculty to realize how their own strategies are impacting certain students, well or not well, and then make those changes immediately. If I have an AI coach in my class and it’s telling me that they have identified, I have six African American students, but one of my strategies is unintentionally impacting them in a negative manner, I want to know that now. I want to know what it is that I’m doing in a private message from my AI assistant, [LAUGHTER] and I want to rectify that and immediately change. So that’s the kind of agility that we currently lack in education and in research that I’m very, very excited about: bringing in AI to help with that sort of private, supportive coaching done in the privacy of my own course from a desire to want to improve and serve all of my students.

John: Now, you’ve talked a little bit about some of the benefits, or some of the cost first, and now some of the potential benefits that we have from using AI. How do you see online learning changing in response to AI? And we’ll start with Marina.

Marina: Yeah, so I just want to say it’s really hard to follow up with all the positive, I think benefits that AI can bring that Van and Yakut have brought up, and I 100% agree with those. So I don’t always want to be the downer [LAUGHTER] in this podcast, but if I had a crystal ball and kind of playing on the previous question that you asked, John, around academic integrity, assuming that that is a problem that we’re not going to be able to immediately solve, either programmatically or technically, one of the concerns that I have is that it will affect a specific kind of online learning, which would be fully asynchronous online courses. The ones where they don’t have any set live meetings, I think people will be most critical of those kind of modalities. And so if I have a crystal ball to look at, I suspect that we’ll be bringing back more hybrid forms of online education to replace more asynchronous forms, because of the concern around academic integrity. That’s my crystal ball. What does that mean? Well, it could mean that hybrid, maybe half the courses meetings will be in person, half online, or it could be that there’ll be more proctored assessment, either online proctoring solutions with some of the main vendors that you may have heard of, or in person. So we would need some kind of proctoring network, or some way to address that concern. So I think as a stop gap measure until we kind of come to terms with the academic integrity piece, I see some of that changing. That being said, I do want to sort of add to the good elements that you know Van and Yakut are bringing up, because I think AI can help humans be more human in some ways. I have not been in the classroom for a number of years, but I did teach online for a long time, and I would love to go into my learning management system, and my AI assistant pops up and says, “Hey, Johnny has not interacted with you or any other students for eight days. You might want to check on him to make sure he’s engaged.” Or my assistant tells me, “Hey, you interacted with these 10 students in the discussions this week. You might want to answer posts from these 10 on the public posts” and things like that that help me to be more human, that help me to be more equitable in terms of how I interface and touch my students online, I think that could be a really amazing use of AI, and I don’t think we’ve really realized the true potential of that.

Van: And if I could jump in, I agree with everything that Marina said. My fear, though, is exactly what her crystal ball says, and that is that asynchronous, online education may become more difficult, or will face additional scrutiny, because we already know that we’re highly scrutinized, but we also know that online, asynchronous education plays a critical role in access for a certain student population, and I would hate to see those students, particularly adult learners, or learners who are working or have caregiving responsibilities, lose those opportunities. I think that the main thing that AI is going to do for online education is, I do think it’s going to force us to rethink our pedagogical practices. And I think that we are probably going to need to think about, how do we do alternative forms of assessment, and how do we move from assessing knowledge to assessing abilities and skills. I said that I started my career as a historian. When I took my PhD history exams back in the 1990s so back in the last century. When I took those exams, I sat in my office with my laptop and had no ability to consult any sort of document. It was purely from memory. It was eight hours of answering questions from memory. The reality is, though, that whenever I got into a classroom to teach, I never created a lecture, I never created an assignment, I never created a test without pulling a textbook down or pulling some sort of reference manual down and looking at it. What I did in terms of my teaching was completely disconnected with the way that my graduate program, which came from an R1 institution, assessed me. And so I do think that one of the things I hope AI is going to push us to do is to really rethink our assessment practices so that they are more in line with the reality of our students after they leave our classrooms. And that’s not unique to online education. I think that that type of shift in pedagogical practices really needs to happen across the board if we want our students to truly be prepared for the world that they’re going into after they leave us.

Yakut: Can I quickly add two things that we’re seeing actually happening in terms of change in online, and that’s in the design process. Those of you who are designing online courses will know that one of the first friction points is when we ask faculty to write learning objectives, because they’re not used to thinking about the end in mind, right? They’re used to thinking about, “This is the content I’m going to teach.” So that friction is reduced by use of AI, because you’re easily creating a set of objectives, reiterating from that. And then the other aspect is video production process. You can now ask a faculty member to say something for three minutes, and then you can make that faculty member say anything in any language. Now it may look fake, but it’s going to get better, right? So the video production process and instructional design process are immediately impacted by the use of AI, and it’s going to impact those professions, so we need to do something about it, but I think it’s making our institutions a little bit more agile in terms of updating courses, but also creating new courses based on the input from the market. I also see in larger, better adoption of open source content, because open OERs are problematic because faculty do not have the time to go and research, curate, adopt OERs. AI is proving useful in that process too, going and researching and bringing you things to review and curate. So I think there are immediate gains there as well.

John: Moving to a somewhat different topic, federal regulations and political shifts, of which we’ve seen an awful lot in the last year or so, can create uncertainty. And we’ve seen a flurry of executive orders designed to dismantle long established higher ed priorities concerning equitable and inclusive education, while also restricting financial support. What advice do you have for leaders trying to stay agile and innovative in this evolving and somewhat seemingly random political environment while maintaining compliance with federal regulations in areas such as accessibility and SARA and any other area that may pop up?

Van: Well, if you don’t mind, I’ll start. Given what we do at WCET, if you’re familiar with WCET, we really function at the intersection of policy and practice of digital learning, and we spend a lot of time monitoring what’s going on, particularly in Washington D.C., at the federal level, at the Department of Education, and increasingly in the executive Oval Office, and translating that to the field, in terms of what that means. It’s not lost on me that before I joined the webinar this afternoon, I was watching several of my colleagues respond in real time to the negotiated rule making that’s taking place in Washington DC right now around things like accountability measures, new accountability measures around student loan limits. So it really is a dynamic environment that we’re in. I think that there are ways that AI can help us navigate this, actually. So, one of the things that I played around with, I spoke to a group of SARA administrators, that’s the State Authorization Reciprocity Agreement. I spoke to a group of SARA administrators this summer at their annual meeting, specifically about AI. And one of the things that I played around with was taking the SARA policy manual, pointing Perplexity to that manual, and then asking it specifically to create a synthesis of the regulations in that manual, and then asking it to go out and pull out the contact information for each state SPE that’s the State portal entity, that’s the primary SARA person in each state, and to provide me with a table of who those individuals were and what their contact information was. This is something that it would take somebody hours, if not days, to do, I got this back in two minutes. So I do think that there are ways that AI can be used to help us navigate the policy landscape more quickly than what we have been able to do in the past, and to democratize that so that everyone has the ability now to understand these regulations, rather than just a few people at each campus. We are going to have to remain vigilant. We are going to have to make sure that we follow what is happening, particularly in Washington D.C. I will tell you that this Department of Education is very bullish on AI. They are particularly interested in finding ways that it can be used in the educational process. Having said that, they have done very little, though, in providing regulatory guidance. So, my advice is to monitor, to lean on organizations like WCET that are monitoring this for you, to not be afraid to experiment with it. And then I would say this, regardless of the technology: start small and fail, and learn from your failures, and don’t be afraid to fail. And I think that that last piece is really critical, it’s critical with any technology, but I think it’s especially critical with AI, because the only way we’re going to be able to figure out how to best leverage this is if we experiment with it, and if we experiment with it, we have to accept the fact that we’re going to fail sometimes, and we can learn from those failures. That’s scary in higher education, we’re taught not to fail in higher education, and I think that we’re going to have to shift our mindset so that we really are embracing a growth mindset, to borrow Carol Dweck’s framework, if we’re going to be able to remain agile, but also be very innovative, and leverage AI to the ability that we are able to.

Yakut: So Van has been doing this for such a long time, I don’t know if I can add anything except for a plus one to everything that he said. Maybe one thing that I can add is agility does not mean reacting to every political signal. Staying grounded in institutional values and your personal values, and then building flexible implementation strategies. So our commitment to accessible, inclusive, high quality education, and student success doesn’t change how we articulate and implement that work may shift because of what’s happening around us, and the importance of being connected to networks like WCET, UPCEA and learning from these state compacts and peer networks is extremely invaluable. There may be institutions out there without enough resources to allow their people to be connected to those networks. So maybe this is the time to urge these professional networks to be a little bit more lenient and allow, for example, I’m just making this up, I just thought of this while I was listening to Van, what would it be like if professional organizations allowed 5% of an institution’s size to be peers like gift memberships? So the institution X, a regional university, may not have the resources to become a member, but it is so important for all of us to be successful at this point in time. Maybe, you know, my institution can gift a 5% of our size to that institution, so like sister institution kind of thing. And very important to document your decision-making processes at your institution, how you’re interpreting regulations, documented, so that you have both accessibility to that information but also accountability. It protects your institution. It demonstrates good faith effort and legal counsel and government relations are your friends in this uncertain political environment, and proactive communication with them is very important.

Rebecca: In states with multiple public colleges such as the SUNY system, each campus is, to some extent, competing with other campuses in offering online degrees. What mix of cooperation and competition is optimal in such environments?

Marina: I can talk a little bit about the California Virtual Campus because the California Community Colleges are very similar to SUNY in terms of just being a big ship to steer. A lot of you know, local decisions, some centralization, and people are always, I think, generally speaking, a little bit threatened by the centralized stuff. When the California Virtual Campus first rolled out, gosh, 10 plus years ago, and we talked about course sharing, there was immediately sort of concerns around, “Are we going to be cannibalizing our enrollment?” We love the word cannibalizing, I don’t know why in education [LAUGHTER], but immediately it’s like, “oh my god, we’re going to lose enrollment. This is going to be terrible for us. The Big Brother is going to take over our curriculum and tell us what to do.” There’s all of these anxieties that come out from administrators, from faculty, from different leaders, and so I think to put that in a different perspective, has been really hard, because you’re fighting against that cultural anxiety that we have around being an independent institution. So when do you hear and when are you independent? What I’ve tried to do with the CBC and our system is to have some very honest conversations around “what exactly is your concern? Is that concern an institution centered concern, or an enrollment concern, or a funding concern, and how does that then relate to your learners?” Really shifting the conversation from, “we’re worried about how we do things to what’s the best-case scenario for our students? Do they have the options that they need? Do they have the access that we need? Are we really leveraging our collective power in a way that benefits the learners in the most compelling and powerful way?” That’s sort of the question that I’ve had to ask, and it took about 10 years [LAUGHTER] for California Community Colleges to get on board with course sharing, and now it’s a big success. We share professional development, we’re sharing financial aid, we’re sharing transcripts, things that people have held on to so closely, of like, “No, this is our school. This is our university.” And I think this idea is very outdated now, thankfully. And the future really is less institutions, especially smaller institutions, rural, under-resourced institutions trying to be everything for their students, trying to give them every class that they need for every program, and meeting every need. That’s exhausting, and what we’re finding is courses are being discontinued, faculty are, in some cases, unfortunately, losing their work, especially part-time faculty and adjunct faculty, programs are being discontinued, and in some cases, entire institutions are being shut down and absorbed by other institutions, or just not being absorbed at all. And so to avoid this, I think, unfortunate, outdated approach, we have to pull together, we have to pool our resources, and we have to put aside our institutional egos and really realize that there’s a lot of strength in our collective power and when we come together. I can give some examples later but one thing that we’re doing in the California Community Colleges, which is really exciting, is several of our rural colleges, which, again are tiny, they don’t have a lot of staff, they don’t have a lot of programs or money. They’re pulling together to create fully online associate degrees. So by themselves, they would never be able to meet all the requirements to offer those degrees online, but collectively they are, so it’s benefiting their programs, they’re able to grow their enrollment, and it’s benefiting a large swath of their students as well. So that’s the kind of thing that I think doesn’t have to be competitive, but they can actually be mutually beneficial for students and institutions.

