Category: online learning

Show Notes

• Yakut Gazi
• Marina Aminy
• Van Davis
• Duke University Center for Teaching and Learning
• California Virtual Campus
• WCET
• WICHE
• Palmer, P. J. (1983). To know as we are known: Education as a spiritual journey.
• Khanmigo
• Bogdan Yamkovenko and Anna Bergstrom (2024). Khan Academy Efficacy Results, November 2024
• The State Authorization Reciprocity Agreements: SARA – U.S. Department of Education.
• Dweck, C. S. (2006). Mindset: The new psychology of success. Random house.
• UPCEA – Association for Higher Ed
• Van Davis (2026). And the Magic Eight Ball Says… WCET Frontiers. January 9.
• BiPartisan Workforce Pell Act

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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