Van: So you know, the “C” in WCET stands for cooperative. We’re the WICHE Cooperative for Educational Technology. So we take this idea of collaboration and cooperation really seriously. And I think just plus one to everything that Marina said, it’s always hard to follow Marina whenever I’m on a panel. I think that course sharing was a great example. It’s also oftentimes a really scary example for faculty, especially. What I wish that I would hear more about is the way in which institutions begin to share administrative burden. And I think that maybe, if there’s a thin end of the wedge that can maybe help an institution eventually get to that course sharing, It’s to have that conversation about, “Are there administrative functions that can be done better collectively than individually? And what are those?” And maybe the easiest thing to do to start with is an arrangement where you are jointly purchasing services. We know that folks are able to get better deals whenever they go in with multiple institutions than whenever they go in with individual institutions. At an earlier point in my career, I was in charge of online education in the state of Texas, and the legislature asked me to determine what each institution in the state of Texas was paying for its learning management system, and I learned very quickly that A) I didn’t have access to that information because there were non-disclosure agreements, and B) there were non-disclosure agreements because what institutions were paying for the same learning management system were vastly different. And so I do think that one of the things that institutions can immediately do, that isn’t scary, is to create those purchasing co-ops and to look at what administrative functions can you share. And then, by all means, move as fast as you can towards course sharing, because I think that Marina is absolutely right when we think about what is in the best interest of our learners, course sharing is oftentimes in the best interest of our learners. It gives them access to a larger number of classes, it gives them access to a larger number of opportunities to progress in their academic career and move towards that credential that they’re seeking, and it allows institutions to specialize. As a recovering historian, as much as I would love to see every institution offer a history major, the reality is every institution probably doesn’t need to offer an online history major. So think about what is it that your institution can do that is unique to you, that you can double down on and do well, and then begin to share that with other people and with other institutions. And I think that will help us greatly in terms of being able to really move towards what our learners need, rather than just what’s going to be in benefit of us. And the last thing I would say is not only think about, are there programs you can specialize in, but think about, are there student populations that you can specialize in? You know, maybe you have an institution that is particularly good working with English language learners. Not every institution is going to have that level of expertise. And so maybe think as well about, in addition to the programs that you may have some specialization in, are there learner populations that you know that you are doing a good job of meeting their needs, and you can help other institutions do that as well.

John: Institutions vary quite a bit in terms of their size, their funding, how well established online programs are. What strategies can leaders at small or resource-constrained colleges use to develop sustainable online learning practices despite often limited staffing and infrastructure, or in environments where faculty are experiencing burnout or there’s some form of organizational resistance to change?

Yakut: Well let me jump in, and I think there are quite a few parallels between this question and the previous question that was asked. 15 years ago, I was at a regional university in Texas, and in fact, I know Van from those days. In a matter of three years, this was a new institution, we rolled out 13 online programs. It was amazing. There were not a lot of resources, brand new university, faculty resources are scarce. I was a team of one. So fast forward to today. This would not be possible. One, the market is so saturated with degrees, you don’t have the competitive advantage anymore. And two, the resources are even reduced compared to 15 years ago. So in places where resource-constrained or smaller institutions, sustainability of effort comes from focus. So, not trying to do everything like, “Oh, let’s just roll out an online program for every single program that we offer,” but intently looking at the market need and connection to jobs, starting with that community where the institution exists, but a little broader too. So those small number of programs that clearly advance this mission and student access, then invest deeply in quality faculty support, instructional design, quality is by itself, a whole entire conversation, I think. And then faculty burnout. It’s real. And many times when faculty resist online, we take it personally. We don’t like it. But then it’s just exhaustion rather than real opposition. Transparent communication, realistic timelines, recognizing that this is instructional labor, this is on top of everything else that we’re asking from you, and then supporting that instructional labor in a respectful way goes a long way in helping faculty burnout. Sometimes, the most strategic move is to slow down and build that trust and capacity within the institution, because let’s face it, in institutions like that, you’re going to rely heavily on faculty time and faculty effort to design and develop those courses, compared to larger institutions where there are large teams of instructional designers or video producers that are going to do the heavy lifting for your faculty. Again, tying to the previous conversation, shared tech services, vendor collaborations, a lot of these institutions can punch above their weight and without overextending people, so that those kinds of strategies should be part of it. And finally, I want to tie this conversation to the AI conversation. Many universities have responded by signing big enterprise deals with AI companies. And they paid a lot of money for this, and they wanted to achieve equitable access for their own communities, they wanted all their students to have access, all their faculty to have access. But this results in us treating AI as a competitive advantage, where we should start seeing it as a collective challenge. So we should be forming system-wide and cross-institutional partnerships when it comes to AI deals as well. These partnerships can be between universities, between universities and community colleges, sister campuses like sister cities, so that we raise all boats and ensure equitable access for everybody. There is room for all of us to be successful, and our actions should complement that belief. And we need to bring faculty into these decisions, because the moment you announce a deal, AI deal, that will allow everybody to have access to AI, it may be interpreted as a blanket permission for unrestricted AI use in every course, which is not the case at many of our institutions. So faculty’s presence at these tables is very important.

Marina: Yeah, I just want to say amen to Yakut’s point about faculty burnout and kind of respecting the time and the expertise that they bring to the table. Our faculty also have, at many of our institutions, expressed burnout, and so anything we do is really thought around that concern. In terms of AI, if we want to foster leadership amongst the ranks of our faculty, we actually created an AI fellowship, and we selected a couple dozen AI fellows from the system to lead some really important work. And they’re being compensated with reassigned time or stipends in some cases, to do that work, in addition to some staff members, classified staff members as well, because they often, you know, occupy the gray space that we forget about, that support faculty and instruction too. So that’s very important. I think about a parallel to online learning, and actually it’s an important parallel with open educational resources. In my previous role as a dean at one of the community colleges in our state, we wanted to grow open OER, but it was the same thing. Faculty were like, “In order to change my textbook to an OER, it’s gonna take so much work and effort, and I have to change all my tests and all my prompts and all of this.” So we started giving very small stipends. I think at the time, it was like $500 for faculty to adopt OER. And I did an analysis of this over time… I wish I had AI to do that, but I had to do this [LAUGHTER] myself… but essentially, we saved students something like five million dollars within the first couple of years that it was being adopted, and we spent, I think like 200k during that same period of time. So doing the data on the return on investment for supporting faculty, it’s going to be tremendous to kind of ensure that they’re not burnt out, that they have time, and they have the space mentally to do the work that we need them to do. So that would be one key thing. And I think OER, what we’re finding, and what we found in our institution is as well, that there are higher outcomes in terms of success rates and grades in online courses when an OER is used in that course than in the same course with a physical textbook that students had to purchase. So those are kind of connected. And then just to kind of go back to course sharing, I can always count on Van to be my greatest ally with course sharing, but we have colleges that were very reluctant to get involved in course sharing, as I mentioned. When I joined the CVC four years ago, we had something like 2,000 enrollments on course sharing and 2,000 enrollments for a system of our size is not that great. We’d joke that most of it’s our staff trying to see if it works. This year we’re going to have more than 50,000 enrollments. So within four years we went from 2,000 to 50,000. I think we’re going to double and triple that in years to come, because we finally gain momentum and trust and involvement and engagement. Of those 50,000, you look at the numbers, some rural colleges are making out like bandits. They’ve gained 1000s of enrollments through course sharing, by putting their courses on our course sharing platform, and benefiting from it tremendously, and gaining enrollments that they would never be able to get from their local zip code because of their rural environment and so forth. So, it’s a win-win proposition, it doesn’t add any extra work. We let them fill their classes locally with their local registrants, meaning they’re returning students, their athletes, their disabled students, and so forth. And then any courses that are not filled, we pull them into our platform, and they’re open for anybody in the state to take. It’s a very simple formula that, again, has really netted 1000s of enrollments for these small rural colleges, brought in funding, brought in additional resources for their burnt out faculty and their staff and their infrastructure. So I think my suggestion for strategy is find those formulas that are win-win propositions. It doesn’t need to be something where one person loses, another person wins in order to get the involvement, there are ways for everybody to win, and that’s, I think, an exciting opportunity for us.

Rebecca: We always end by asking: what’s next? Start with Van?

Van: Sure. So ironically, I just finished writing a blog post for WC Frontiers on this, this morning. I don’t have a crystal ball. I do have a magic eight ball, though, if you remember those. My predecessor gifted me one to help me navigate the challenging federal policy environment. So I have my trusty magic eight ball. I think there’s six things to look for in 2026. I think the first thing is we need to remember that the connection to workforce education is everyone’s responsibility. It’s not just a few faculty members’ responsibility. This is particularly important with the move to Workforce Pell, but it’s important as we see a workforce increasingly asking for graduates that are AI literate. And I think that AI literacies is everyone’s responsibility. Two is AI literacies. I think that we need to desperately change the narrative from just focusing on academic integrity to beginning to focus on AI literacies and you’ll notice that I use the plural there, because I don’t think it’s black and white. I don’t think it’s you know or not know. I think that it’s a spectrum of issues that faculty and students and staff need to have mastery of. Three, and this really ties to what Marina was just talking about, we need to get creative economically. Like I said, I’m a historian by training. That means that I tend to run towards the pessimistic side, because I know just how bad things can get sometimes, and I am afraid that we’re looking at a very challenging economic environment on the federal level, and that’s going to trickle down to the state level, especially for our public institutions. So I do think that we’re going to have to get creative economically. We’re going to have to do things like think about course sharing, like thinking about purchasing consortiums, changing our business model in higher education and in digital learning. I think that that means that one way that we can do that is to lean into those post-traditional learners. There are 43.1 million Americans who started college and did not get a credential. That is a travesty. We should not have 43.1 million people that stopped out. That tells me that, first of all, we’re not doing right by those folks, but it also means that there are 43.1 million people that we can get back into higher education if we try, and we won’t get all of them back. But we need to think about how do we offer programs that attract those adult learners and keep them. What did we do wrong the first time around to make them stop out? It’s not all an institution’s fault. Adult learners have a number of challenges that they’re juggling, but are there ways that we can attract them back into our classes, and that we can make sure that we provide them with the supports that they need in order to stay and be successful? I don’t think we can lose sight on accessibility in 2026. It is not just the right thing to do ethically, it’s going to be the right thing to do legally, with the Title II regulations that go into effect at the end of April. And the upshot of those five things is that digital learning is going to be more important than it has ever been before. And that means that your job as digital learning leaders, your role on your campus, your role in the SUNY system, is going to be more critical than it has ever been before. And so I think that that’s what we have to look forward to and the upshot of it is that, at the end of the day, what you do as a digital learning leader matters and is going to be more important than it has ever been before, more important than it even probably was during the COVID-19 pandemic. So that’s what the magic eight ball over in my WCET office says.

Yakut: Now, how do I follow this? I mean, you covered everything, Van. I was actually going to start with the same sentiment, online education sits at the center of all the challenges higher education is facing, and the work ahead of us is to leverage its pedagogical power, its ability to expand access to affordability, and its ability to amplify our institutions’ impact. You covered a lot of things. There is one thing that I would like to just append to what you said. I want to point out that we in higher education often forget that we are not just educators. We’re also employers. In many cases, we are one of the largest employers in our communities. And many campuses, many of our institutions still lack a coherent AI upskilling plan for our own workforce. We have tons of projects facing transfer efficiency across our institutions, productivity, but then we don’t really have, in many cases, a plan to help our own people adapt. A Brookings Report from, now, a year and a half ago, estimated 19 million US jobs that are significantly affected by AI. And many of those roles exist right on our campuses. Many of our employees hold jobs at risk of automation, such as legal analysis, paralegal, administrative assistance, accounting, communications, we just talked about instructional design and production. These job categories are, drumroll, they are populated by women. You know, we identify these areas of risk, but we don’t really come up, at least in higher education, strategies to address those. We must understand that there is a differential impact of AI on our workforce. These jobs that I listed are stable middle class professions. They are pathways for upward mobility for a lot of families. So we have to ask this year and beyond, what happens to our societies when women or any subgroup begin to disappear from the workforce as a result of AI, and what are we doing to stop that from happening? Thank you.

Marina: It’s unfair to go after Yakut and Van, but I just want to really say I appreciate that Yakut points to our own employees and our own communities, and starting at home, I think that’s something that’s an important reminder for me myself. If I could just leave with three additional things to summarize some of our conversations from today, I would say, you know, number one is a reckoning when it comes to AI in higher ed. There is a reckoning for specifically, as I mentioned, asynchronous fully online courses, and how we’re going to manage that, and the long game of changing the way we teach and the way we assess our students and the use of outcomes in our courses. So I think this is going to be the next big thing for us to tackle. The second and this is something Van has spoken to about extensively, is a move toward thoughtfully shared resources. In our system, we pay for the common LMS. We pay for and negotiate common ed tech vendor contract tracks as well, course sharing, as I mentioned. So there’s going to be more and more a move toward this, especially in light of federal funding issues that colleges are experiencing. And the final piece of it that’s actually really near and dear to me, is a revisiting of old norms and ways of doing things. The way we handle our transcripts, for example, is… I’ve spoken about this in other venues… highly inequitable. The way we handle our financial aid, that can be very hit or miss depending on what institution a student goes to. The way students have access to courses, our levels of accessibility, the way we gather analytics and research, which I think AI will dramatically transform. Institutional research is going to greatly change in the next couple of years. I think these are going to be really exciting “what’s next” as well for higher ed.

Rebecca: Well, thank you so much for joining us. We appreciated the conversation and the many things to be thinking about as we trudge down 2026.

John: Yes, thank you.

Yakut: Thank you for having us.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on Apple Podcasts or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing Assistance provided by Madison Lee.

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367. Teaching Effectively with ChatGPT

Posted in edtech, educational technology, equity, feedback, higher education, inclusion, learning, pedagogy, personalized learning, professional development, teaching
November 13, 2024

AI tools can help faculty improve the quality of their teaching and assist students. In this episode, Dan Levy and Angela Perez Albertos join us to discuss a variety of ways in which ChatGPT can be used to support learning.

Dan is an economist and a senior lecturer in Public Policy at Harvard University where he teaches courses in quantitative methods, policy analysis, and program evaluation. Angela is a graduate of the MPA program in International Development at the Harvard Kennedy School, where she currently serves as a Teaching Fellow. Dan and Angela are the authors of Teaching Effectively with ChatGPT.

Show Notes

  • ChatGPT
  • Levy, Dan (2021) Teaching Effectively with Zoom: A Practical Guide to Engage Your Students and Help Them Learn. 2nd ed. LSC Communications
  • Levy, D. and Angela Pérez Albertos (2024). Teaching Effectively with ChatGPT: A practical guide to creating better learning experiences for your students in less time. LSC Communications
  • TeachFX
  • Teaching Effectively with ChatGPT Companion site

Transcript

John: AI tools can help faculty improve the quality of their teaching and assist students. In this episode, we explore a variety of ways in which ChatGPT can be used to support learning.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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Rebecca: Our guests today are Dan Levy and Angela Perez Albertos. Dan is an economist and a senior lecturer in Public Policy at Harvard University where he teaches courses in quantitative methods, policy analysis, and program evaluation. Angela is a graduate of the MPA program in International Development at the Harvard Kennedy School, where she currently serves as a Teaching Fellow. Dan and Angela are the authors of Teaching Effectively with ChatGPT. Welcome Angela and welcome back Dan.

Dan: Thank you.

Angela: Thank you so much for having us

John: Today’s teas are:… Dan, are you drinking tea?

Dan: Moroccan tea.

Rebecca: That sounds nice.

John: And Angela?

Angela: I am drinking Chai Latin, my favorite.

John: And Rebecca?

Rebecca: I have English afternoon tea. It’s been a while since I’ve had that.

John: And I have English breakfast tea.

Rebecca: That’s unusual for you.

John: Yes, it is.

Rebecca: [LAUGHTER] Alright, yeah. We’ve invited you here today to talk about Teaching Effectively with ChatGPT. Can you talk a little bit about how this project got started?

Angela: Well, Dan and I had been working for a while already, when we started talking about this book, and we had been experimenting on different ways to integrate technology for more effective teaching and more effective learning. And I had personally always loved working with Dan, and he had written a book already about Teaching Effectively with Zoom that was very valuable for many educators during the pandemic, and he was thinking about doing something similar for ChatGPT. We saw that many educators were paralyzed by this technology. We saw that many students were already using it, but may not necessarily know the ways in which they could use it best for their learning, and there was just a big need to experiment better with this technology. We started with a small workshop that we did at the Kennedy School that was a massive success, and then from there, we saw there was a big need and a big desire from educators and students to learn more, and so we just decided to go ahead. And I think since then, it’s been a wonderful experience.

John: One of the things we’ve noticed in workshops is that faculty are often a little bit resistant to experiment with ChatGPT or other generative AI tools until they actually try it. So we thought maybe we’d start with that. What are some ways in which generative AI can be used by faculty to help design new courses or improve the design of existing courses.

Dan: I think generative AI can be used by faculty in a number of ways. And just to reinforce your point, John, I think you don’t have a good sense of how useful it can be to you until you actually start using and experimenting. So I think if we could leave faculty with one message today, it would be: “Start experimenting with the technology so you can understand the benefits and the potential limitations.” We can talk about ways in which we can use the technology to, as you said, design new courses or improve the design of existing courses, and I’m sure we’ll be doing it throughout the podcast. In terms of designing new courses, you can do things like help you refine your teaching goals, for example, or you can have it help you as a brainstorming partner in a class that you’re thinking about designing. You can ask it to provide you feedback with material that you’re developing. So there are a number of ways in terms of improving your course. You can ask it for feedback on slides that you have produced in the past. You can ask it to summarize documents, summarize course evaluations, to gather what are the main things that you need to do, but at a super broad level, maybe the way that I think our book tries to crystallize the use of AI, or ChatGPT in particular, is you should think of it in potentially two roles for you. One, it could be a brainstorming partner, a thought partner for you, and the second one, perhaps the most current use, is it could be an assistant. It could be a teaching assistant, a research assistant, a general assistant. So anytime that you are in front of your computer and you’re trying to get something done and you’re feeling stuck, I think you should just ask yourself, is this an activity where maybe spending three minutes with ChatGPT could help me either get unstuck or arrive to an output faster than I could in the absence of it.

Rebecca: I know as an instructor, I’ve used it to get past a blank page, just to get a jump start on something. In the second chapter, you describe ways in which ChatGPT can be used to customize your courses in the interests and needs of your students. Can you talk a little bit about how ChatGPT can create a more student-centered learning environment?

Angela: One of the biggest potentials that the education world has seen in ChatGPT is the opportunity to approach closer to the ideal of one tutor for every student. Research has known for many years that providing personalized learning can have huge impacts in each student’s learning and. And ChatGPT can enable part of that benefit. It’s not perfect, but it can get us closer to that outcome. Now, how can it do it for a professor? I like to think about it in three ways. One is that it allows educators to process the student information that has high volumes. So for example, in every class that I teach, I start by asking students to fill in a form where I ask them about their interests and what they’d like to learn. If with ChatGPT, I can get a summary of those interests and like the profile of my students. The second way is that it provides access to knowledge or to context that allows educators to tailor activities to their specific students. So for example, when Dan and I were updating one of our activities for the statistics class that Dan teaches, we were trying to think of topics that were relevant for the students in the class. And we asked ChatGPT to provide different ideas for topics. And these went from topics related to health to finance to arts, and many of these topics we were not experts in, but they were topics that our students were interested in learning more about. So ChatGPT allowed to tailor our activities and our context to make them more relevant to what our students wanted to learn. And then the third is that ChatGPT can be made as an avenue or as a tool to personalize student learning directly. So we can ask the students to learn about different topics directly on ChatGPT, so that the approach is the one that works best for them. And we’ve seen this in action as well, where, in one of the examples that the book covers, is how one of the courses that Dan was teaching, he asked his students to learn about signal theory, and then you have two students that take a completely different approach. One of them asked for more in-depth and more expert-level explanations, while the other one says, “I have no context about this, so I actually need to understand this as someone that has zero background knowledge,” and that is super powerful. There’s no way that an educator would be able to provide the two explanations in a class to different types of students. So in those ways, I think that the potential is huge.

John: And you mentioned using a form to gather information about students and feeding it into ChatGPT. One of the things you suggest, though, is that you don’t want to include personally identifying information, such as their name or other things. Could you talk a little bit about why it’s important not to do that?

Angela: Absolutely, so there are huge privacy concerns about what data we put in to ChatGPT, because basically, the algorithms are trained on the data that is existing on the internet, and right now, we have no good sense of what openAI is doing with the data that we input into ChatGPT, and so in that sense, we strongly encourage educators to not put any sensitive data, like student contacts, student grades or anything that would make the data that we are adding about the students identifiable. Now there are solutions that openAI is introducing for universities that are available and that we also talk about them in the book, but our general recommendation is to be very, very careful with the type of data that is used in conversations with ChatGPT.

Rebecca: Can you talk a little bit more about additional ways that faculty could incorporate ChatGPT to improve the active learning in their classrooms?

Dan: So in the book, we describe sort of a simple prompt that you can use, where you give it the slides or the materials that you use in a class a prior year, and you sort of say, “I want to incorporate more active learning into this class. Can you give me a few ideas?” and what ChatGPT would tend to do is provide you with a list of active learning activities. So maybe it would suggest a poll question for your slide number three, or a breakout room kind of activity for slide number seven. The reality is that if you’re just trying to inject a little bit of active learning into a class you’ve already planned, you don’t want to incorporate the 10 ideas that ChatGPT would give you, but it’s enough for you to have one or two good ideas come out of that literally three minute exploration for this to be, I think, worth the effort. I think often we think about, can ChatGPT be better than us?, or can AI be better than us? In the book, we emphasize that’s not the question that you should be asking. The question we suggest you ask is, can I plus ChatGPT be better than I alone? And so in this particular example, you might have a class from the previous year that you’re trying to inject some active learning activities. You ask ChatGPT for a little bit of help, maybe give me five ideas or 10 ideas of active learning that I can incorporate into this particular class. You get one or two that you like, and you might run with them. Or maybe it’s enough of a…not the exact thing that you want to do… but it’s enough of an idea that you can explore. The other interesting thing about AI is that it has infinite patience. So if you don’t like any of the 10 activities that it gave you, you can ask for 10 more, maybe 15 more. And if all you’re looking for is one or two, the spark of an idea might be there. You sort of were describing before, maybe there’s a blank page and I want to get some ideas. The same thing is for injecting active learning into the classroom. One more thing that I want to add is that both in terms of personalized learning and in terms of injecting active learning into our classroom, is that one of the things that has been very interesting is to observe, and I think Angela has been an amazing conduit to be able to see this, is to observe how our students are using ChatGPT, in the absence of us doing anything to use ChatGPT. And so just a couple of quick anecdotes. This happened, actually, after the book. This happened in the last week. So in one of them, I have a student in one of the classes who was hesitant to take the class because the class does require you to run simulations with Microsoft Excel. And he said, “I don’t know Excel. I’m willing to work hard, but I don’t know Excel.” And he, basically in a couple of weeks in the semester, designed simulations with the help of ChatGPT, in a way that he didn’t even think was possible. So to observe how he was able to pursue the curiosity, the intellectual wanting to learn about this and sort of struggle a little bit with ChatGPT: “Can you give me the Excel formula so I can do this?” And so it has been wonderful, and frankly, I couldn’t have spent the hours with him that it took him to do this, to the point that he’s now creating simulations of stuff that we’re doing in the class where we’re not even asking them to do simulations. He came back this week and said, I have created a bot for your course, and I have essentially put the materials from the class, and now I’m asking the bot some questions and getting answers, and that I think it would have been hard to do before. So those are just a couple of the examples of being able to see the students engage in this active learning themselves without us even doing much.

Rebecca: I think that underscores the idea that you suggested earlier, of this infinite patience. I love that idea.

Angela: In the book, we also talk about ChatGPT being empowering, because at the end of the day, it removes the barriers to learning, and so it gives them the encouragement that students need to be able to learn new things by themselves. And of course, critics have sometimes criticized that this only works for motivated students, because you need that motivation to learn to begin with, and that’s absolutely right. But is it not a benefit that at least those that are motivated to learn will be able to do things like this and feel that empowerment, which in turn, is going to feed more into their desire to learn new things?

John: And for those students who come in with a somewhat weaker background, it can help to equalize that background by providing a tutor that never gets tired, for the same reason, that it has infinite patience, and that’s something that human tutors may not have, or their friends, or other people in the class working with them may not have.

Rebecca: Or that lack of judgment.

Dan: There’s an additional benefit, John, to what you just said, which we discovered when interviewing students for the book. It’s not just that it has infinite patience. It’s when we asked the students, what do you see as the advantages of using a bot or just an AI tool to learn, two that came up, one we expected, the other one, in retrospect, we should have expected, but didn’t. The one that we expected is, well, it’s available to me. 24/7, an instructor is not. And the second one, which is very interesting, was summarized by a student who said, I can ask the tools questions that I would be too embarrassed to ask a teacher or a teaching assistant. So it’s not just the infinite patience, which, frankly, a human being won’t have after the third or fourth time that a student says, “I don’t get this.” You might sort of try in seven other ways, but at some point you might have difficulty understanding where they are. Or it’s not even infinite patience, it’s infinite time. You might want to spend time with a student, but you might not be able.

John: And students who may experience some imposter syndrome, perhaps that may help them get past that barrier of asking for help, because they don’t want to reveal what they’re not comfortable with.

Dan: Absolutely.

John: One other thing you talk about is course evaluations, and one thing that most faculty experience is that they may get hundreds of really positive ones and two or three really negative ones, and what they walk away with is a memory of those less positive comments, the negative comments tend to stick a little bit better. You suggest, perhaps using AI tools can help remove some of that. Can you talk a little bit about that?

Rebecca: How did you know they stick more, John?

John: I’ve heard rumors, anyway. [LAUGHTER]

Dan: Yeah, so I think you just pointed out one of the advantages of being able to use an AI tool to summarize course evaluations, which is that sometimes what you remember from reading course evaluations is that comment that was either offensive or hurtful or just like, mean, frankly. And what you can do with an AI tool is to just summarize and sort of say, “Give me what are three to four things that students seem to find effective in this course, and give me three or four things that students find ineffective,” which at the end of the day is probably the most important thing you want to learn. And you might even ask the AI tool to give me some specific suggestions for how to improve the course given these evaluations. Now, you might not agree with the suggestions. You might sort of say, “I know students are unsatisfied with this, but I’m not willing to do what ChatGPT is suggesting.” But again, it at least gives you a little bit of a sense of direction. The other thing I would say is that even the mere act of summarizing the themes is something that I think it’s hard for the human mind to do, like if you have 50 or 60 or 100 evaluations to read one after the other, you start thinking about that comment that was either very good or very bad, and you lose a little bit of track of what were the major themes. And so I think those are some of the benefits. I do want to say that I don’t think ChatGPT is as good as having a teaching professional read the evaluations and come to you and have a conversation with you about them. But you might not have such person at your disposal, and even if you did, that person might not be available when you need to do this, or maybe might not be available every semester. So I think there are some advantages of doing it in this way.

Rebecca: It almost seems like a possible benefit of: “Here’s your evaluations, and also here’s the summary.” [LAUGHTER]

Dan: Yeah, well, I think academic institutions should be thinking about doing things like that, where you automate and by the way, we had a meeting two weeks ago with our faculty, and in half an hour, I created a simple bot that our faculty could use to upload their course evaluations, and it gave them sort of the equivalent to what I was just describing. I even added like a little instruction for the bot: take the most favorable quote so you end reading the evaluations with a nice quote from a student. And this is just like half an hour investment in something that could be useful to many faculty members. You could imagine thinking more deliberately about, “Okay, what are the things that we want the faculty to be working on, and how do we design a simple bot to bring those things.”

John: And I think you also suggested that if a department chair or Dean is also reviewing evaluations, this could make it a much more efficient process to summarize them, rather than them having to go through and read them one by one, especially in larger classes or departments.

Dan: Absolutely, I think in general, the ability of AI to both summarize documents and to allow you to interrogate documents really far exceeds the human capacity to do that. So if you have 100 pages of student feedback or course evaluations, there’s no way that you can do it in the speed at which ChatGPT, or an AI tool similar to it, can do it.

Angela: Dan, there may be, like, a useful tip to give here to our listeners, which is, when summarizing student evaluations, it’s always useful to ask for how many people have made comments about a specific theme, because this helps you weight how much the themes that are being brought up are widespread across the students or just a one-off comment for somebody, and that may be something that ChatGPT may not organically. And then the second one is to also split themes between positive and negative, because there are some times that you have a theme, say, like, problem sets are very long, maybe some student makes a positive comment about it because they think that they allow them plenty of time to practice topics in depth, while some other complaints about the workload, right? And so here we comment how interrogating a document may also be a back and forth process where the better questions that we ask, the better the quality of the output that we are going to get from ChatGPT is going to be.

Dan: Absolutely.

Rebecca: One of the other examples that you provide about getting some feedback about teaching is having faculty members record their class audio and then have it transcribe and then ask ChatGPT to evaluate and provide suggestions about their teaching, and then have ChatGPT analyze those recordings and assist with peer evaluation of teaching. Can you talk a little bit about how that feedback might be helpful and maybe perhaps less biased than human feedback on teaching?

Dan: I want to first preface this by saying that this was done by one educator, as we highlight in the book. There are obviously issues of privacy and permissioning that need to be resolved before scaling this, but I think the potential of having an AI tool be able to analyze a class that you teach and provide you with detailed feedback customized to your own teaching philosophy, you can sort of say, here are the teaching principles that I have and that I bring, and I want you to assess what I did relative to those principles, as the educator that we highlighted in the book did, I think is wonderful. Again, comparing with a counterfactual, what would you do if you didn’t have the AI tool? If you had a teaching coach or someone who specializes in teaching and learning, they might be able to see things that the AI tool won’t be able to see. Because they know you, they might be able to give you advice that’s more sort of adjusted to your own needs. But there’s no question in my mind that most of us don’t have access to such a person on a literally daily basis, and so the AI could help scale that feedback, could help complement… Angela and I in the book, talk a lot about how AI can complement things that we tend to do with other human beings, and I think this is one of them. In terms of being less biased, obviously, there’s a lot written about how AI has some built-in biases that goes, I think, beyond the scope of this podcast, but I do think that an AI tool can, if you give it: “Here’s the criteria by which I want you to judge the class that I just gave,” might be able to help you see things that you’re not seeing.” So is it ready for prime time right now? I mean, our educator was super, super innovative in the way that they did it. My sense is that the tools will come so that it’s much easier to do it. And one of the areas that we’re most enthusiastic about is to think about the use of AI inside the classroom, not just to provide feedback after class to a teacher, but how can AI be used inside the classroom to do lots of things? I think that’s a chapter in the book that is a little bit of reflecting experiences from educators, but mostly speculating about future directions. My hope is over the next year or two, we’ll see lots of super interesting uses of AI in the classroom that could help advance our teaching goals.

John: One of the things that people who run teaching centers sometimes note is that faculty will come to them and talk about what they’re doing in classes, and then the feedback they get from more senior people in the department may not always be consistent with evidence-based teaching practice. [LAUGHTER] Of course, that would not happen at our institution, [LAUGHTER] but I think that would be one way in which, while there are biases in AI because it’s trained on human data, it’s at least trained on a very large base of human data, rather than just the perceptions of the one person who’s sitting in observing the class that semester. So it might actually provide somewhat more neutral feedback, again, as long as the prompts are well designed.

Dan: Yeah. And one thing I want to add is that you can literally tell the AI tool, let’s say there’s a book about teaching and learning that you think captures particularly well the evidence and how you would like to teach. You can sort of give that material to the AI tool and say, I want you to use this as a basis for providing the feedback. As a person who teaches and tries to be as helpful as I can to my colleagues when they ask me to observe them teach, one of the challenges that you face when you observe someone else teach is that instead of thinking about, how can I be helpful to this person where they are, it’s almost inevitable you start thinking how you would have done it differently, and that’s probably not the right approach to help someone get better, because whatever you would have done differently might or might not help the learning of the students in that classroom. So I think trained teaching professionals might not have this issue, but if you’re getting feedback from a colleague, again, I don’t think it has to be a substitute. But if you ask yourself, how many times have I had a colleague observe me teach in the last year? I think most of us would reply, not that many. And AI could help scale that feedback and certainly complement.

John: A related note is that when there’s surveys of faculty in terms of how much they use active learning techniques, generally, most faculty report a relatively high level of use, but when students are asked about that, the reported level of use is a bit lower. And perhaps this, again, might be a nice way of getting at some assessment of that that’s independent of the faculty’s self-perceptions.

Dan: Absolutely. Again, I’m not saying that this is happening now, but one very raw measure of how much active learning is happening in your classroom is what percent of the time in your classroom you are speaking. I have had someone measure that in my own classrooms, and I severely underestimate that percentage, I think I speak much less than I do, and so it’s a reality check. But now that plus 100 other statistics about your class, average wait time, how many open-ended questions have you asked? How many times do you say “hmm” when you’re teaching? How much back and forth there’s between students, like a volleyball kind of dynamic rather than ping pong, where they’re always going through you? All of those things which you might have imperfect perceptions of what’s going on. You could imagine an AI tool being able to capture and give you a more realistic sense of what’s happening in your classroom.

John: I believe that there is a tool out there that will record your classes and use AI to give you some feedback. What’s the name of that application?

Dan: The people that I actually know and highly recommend the tool is called TeachFX, and they’re able to do that, and maybe some of the things that I’m describing now they can already do, but if not for sure, they will be close to being able to do. It’s almost like you have a full diagnostic of what happened in your classroom in a way that would be very hard to do otherwise.

Rebecca: So shifting away from teachers thinking about their own teaching and back into the hands of students being in control of some of their own learning, what are some ways that students can use ChatGPT to assist their own learning?

Angela: I love this question, Rebecca, because it’s a big part of the book, and it’s something that was very important for us to reflect. Dan has a very good sentence that he uses when he starts introducing workshop which is, teaching is not the same as learning, and so teaching effectively may not always be the same as learning effectively. So that said, I’d like to start this the answer to this question by highlighting one of the most important principles that we talk about in the book, which is that different uses of AI lead to different degrees of learning. A nd so we strongly believe there’s not such a thing as ChatGPT helps improve learning, or ChatGPT impacts learning negatively. It just depends on how it’s used. So I could give you a range of different uses that students can give to ChatGPT, and we actually talk about six of them in the book. They include things like getting feedback about their answers, which relates to this idea of using ChatGPT as a tutor. They are things like learning about a new topic or finding arguments and counter arguments about a topic. These are basically anything that a student has to do in their schooling process, t here’s a way in which ChatGPT can learn. What I want to give you is this very specific example that we include in the book, and that explains how ChatGPT can be an agent in the active learning process, rather than an agent in making learning passive. And it’s actually an example that I did myself when I was trying to create an essay for one of my philosophy courses. And one way in which I could have gone about this exercise is asking ChatGPT to write the essay for me. And I could have said this topic is about effective altruism, please write an essay of 1000 words with some pros and some cons, and then just submit that essay. That would have, of course, negatively impacted my learning, and it would have been cheating, obviously. But there are many other ways in which I could use ChatGPT for the same task. What I did in particular is I asked ChatGPT to play the role of a philosopher that was very acquainted with Peter Singer, one of the philosophers that was particularly relevant for the topic that I was writing about. And I had a debate, a back and forth debate, with the AI tool while the AI tool was impersonating Peter Singer. And so I threw the AI some arguments, I threw the AI some questions, and then the AI tool would answer me back, and that was incredibly helpful for me. It helped deepen my understanding of the philosophy, it helped me strengthen my arguments, and it overall improved the quality of the essay that I ended up submitting massively. Now it’s very difficult for educators to understand how students are using ChatGPT in the background when they just see the end result, but educators can play a role in encouraging productive use of ChatGPT.

Rebecca: It’s a super fun example. [LAUGHTER]

John: It’s something I’ve encouraged students to do too, to have it play the role of an economist and then ask questions about current policy debates. In particular, I suggested having it play the role of David Ricardo and ask questions about trade barriers in an introductory class. And they enjoyed that, or at least the students who tried that enjoyed it. It wasn’t a required activity, but it was just something I encouraged them to do.

Angela: It’s fascinating. Yes, I agree

John: What are some of your own favorite ways of using ChatGPT.

Angela: I’m using it recently for things related to marketing. I find myself having to create a lot of marketing copy, which is something that falls way out of my comfort zone. And it helps me make my communications more crisp, and it helps me with creativity. I use it a llot to brainstorm ideas where I just say, “Give me 10 different jokes that I could use in starting this email, for example. I find it amazing. It solves my life. It’s my best friend these days. I have been integrated into almost everything I do, and I’ve been finding it particularly helpful for this task.

Dan: Going back to what I said at the beginning, I think of it as a thought partner and as an assistant. As a thought partner, a way of brainstorming lots of things. So it could be teaching related or not teaching related, particularly when thinking about broadening things, I think it can be very helpful. But I want to talk maybe about a use that we haven’t touched on and which I think might be of interest to people, which is that it can be incredibly helpful at doing things that we hate to do with our computers. So for example, the other day, someone sent me a list that, in reality, I would have preferred to have a table, because it really had literally separated by, I’m not even sure, space, double space, sometimes comma, whatever it is. And I just copied and pasted that into ChatGPT and I said, “Give me a table with this.” So anything having to do with formatting, you’re much more likely to be able to do it quickly with ChatGPT or similar AI tool than you would trying to do it manually. So another of those menial things, I know it’s not a big sort of deal to do it, but I basically had to write an expense report for someone who had invited me to do something, and all I had on my computer was a scan of the eight receipts that I had. And one day I said, “Okay, I have to do it.” And so I just dragged the eight receipts to ChatGPT and I said, “Please prepare an expense report with a table in the middle, listing each expense and totaling the expenses” and in three minutes counting the time that I edited that letter afterwards I had the expense report written. I know that’s like a super small sort of thing, but think about a number of things that whenever you find yourself dreading to do something on your computer, you should think of this almost like as an augmented operating system. It’s almost like as an assistant. Normally, when you’re trying to do something in the computer and you need help, you might go to someone and say, “Can you do this for me?” And then you have to wait. Here it’s almost like the assistant is right there in between you and the computer, and able to do lots of things like this, even things like, here’s a printout of a table that I had on a piece of paper, put it on an electronic table, add these two columns. So after you brainstorm if you want things in a different format. The other day I had someone send me a bunch of numbers, and I just copied and pasted and started doing some basic data analysis with ChatGPT. So lots of activities that would normally take you more time are ones that you can do. Those are not very profound uses, but if they save you time so you can go back to your research, your teaching, or anything else, or even fun, that would be great. And then just as a couple of fun uses: one, next time you’re at a restaurant and there’s a dish that looks delicious or that tastes delicious and that you want to have a sense of how it was done, take a picture, upload it to ChatGPT and say, give me a recipe for this dish. I’m sure it’s not going to be as good as the restaurant, but it can literally give you a sense of that. And then nowadays, with audio capability, you can literally have conversations with ChatGPT. You don’t even need to start typing. So all of those things are now possible.

Rebecca: I like the idea of saving dread. That’s good. [LAUGHTER]

Dan: Yes.

Angela: Someone once told me that they took a picture of their fridge in their kitchen and then asked for potential recipes they could cook during that week. So the people really get very creative with what they can do with ChatGPT once they start experimenting

Rebecca: Anything to save a headache, sounds like a good plan.

Angela: Totally.

Dan: Absolutely.

Rebecca: You included a chapter in your book on creating chatbots. Can you talk about some ways that an instructor can use chatbots, either for their own work or for student use?

Dan: I think Rebecca, the distinction you just made now, I think that is a very important one. You can create a chatbot for your own use, or you can create a chatbot for others to use. Others could be your students or any other person that you want. So for your own use, if you think about activities that you’re constantly doing, and you don’t want to write a prompt that has a context every time that you go into ChatGPT, you could have a bot that helps you with this repetitive task. So a simple example would be, suppose that you have a course where you want ChatGPT to help you every class session, you want to upload your slides, and you want ChatGPT to give you feedback. And so you might create a bot where you upload the syllabus of the course, any material of the course that’s relevant for ChatGPT to have as context. And then you have a prompt for the bot that describes the type of feedback that you’d like to receive. Now you could, every time you’re going to teach a class do exactly what I’ve just described, so upload the materials, put the prompt and so on, but without much extra effort you can create a bot that basically allows you to go to it every time, and then instead of having to do all of that from scratch at that moment, you just sort of say, “Alright, here are my slides for tomorrow. Go ahead and give me the feedback,” and so it’s a very efficient way of being able to do tasks that you do often. I would say one important thing, maybe the most important thing that I think Angela and I can say about bots is that they sound like something very hard to do, but they are not. If you already have the basics of how to prompt ChatGPT, creating a bot is not anything extra. There’s no code. It’s English. It basically has two components. You upload files and you write a prompt, and that’s it. You obviously need to test it to make sure that it does what you want and so on. So that’s number one. That’s for the instructor. But you can also create bots for your students or for anyone else. And so there, I think there are interesting opportunities. in the teaching realm. You would upload your syllabus, you will upload a bunch of documents relevant for the course, and then, depending on what you want the bot to do, we have lots of examples in the book about what educators have done. So, for example, our colleagues Sharad Goel and Teddy Svoronos created a bot to answer questions about statistics course and give practice problems for them. We have David Malan from CS 50 created a bot to help answer computer science questions that students often have at different times in the day and at night. So there are many, many different uses. You just have to think about, okay, what are things that I would want my students to have access that maybe I cannot provide that access right now, and it might be a good thing to do. But the bottom line is, this is not rocket science, if you know how to prompt, you know how to build a bot.

John: You also provide a companion site. Could you talk a little bit about what’s on that companion website?

Angela: Many things, but on the topic of creating chatbots, we actually have that same chapter linked for free in our companion site. So as Dan was encouraging educators to just try and give it a go, they can refer to the chapter that is linked in our companion site. A lot of the work that we did with this book was try to demystify AI for people that were not familiar with this technology. So we are not using any particular slang. It’s all conversational English to explain how to use it. In addition to these, we have other resources that, due to the nature of AI and how rapidly it’s changing, we expected them to potentially be out of date soon, and so we have various types of resources linked. One example that we reference on our book and that we feel that people may want to explore in more depth, too, are the same prompts that we included in the book are included in a document that is linked in the site. And we feel this is important, because if anyone is reading the book and they feel that they want to try out something, instead of going to ChatGPT and having to write the prompt from scratch, they can just go to this document, copy and paste and have the prompt ready to go. And then we also include some additional research or additional resources that can be useful for people who are interested in learning more.

Rebecca: One issue that students are definitely concerned about is the effect of generative AI on their job prospects. What types of jobs are most likely to be affected and will the widespread availability of AI assistance reduce the productivity difference between skilled and less skilled workers?

Dan: So, I can see that John was behind this question, because I know he’s an economist, and I’ll be curious to hear your views on this. As part of the job that I do here at the Kennedy School, I started co-teaching a course on generative AI, and we had a session on generative AI and jobs. And so I think the short answer is that I don’t think anyone knows for sure. So people are trying to understand how these tools can be helpful. I find historical evidence about technological breakthroughs to be at least informative, but there are people who say that we’ve never had any technological revolution that was as disruptive as this one. And so those people basically say this time is different. But the one thing I would say is, if you study technological innovation in the past, almost every time, people have said this time is different. [LAUGHTER] So I’m not sure what to make of it. Maybe this time is indeed different, but I think at a super broad level, I think we’re not gonna run out of jobs. I think some jobs will disappear, and some jobs will substantially be augmented by AI, and some new jobs will be created. That’s my best guess as to what will happen. Students who go to college, my sense is that they will probably be in a relatively good position to take advantage of AI. But if I had to give students advice, is to take advantage of the opportunity of being in school to learn how to use AI in a productive manner. I think we tend to sort of think that our students will figure it out quickly, but I do think that there’s ways of using it that can help students learn more effectively and just to work more effectively. Whether it will reduce the gap between skilled and unskilled labor. I think that feels to me that there are different sides of that argument, so I leave that up to you, John, to sort of say in which side you’re in. On the one hand, some people say that it’s substituting cognitive work, and therefore it’s going to reduce the gap; It’s one of the technological innovations that seem to be displacing more of the cognitive work than previous technological innovations. On the other hand, it might be precisely for the skilled labor for which the technological innovation can help increase their productivity.

John: Yeah, it’s really early, as you said, and it’s really hard to guess where this will be heading, but some of the initial studies, I think, have shown that it’s tended to raise the productivity of less skilled workers, or less experienced workers, relative to those of more skilled workers, which could reduce some of those gaps and perhaps narrow some of the pay differentials. And we don’t know in what direction that will go.

Dan: It could.

John: And on a broader level, there’s the whole issue of AI raising productivity of workers, which could result in substantial gains for society. But the question is, how are those gains going to be allocated? Will they show up in the form of higher incomes for everyone? Will it be concentrated among the firms providing AI tools and services, or will these be shared? Could we end up with that 15-hour work week that Keynes predicted way back in the 1930s?

Dan: I’m skeptical that we will end up in the leisure society of 15 hours a week, because one of the people that I follow on this topic is David Autor, a professor at MIT, and he argues, I think convincingly in my view, that we as human beings have an insatiable sort of capacity and demand for new products and services, and I think that’s going to feed the next generation of products and services that we will want to consume. And if you think about occupations that existed at the beginning of the 20th century that no longer exist, and now there are occupations completely new that didn’t exist before. And my sense is that 20 years from now, there will be occupations that we didn’t even think would exist. So my best guess is that it can have a positive effect on productivity and therefore increase in general economic well being, but I don’t think it will increase it to the point where we’re all gonna work less. I suspect that the two of you will work just as hard, even if AI could do half of your job. Maybe you’ll create a second podcast. Maybe you will do some other thing that will keep you occupied. That’s my best guess. But again, this is highly speculative. I don’t really know what’s going to happen.

Rebecca: Dan, I feel like you know John really well. [LAUGHTER]

John: And the work week has been stuck at about 40 hours since the 1940s or so, roughly.

Dan: I do want to sort of at least mention very explicitly that, even though the tenor of this conversation is one that is positive on AI, there are obviously lots of challenges and risks. On the Teaching and Learning realm. Angela already mentioned, but AI can be used in ways that hinder learning, that are not going to help learning if students start using it just to do homework for them instead of with them. So I think there are many, many challenges and risks inside the teaching and learning realm, and there are obviously huge societal level challenges, just to mention a few: misinformation and disinformation, challenges of intellectual property, challenges of jobs that we have just discussed. There are some people who are really worried that, even though, in the long run, we might not lose jobs, the reality is that some jobs will disappear, and so do we have a strong enough safety net to be able to help people transition from one kind of job to the other? And there are obviously existential risk that people talk about, what AI could do if it were out of control… so beyond this podcast. But just to return to the job of an educator. I think if you’re an educator and you’re not using AI, we suggest that you at least experiment to see how it could help you advance your teaching and learning goals, and maybe even try to sort of see how your students are using generative AI, because that would be very instructive in terms of you hinking about how you can use it to stimulate their learning.

Rebecca: So we always wrap up by asking: “What’s next?”

Angela: For us, the hope is to get our work out there to as many educators as possible that could benefit from it and continue learning, because there’s so many changes that are still going to happen, and so many that we don’t know. So staying open to learning and to exploring how to incorporate this technology for better teaching and better learning.

Dan: I agree with everything that Angela just said. I think for me, I’m fascinated with the potential of generative AI helping us learn. Not just help our students learn, but help us as educators learn. And so to me, it’s like incredible to literally have available 24/7, a resource that I can ask questions and have conversation with in ways that wasn’t possible before. So in terms of what’s next as an educator, I’m very curious to continue this journey. As an educator thinking about our students, I’m just literally fascinated just learning how they are using some of these tools. And if you could do one thing as an educator, maybe create space for them to not just use the tools, but share with you how they’re using it. So it could be something as simple as you build into an assignment, answer this question, and then go into an AI tool, get some feedback, and then tell us how useful the AI tool was for you. And I think you’ll start discovering some really, really interesting uses that, as Angela said before, get closer to fulfill the promise of personalized learning that we have been sort of exploring for many years.

Rebecca: Well, thank you so much for joining us. This has been a fun conversation.

Dan: Thank you.

Angela: Thank you so much for having us.

John: Thank you. It’s an exciting topic, and thank you for helping lead people along in learning more about AI and how it can be used.

Dan: Well. Thank you, John and Rebecca, for the work that you do to illuminate us, not just in use of AI, but in so many topics related to teaching and learning, you do a huge service to the teaching and learning community. So thank you very much.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on iTunes or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

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239. Credential As You Go

Posted in equity, higher education, inclusion, learning, microcredentials, personalized learning, social justice
May 11, 2022

Students from low-income households often encounter barriers that prevent them from completing a degree. These students are left with a large burden of student debt, limited job opportunities, and low wages. In this episode, Nan Travers and Holly Zanville join us to explore the possibility of a flexible education system that would allow students to gain credentials incrementally by documenting all of their learning throughout their educational and career experiences.

Nan is the Director of the Center for Leadership in Credential Learning at SUNY Empire State College. Holly is a Research Professor and Co-Director of the Program on Skills, Credentials, and Workforce Policy at the GW Institute of Public Policy at George Washington University. Nan and Holly are co-leads on the Credential As You Go project.

Transcript

John: Students from low-income households often encounter barriers that prevent them from completing a degree. These students are left with a large burden of student debt, limited job opportunities, and low wages. In this episode, we explore the possibility of a flexible education system that would allow students to gain credentials incrementally by documenting all of their learning throughout their educational and career experiences.

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John: Thanks for joining us for Tea for Teaching, an informal discussion of innovative and effective practices in teaching and learning.

Rebecca: This podcast series is hosted by John Kane, an economist…

John: …and Rebecca Mushtare, a graphic designer…

Rebecca: …and features guests doing important research and advocacy work to make higher education more inclusive and supportive of all learners.

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John: Our guests today are Nan Travers and Holly Zanville. Nan is the Director of the Center for Leadership in Credential Learning at SUNY Empire State College. Holly is a Research Professor and Co-Director of the Program on Skills, Credentials, and Workforce Policy at the GW Institute of Public Policy at George Washington University. Nan and Holly are co-leads on the Credential As You Go project. Welcome, Holly. And welcome back, Nan.

Holly: Thank you.

Nan: Hi.

Rebecca: Today’s teas are… Holly, are you drinking tea?

Holly: I am, green tea with lemon.

Rebecca: Perfect. How about you, Nan?

Nan: I’m actually drinking a plain seltzer. [LAUGHTER]

Rebecca: That’s a good choice. It’s a good choice.

John: And I am drinking Irish breakfast tea, again.

Rebecca: I’m back to the supreme English breakfast again, John.

John: We’ve invited you both here to talk about the credential-as-you-go project. How did your collaboration on this project begin?

Holly: Well, let me start, if that’s all right. I think about this as several trains, actually, converging in a train station. [LAUGHTER] First, there was the Prior Learning Assessment train, or PLA, which is really addressing the growing importance of recognizing learning acquired through prior coursework or through work, military travel, and other non-classroom venues. But at the same time, and this is many years ago, there was a national effort around reverse transfer to recognize significant learning equivalent to the associate degree that occurs when a community college student transfers to a university without acquiring the associate degree when they were at the community college, but they do acquire that learning once they’re en route to the baccalaureate at the university. So there were many efforts underway to develop mechanisms that will enable folks to award the associate degree to those community college students who transferred in many, many states around the US. And I was actually at the Lumina Foundation, then, as Strategy Director working with several foundations to help fund this type of work. But really troubling in this work, as great as that was, was a lack of parity for students who started at the four-year institution. What about them? What about them when they acquired learning equivalent to an associate’s degree? What about fairness? The data were telling us that 50% or so of the students at community colleges and public four-year institutions leave before the baccalaureate. So those tea leaves, [LAUGHTER] since we’re speaking about tea, were really clear, that higher education loss recognized valuable learning, credentialed learning. So when I was at Lumina, we made a new grant then to SUNY Empire State College to explore the concept of a credential-as-you-go approach for both community colleges and universities that would recognize that important learning is acquired at the workplace as well, assess that and recognize that in an improved system overall, and that would pull on the importance of prior learning assessment. And it would also incorporate advances in technology that were really getting much more prevalent that would let us think about data systems that could automatically determine when you had completed all the requirements for a credential, and Nan, luckily, was the PI at the effort that was working on that. And that’s when we started to collaborate, particularly on this larger credential-as-you-go concept, and implement a rapid prototype and to really try this out.

Nan: So in addition to that, we also had some other projects that were going on. At that time, Holly was also the strategy director at Lumina Foundation, in which we were looking at some different frameworks for being able to assess student learning, as well as really connecting the different kinds of competencies and credentials. And so with that work, we were also working with the Corporation for a Skilled Workforce, Larry Good, who is the third co-lead on this project. So we’ve really been trying to think about how to bring together all of this work over the years that really works to being able to identify learners’ knowledge and skills, and being able to credential that regardless of the learning source.

John: So the basic problem is we have this pipeline where students go into colleges and don’t finish and they end up with these very high burdens of debt that make it really hard for them to make progress in their careers and so forth. And so the goal, then, as I understand it, is to tie all the learning that students have done together into some type of pathway that will allow them to progress forward, both prior learning that they’ve done on the job and in their everyday experiences, as well as any additional training they require through formal college and microcredentials. Is that a good summary of the goal of this program?

Nan: Yes, John, it is a good summary of it. What we really want to make sure is that learners are not left with learning on the table where they have to walk away from it and not be recognized for what they know and they can do. With the current higher ed system, we have a four-tiered degree system that we are basing everything on, but many learners actually come, they get the kinds of competency use their knowledge and skills that they need, they go to work, they gain more knowledge and skills in the workplace, and many of them go unrecognized. The data is pretty clear on this, that a little more than 50% of the adult population in this country do not have a college degree, and 1/3 of those have some college and no degree. So what we see is that we have a very large number of people who have learning that could apply to a college-level credential, but are not being recognized. And so we really have to be rethinking this system so that we can meet the needs of our adult population across the country.

Holly: And one last point I would add is that, in the middle of all this flux over the last 10, 20 years, workforce demands have changed, and the workforce demands are changing such that degrees are not required, particularly in this climate now for all jobs. And that means that shorter-term credentials that save some time and money that enable individuals to enter the workforce, and skill up over their entire lifetime. College is not just for six, eight years, it’s going to be a lifetime of acquiring competence and skills. It creates a situation where we need a different type of post-secondary system, we need a system that is made up of an array of different kinds of credentials, some short term, some that stack toward degrees into pathways, and that there are many ways in which you acquire competency and skills. So the employer side of the learn and work ecosystem has become very, very important. And we’re striving actually toward a transformation of the entire higher-ed system.

Rebecca: So, microcredentials have grown in popularity over the last decade and this idea of stackable credentials, there’s certainly a move in this direction. But why is there a need to go beyond just these more typical micro credentials and typical degree pathways that we’ve had before? You’ve talked about this need to do something a little more than that.

Holly: But it’s interesting that you use the word microcredential as typical because they are not typical in the current system. The typical system was what Nan already referred to, our four-tiered degree system, is typical. And what we’re recognizing is that there is an array of credentials. Some folks call them microcredentials, some call them non-degree credentials, some call them micro pathways, there are more and more synonyms for essentially what is the same thing, and it’s causing tremendous confusion in our system. But we do recognize that shorter-term non-degree credentials are needed, and they need to be woven into a system that makes sense that students and employers can understand. And that we can make changes in our curriculum to accommodate this change around the array of credentials that have value.

Nan: And so we really want to be able to capture people’s learning throughout their entire learning process, and not have the perception that when you’re done with a degree, you are just done with your learning, and that somehow, you can’t be captured for additional learning that you’ve acquired. And so when we think about the types of learning that is happening in the non-credit, and the credit from entry level all the way up through… we were talking with one of our institutions earlier today, one of the institutions in the project, and they were saying at the community college level, that they have the highest number of graduate students taking courses at their institution more than the universities in their whole area. And so when we start to think about the ways in which adults are gaining knowledge, and gaining the skills that they need in order to stay employed, to get reemployed, upskill, and be able to also continually grow and learn throughout their lives, the system right now does not capture that. And so we leave people hanging out there where they’ve got to figure out a way to get recognized for what they know. And so really, what we want is a system… and that’s why a project is called credential-as-you-go… that as people move through their lives, that they can be recognized formally, and be able to use those credentials in order to continue their education and for employment.

Holly: The other factor that’s emerging in this is the technology factor, and the development of learning and employment records, a type of e-wallet, as many people are calling them, that would be self sovereign, where learners, would take it with them wherever they go, kind of like your health record. And that on that record goes learning acquired in the world of work and from the military and from your college and universities, in some cases from high schools, where students increasingly are acquiring some industry awarded certifications and certain types of certificates. And that technology record is going to be very important to fit into this entirely transformed system that many folks are envisioning.

John: So one of the goals is to work towards a sort of common platform that any type of learning could be incorporated into to make it easier for individuals to share what they’ve learned with potential employers and for employers to get some record of the training that people have acquired?

Nan: There are groups that are working on platforms, such as what Holly was just talking about in terms of that digital wallet, or the learn and employment record, the comprehensive record that would incorporate, we do see a lot of movement there. Our project is not focused specifically on that, although we are integrating that work and looking at the ways in which technology can really help us here. And including in that technology is also thinking about a more comprehensive auditing system where people can be auto awarded for their credentials, because what we’re anticipating is that as people are acquiring more and more of the incremental credentials, that there are times that a learner will not realize that they’ve met one of these and that they need to be doing that. So there is a part of our project that is looking at technology and the technology solutions, but thinking about it from a holistic perspective in terms of what is the learner need, what are institutions’ needs? And then at a national level, what are the kinds of repositories that can really provide the right information about credentials, such as credential engine. And so by bringing those kinds of three pieces together, thinking about how we can meet the needs of the future of education.

Holly: And I would say, John, the one issue that I have with what you said was higher education doesn’t typically think of having common credentials, because there are many differences among them, among their kinds of programs, etc. And what we’re purely interested in is transparency of credentials, I might call mine X, and you may call yours Y. But what we want to know is: “Can you translate that so an employer and the student and others can understand?” Just tell me what those competencies and skills are that that credential that I carry on my wallet stands for. And those are the issues that we’re trying to address is put them into some understandable language so that we can translate and then we can carry with us. We don’t think that there will ever be a common list of credentials, but they can be interoperable and they can be decoded, so we can understand what they are.

John: It’s hard enough to get people in a department to agree on what they’re doing…

Holly: Exactly.

John: …trying to get across institutions with that challenge, that’s probably beyond the scope of anything that could be done by a group such as this, but making more transparent the learning that people have received so that it could be shared more easily sounds like a really wonderful thing to be doing.

Rebecca: The conversation’s really interesting to me, based on the discipline that I’m in, because I’m in design, and in kind of a coding side of design. So this is not radical to me at all. This is why it seems typical. [LAUGHTER] But when we look at all of higher ed, it seems quite radical, but in the area that I’m in, boot camps are really common ways of demonstrating knowledge about certain kinds of coding skills, or portfolios are good ways of representing design skills, that are these transferable kind of credential like things that you can show somebody and people understand what they mean.

Holly: Yeah. And some disciplines really do lend themselves to common standards, because they’re accredited by national, in some cases international, organizations. So nursing and cybersecurity and there are several other professions. Many of the IT professions are guided by common competency standards, and the faculty in those disciplines, they’re not troubled, just like you’re not so much Rebecca. [LAUGHTER] On the other hand, many of the liberal arts faculty do approach their disciplines as very unique. And so we have got to accommodate the largesse of the post-secondary system. And the ask is just tell us what competences and skills your credential stands for, and let everyone else figure it out.

Rebecca: I’m really curious about how you see this project impr oving equity for students. You’ve hinted at some of these ideas as we’ve been talking, but I’m wondering if you can underscore maybe some of these ideas.

Holly: Nan, maybe can add a few of the statistics that are so sobering, but I would say that credential-as-you-go, has a high priority on fairness, on equity in a higher ed system. And Nan’s got the numbers in her head. She talked about it earlier today. So I’ll let her explain why equity is at the top of our list.

Nan: So the numbers are actually quite striking when we look at those with credentials, and then those without credentials, and when we look at those that have been credentialed, around 78% of those with college degrees who are adults are white, and then the balance is divided across different racial and ethnicity groups. And when we start to look at that in terms of the proportion of those groups in our population, it becomes even more striking, because if we just look at the white population, only 33% of the population are white and yet 78% have degrees. So we really have an unequal system here. We also did some analyses that looked within groups to get a sense of, within any one group, where are the proportions falling in terms of people without credentials, some college and no degree and then being college credentialed. The highest proportion of adult learners that have some college and no degree are our black learners. And they are also carrying the highest debt load. At the same time, again, the proportion of those who are white are much higher in terms of college educated. And so we really see that credentialing is a equity issue. And when people are not being recognized for what they know, they’re not able to get the types of employment that they should be getting. And then it becomes an economic downturn cycle. So if you take our black population with having the highest college debt load, and then they’re also the highest percentage with some college and no degree, then they can’t be getting the jobs in order to be paying off the debt and moving their way up. So yet, if they have some college and no degree, they have knowledge that can be credentialed. And so we really feel very strongly that credentialing is an equity issue. And by credentialing learning, as everybody is going through it, more people can be recognized for what they know and can do, and are able to get the jobs that are appropriate for the types of knowledge that they have.

Holly: And many new job areas, I would add, are opening up that require possibly the equivalent of an associate degree, shorter and shorter term credentials around drone technology and sensor technology areas. But if we don’t open up doors and enable folks to enter into those programs, they’re not going to have access to good paying jobs.

John: And in addition to the racial inequities, there are also some fairly significant ones by household income, and also by first-gen and continuing generation status, which is also very highly correlated with the racial gap. So we’re seeing essentially that people who are privileged in society or children from households that have more privilege are likely to acquire more education and see relatively large increases in income, while households who are in the bottom of the income distribution end up falling further and further behind. Because much of the growth in income inequality has been due to the rising skill premium associated with education, and so forth. So this is a really important initiative that can do quite a bit to help clean that up, I think.

Holly: And there’s an important reason as Nan raised is that why the graduate student numbers are growing at community colleges. So they put time and money into developing graduate degrees, baccalaureate degrees, too, and then they can’t find good jobs, because they don’t have a strong enough skill set. So they’re going to go to a community college, potentially, and that’s where they’re going to acquire skills needed in order to enter good jobs. So it’s affecting everybody.

Rebecca: Do you see this as a replacement for traditional college pathways, or an and/or, both, all?

Nan: So when we talk about incremental credentialing, we’re not saying, you know, just the smaller term credentials, what we’re saying is that we need to credential people as they go through their learning. And so some of the credentials are degrees, some are certificates, some are diplomas, and some are microcredentials, some are badges, some are these other kinds of terms that are coming up. But what we really are saying is, let’s not just leave it to only a few ways of being recognized, let’s create a whole array. And so everybody is recognized along the way. But the degrees are part of the system, we’re not planning to replace the degrees, what we want to do is add to the possibilities.

Holly: So we are calling for a transformation of the hiring system that recognizes the changing workplace demands that recognizes the need for equity, and opening up options that actually I like to think about this like going through the drive thru to get a burger. Invariably, I like a single burger. So I asked for the single burger. And usually the person on the other end says, “Well, don’t you want the combo?” And I say, “No, I don’t really want the combo.” But increasingly, I’ve been thinking that the combo in a credentialing metaphor is really powerful because a degree plus two badges plus maybe a certificate is going to maybe serve someone in better stead than the degree alone, or even the two badges alone. There’s a growing number of researchers who as students start acquiring this array of credentials and packing them together, who are trying to understand is there more return on investment for people who get the combo and I will contend that there probably will be, particularly over a lifetime. And that is the kind of system that we’re moving to.

John: So, these additional credentials fill a gap for people who don’t go through the traditional pathways. But can the use of stackable credentials also serve as an entryway into those degree programs for those who may not be able to afford full-time college at some stage of their life?

Nan: Absolutely. And so some of the projects that are in the initiative, they are taking existing degrees and breaking them into smaller credentials that add up. And so that’s one way. And some of the initial research out there, there isn’t much research yet, and this project also has a whole research component to it. But the research is starting to show some tendency of a greater persistence and completion when people are being credentialed in these smaller pieces. And so we are seeing people progressing and going along. So that’s why we hope to be able to document more of that trend. So the framework that we have actually has six different kinds of strategies that really interrelate and the stackable is one of them. But we’re also looking at the incremental credentials, being able to look at skilling and upskilling, specialization, thinking about transfer, thinking about working with employers, and also thinking about what we’re calling retroactively awarding when people already have gained knowledge and skills in courses and being able to credential that. So the idea is some people will still just go through the traditional degree pathway. But what if they also could pick up additional credentials. So just as Holly was saying, there’s the additional that’s the deluxe hamburger, but just in terms of thinking about right now, one thing is that we give people limited choices. But also what if we auto-awarded as people were gaining this so that nobody was going without that, that also means that the incremental credential is also distributed more equally across so that we don’t have some groups getting it and some groups not. But that just everybody picks up as many different credentials as they can. Because the more you can show and demonstrate and have that transparency about your knowledge and skills, the more viable you are for gaining additional employment or upskilling employment and continuing your education further.

Rebecca: Seems to me like a system like this to be fully implemented really requires a lot of change, not just the change in credentialing, the change in how we think about how these credentials are taught, who teaches them, how they’re rewarded, but also maybe how people finance getting these credentials in the first place.

Holly: Yes, all of the above.

Nan: And so we’re seeing some changes in some of the state policies around this as well. For example, in New York, the budget that just passed this last weekend, has some funds for what they call TAP, the Tuition Assistance Program, to be able to be used towards a more workforce focused credentialing, and that’s a new aspect. And Holly, why don’t you talk about the policies that have happened in Colorado?

Holly: Yes, and Colorado recently has provided enabling changes in statute that let the universities offer associate degrees, which previously they couldn’t do. And that gets at that issue I raised when we first started the conversation around reverse transfer associate degrees. What about those students who started at a four-year institution, when they passed the milestone of a learning equivalent to an associate degree can they receive an associate degree? Previously, they could not. They’re at a university and a university doesn’t offer an associate degree, Colorado’s policy now will be permissive, so that the universities that wish to offer the associate degree, and perhaps they’ll call it something else, will recognize that learning and we think that that will be very positive for students. Keep in mind, the majority of students work their way through school, they’re not going full time, by and large. And we think that they’ll find better work, part-time work, full-time work ,while they’re going to school, if they are credentialed well along the way.

Nan: In addition to that, we are seeing other states as well really developing policy around prior learning assessment, around using military credits, finding different ways to enable the adult learner to return to higher education, and recognizing the kinds of knowledge that they already have, which is reducing the cost of their education.

John: Traditional degree programs are often fairly rigid in their structure and that may not meet the needs of employers or the needs of the individuals, and it seems like this system would provide a lot more flexibility for students to create customized paths that are appropriate for their own goals.

Holly: Yes. [LAUGHTER] And the higher ed institutions, the programs are being pushed because of workplace demands to establish stronger partnerships with employers, particularly in their own local areas, in order to improve their programs. And this means weaving in industry awarded certifications, where they may blend well with the curriculum of the degree program, to provide more apprenticeships, more internships, more work and learn, to recognize learning that maybe that part-time person is learning while they’re working. So there’s a tremendous pressure on the faculty in order to make a lot of these changes that would really, truly transform our system into a real learn and work ecosystem.

Rebecca: The Advisory Board for this project is quite large in scope and varied in nature. Can you talk a little bit about how the membership was selected, and really the kind of buy-in that you’re having from this project?

Holly: When we first got going, we talked a lot about the big lift, this will be just to bring awareness to the field about the reasons for this kind of transformative change, essentially. And so we knew we would need to work with influencers. And we would need to cast the net broadly. So we decided, as difficult as it is to set up a large advisory board, that we should go large. And we now have 114 members, and frankly, we’re in the process of trying to invite maybe another 15 or 20. And what we did was establish the stakeholder groups that make up the learn and work ecosystem, so we could guide who we would select to join the advisory board. And we came up with about a dozen stakeholder groups in the broader learn and work ecosystem. There are the accrediting and the standards organizations, there’s industry, the data and learning management industry, the foundations, the philanthropy group, the government and quasi-governmental groups, the higher ed institutions, international organizations that because much of this activity is going on internationally, not only in the US. So when we came up with this dozen or so group, we thought, okay, who fits into those, and we sent out invitations, and we explained what we were all about. We didn’t know what the response would be. And I don’t think there was anybody that turned us down. And we were surprised by this and [LAUGHTER] gratified by this. So we have a large group and it is growing, we meet regularly to seek guidance from the advisory. We have some workgroups going, and this group is going to be very important as this continues in the future, to build awareness for and to help us influence many types of groups about the importance of this work.

Nan: The other thing to note here is, and Holly mentioned that we have workgroups by having a larger board, we are able to break into workgroups and take on some of these critical issues, and really start to think through them in a more detailed way. But on top of that, our advisory board meetings are not where we just asked them: “What do you think about this?” But we’ve actually been bringing in and having discussions around key issues, things like technology. Today, we actually had one where we had the three states that are in the project, there’s 21 institutions across the three states that are part of this. And they were showcasing the kinds of things that are going on and really talking about just the whole credentialing space within those institutions. So there are ways that we’re using the Advisory Board as really a think tank and really helping us move this forward. And as a result, we are also engaging some of the advisory board members to write and to be speaking on this and really helping us create a whole national campaign and movement around this credential as you go.

John: Rebecca mentioned the size of the advisory board, but they’re also people with some really impressive credentials from a wide variety of stakeholders. It’s a very impressive Advisory Board. We can share a link to that in the show notes.

Holly: When we were developing the short bios, along with the photos that exist at our website that describes who was on board, we told them that they would have a 100-word limit and no degrees, no degrees and institutions where you graduated from on the list .We wanted to focus on what experiences do you have that you bring to the table? And that was a little new way of thinking about your bio, and folks really resonated well to that.

John: …ties in very nicely with the project as well.

Holly: Yes, exactly.

John: This is a really ambitious project that’s clearly taking a lot of time and effort on your part and the part of many people. Why is it so important that this work be done right now?

Nan: I feel that this is really important. We mentioned equity, but it’s really a social justice piece. We’re talking about: how do we recognize people for what they know and can do in a formal way that can be recognized across the country or across the world? And when we think about that number of people… 36 to 40 million adults in this country have some college and no degree, we have to recognize that the current system, which is an old, old system… it’s an ancient system…isn’t helping the society move forward. It helps some, but not everybody. And so this is a really important concept to really start to be able to recognize people and to help them move forward with employment and further education.

Holly: And I’d like to address the question of scale. Often, when people talk about innovation, there are the pioneers out there that are building gardens of innovation all around the country, there’s a tremendous amount of excellent work that’s going on at institutions everywhere, and in many of the states to set policy that will enable more innovative approaches. But if you look at all the institutions, I would argue that the innovation is still a drop in the bucket in that, to really transform the higher ed system, we’re gonna have to go to great scale. And so right now we have three states that are prototyping, we have a fourth state that has asked whether or not they can look over our shoulder for a year and plan for how they could become involved in credential-as-you-go, and we think there will be some others that will do that as well in the next couple of years. But, we’re going to be facing a massive issue around scale, how to move from those gardens of innovation around the country to major transformation. And that’s where the national campaign is going to grow in importance. We’re going to need to have resources available for folks that probably will not be in an individual grant project, there isn’t going to be enough money on the table for everybody in the US to join the credentials-as-you-go initiative. So we’re talking about developing the best resources we can put together to enable those who are interested in the concept to want to try to do this on their own, so that we could give them playbooks of what does it take to do this work, explain why it’s so important, give them examples of the kinds of incremental credentials that are being developed by discipline, by undergraduate, by graduate level, by types of institutions, so they can get a range of efforts that are underway, and to put together and employ the policies that the states particularly are passing in order to enable this. So we’re in the middle of discussions about scale, how quickly can we get to bigger scale than really the current initiative. And so I feel like that is something that we’re going to be contending with literally in the next couple of years.

Nan: One other thing that I’d like to bring up, and it relates to the size of our advisory board, we can’t do this alone, it’s not just our project that’s going to make a difference. There are a lot of initiatives that are happening right now in higher education, and also in the employment world, where all of these initiatives are moving the needle a little bit, but everybody is wanting to see change. And so we’re finding right now, the time out there is such that as we’re talking with faculty, when we’re talking with institutions, we’re hearing people say, “Yes…” we’re not hearing like, “Oh, no way.” But we also are building on work they’re already doing, we are connecting. We’re thinking of ourselves as real connectors. And so thinking about the different kinds of initiatives, we see this all being in that learn and work ecosystem space, and that it’s important to really bring everybody together and look at this in terms of where do we go? What is this going to look like five years from now? 10 years from now, 50 years from now? How do we start to really think about the role of higher education in the evolving world?

Rebecca: I think when you’re talking about scope and vision, we can see where we want to be, where we’re headed in the vision that you guys are sharing as part of the project. What are some of the next deliverables your team has planned [LAUGHTER] to incrementally get us there?

Holly: Those are some of the deliverables: to use our advisory board, use the developments in the three states and some others, to pull together some best examples so that we can make them available. We’re talking about assembling a digital learner-work ecosystem library, that would be a wiki model, where we could identify what a lot of these terms mean, identify the networks and partnerships that are working in these spaces, in the broad arena of the learner-work ecosystem, and then identify what are the key initiatives that people who are having to build these highways can learn from. So we have a tremendous lift around developing materials and resources that will help the whole field to understand this better. And keep in mind the stakeholder groups that I mentioned, the 12 or so… policymakers don’t really understand this world so well, because it’s so rapidly changing. Employers are being besieged by candidates showing up with: “Well, I’ve got a badge here and a certificate of this and something of a degree…” and the employer doesn’t know what this means and they’re coming from different institutions. So we have got to help to make this world more understandable. And I would say those are some of the deliverables. We’ll have real examples,we’ll demonstrate what can be done. The research component of credential-as-you-go is very robust, and it will help determine whether or not the framework that Nan’s team in New York developed for pilot testing holds up and can help institutions in order to design a better curriculum. And whether or not it ends up being fairer for all. We need answers to those questions. So these are the deliverables that we have in mind for the next few years.

John: So we always end with the question, “What’s next?” Which is something you’ve already been addressing a little bit, but we’ll leave that open.

Nan: One thing that we haven’t really talked much about, but we do have a US Department of Education Institute for Educational Sciences (IES) grant that is funding some of this work. And in the grant, specifically, we are working with three states, 21 institutions, and by August 2024, we’ll have a minimum of 90 new incremental credentials that are part of what we’re looking at. It is a research grant. And so as Holly has already mentioned, we have extensive research that we are doing. It’s got two priorities, one is looking at the feasibility. What does this really mean? How do we really see that this works? …and the other is looking at student outcomes. And so we have a whole comparative analysis piece that is looking at student outcomes. And then the third leg of the grant is looking at this national campaign: How do we really help people with the messaging, helping all the stakeholders really understand what this is about and the value of it? And as a result, we’re building out a website that will house a lot of tools and resources that will be available for people to use. The research will be there, a lot of messaging, all different kinds of things that we’re building out. So those are some of the immediate things. But then what comes after that is, we see this in a couple of different ways. One is adding on more states. And we even in the grant, develop some strategies to, by the third year of the grant, be able to start really helping others come along. And so when Holly mentioned the playbook, that’s one of the ideas that we’re developing, we’re already developing the resources to go in that. But how do we leave kind of the footprint in such a way that others can just come along… that we don’t have to have more grants that this becomes really normalized. That’s really what we’re seeking is that this is just the normal way we do business. And so adding on more people, taking the results of some of the work. So as Holly mentioned, doing this inventory of policies, we really want to be able to then have some strategies for different states to be thinking about policies, looking at state systems, what are the things that need to be in place there, what are the things that need to be in place as an institution, so sort of the next level of goals is really to see about how to help scale this up, but in ways that can be adopted independent of a lot of support and help. I always kind of laugh about that, this kind of work is you’re working really hard to put yourself out of business. We’re not in this to be hand holding lots of institutions and systems and states, but rather, really providing the resources and tools that are needed to expand the work across the country.

Holly: And so, I just think of two elements in the next steps. We’re in the proof of concept now. And I think we’re heading to go big. And we will need 1000s of trains steaming across the nation bringing these kinds of transformations to their systems, along with all of their different stakeholders. And I’m optimistic… I tend to be an optimistic person. It’s not going to happen overnight. But a lot of this is underway. And this is the time coming out hopefully of COVID, all these workforce changes going on, students waking up that this is a very complicated system we’re all living in and that we need to be skilling up for our whole lifetime. These trains have converged and we think that we’re going to be going towards scale very soon. And that’s, in my mind, what’s next.

Rebecca: Well, thanks so much for sharing some interesting ideas for all of us to consider and hopefully act on.

John: And thank you for all your valuable work on this project. It can make a huge difference.

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John: If you’ve enjoyed this podcast, please subscribe and leave a review on iTunes or your favorite podcast service. To continue the conversation, join us on our Tea for Teaching Facebook page.

Rebecca: You can find show notes, transcripts and other materials on teaforteaching.com. Music by Michael Gary Brewer.

John: Editing assistance provided by Anna Croyle, Annalyn Smith, and Joshua Vega.

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