Future Trends Forum - supporting faculty AI work

Greetings and welcome to the future transform, a weekly conversation about the future of higher education. How might higher education best respond to the AI revolution? In this session, we return to that major question by focusing on how to support faculty as they teach in research with AI. Our guests were Lance Eaton, Gail Matthews and Atali, and Michael, all from Northeastern University's Center for Advancing Teaching and Learning through Research. We explored the team's innovative approach to faculty AI support and development, including their cohort model, how to reach different instructors, thematic groups, disciplinary versus interdisiplinary work, simulations, AI literacy, and a lot more. I hope you enjoyed this session as much as I did. There'll be links in the show notes. And as always, the comment box stands ready for your thoughts and questions. >> Again, let me welcome everybody. Welcome to the future transformed. I'm really glad to see and hear from I hope all of you today. We have some fantastic guests talking about a really vital subject. I'm looking forward to our conversation here at the forum. We've been talking about AI for years and years. We've had sessions covering AI from multiple points of view from the point of view of developing open source to AI literacy to criticisms of AI to how to deploy it at scale. But one thing that we haven't really delved deeply enough into is how to support college and university faculty actually using AI. This week, I'm really pleased to have three guests coming to us from the American Northeast because they've been working at Northeastern University on a really interesting faculty development, faculty support model. Uh this is one where um people are able to work together in cohorts and to pair up with folks from as far as I can tell all over the world. Um let me just introduce each each of them and uh ask them to uh say hello and then I'll bug them with a few questions then it'll be over to you for your questions and answers. Uh let's start off with Gail Matthews Denatali. Hello Gail. >> Oh thank you Brian. Um, well, I'm here at Nor Eastern at the Center for Advancing Teaching and Learning Through Research. I'm the deputy director and Nor Eastern also has a an initiative called the Curricular Transformation Initiative that's basically structured around AI. So, I've been uh helping co-lead that. Uh, >> oh, fantastic. Well, that gets into my inevitable question for you, Dale, which is what are you going to be doing for the next year? What what what's ahead for you at the center for the next 12 months? Oh gosh. Um, I'll I'll say a few things, but I'd really like to toss it over to Michael because he's the director of the center. Um our biggest initiative right now has to do with uh two faculty directors who are embedded in each college and one focuses on redesigning courses uh to strengthen student AI readiness and the other uh is embedded faculty director to work with faculty in terms of developing their own kind of embedded cohorts within. So one one is student capability capacity building in relation to AI and the other is faculty capacity building. But Michael >> well over over to Michael who is actually in charge of the operation. Hello sir. >> Well you know in the world of AI being in charge is is an open question. >> Uh it is >> in many many ways but yeah I'm the director of Catler. I've been here 13 years um at Eastern >> and I saw somebody there from Austin, Texas. uh was in Austin for 9 years and um I Gail is doing a fantastic job connecting with um administration and all the other around the university fleshing out this curricular transformation initiative. Um there really hasn't been anything like it to my knowledge. Um and it's unfolding you know in fascinating and creative ways. Uh part of my job is of course Catler supporting that as much as it can as well as being a teaching and learning center for the rest of the university. So offering workshops and uh cohort groups around uh other topics that have been part of teaching and learning for a long time. So that is what I've been doing. And we were very lucky uh 18 or 19 months ago to reel in a big fish named Lance Eaton who I will pass it to now. >> What a transition. What a nice transition. Well, welcome Michael. Good to see you. And uh over to Lance who is by himself among other things going to resurrect the old rumor that you have to have a beard to be on the future transform as a guest. Um >> got that team on this. Got the team in. Is that the >> I like we've got one on the team if we can let him in. >> That's That's right. That's the secret. But but Lance, how are you, sir? >> Uh excellent and and appreciate you uh sharing this space with with us, Brian. Uh so, hey, everybody and and I see many folks I know and and it's super rich to to see you all. Uh I'm Lance Eaton. My role within Catler, I'm senior associate director of AI and teaching and learning. Um which means I get a lot of those questions of like what do we do with AI? And of course, my first answer is ask AI. Just kidding. Um it is, you know, this this ongoing question. Um and I've had the privilege and excitement to work uh at with with Catler at Nor Eastern um to start figuring that out. And I'll speak to one project that um more recently we've we are we've launched and done and we'll continue to build around which is we have been doing faculty cohort programs some really rich ones the last two years. We're continuing as Gail said in this next year and we started back in February to crowdsource uh what other people were doing around faculty cohort programs whether that was communities of practice fellowship programs uh you know tool like uh some kind of sustained community over time with faculty about what are we doing thinking about figuring out with AI and so we've pulled all of that information together people have been incredibly generous and we have a resour ource that has now over 130 140 different programs from across the world that you can go in and see different like see what are all the different flavors of this and we thought that was really important because like we're all trying to figure this out and this is a really interesting way of like well what are the different ways we can figure it out and so from that we were able to do a summit in May which was of people that are facilitating these programs to get together and just like share mental space about what we are figuring out, what is working, what are the things we still have questions around. And then we folded that into a two-day summit uh in early in early mid August. I don't know three weeks ago. What is time at you know at this point in the semester or start of the semester and really have a really rich two days of events where people were just able to get into rooms and have the space to talk and think about some of the challenges. And that experience was both for the facilitators but also faculty. So there was uh somewhere 270 people from over 125 colleges and universities were just in this rich low stress engaged space to think about how do we prepare for the next year? What can we do? And there's different topics like how to engage people that are uh how to engage the the continuum of people in their dispositions, how to keep programs going when there isn't funding, how to navigate like or how to sustain joy and well-being because >> poly crisis. Um, and so that's been what a a big piece that I've been working on within this as well and that we found to be immensely generative for our own work and just for like appreciating that we're all in the struggle with this and and having other people to share that with. >> Um, Lance, that sounds terrific. You said how many? 237 people >> about 270 somewhere between like 270 280 people uh attended one or more session. Um, and we have the m we're we're putting together some materials of from that to share on that uh that that page that we just shared and we're going to we're looking towards doing more future events. But for right now um we're also trying to get ready for the semester because that starts in a week and a half for us and I know for others it's already started. >> Uh indeed indeed my uh my first full class is in about two hours. Um, Lance shared uh a couple of links in the chat. So, make sure friends that you don't forget that. But I'm also going to share a link for Lance in the chat because Lance just a couple of days ago won a prestigious leadership award from Educ cause. So, I just wanted to say, you know, Michael is absolutely right. He's a big fish and I'm really glad to welcome him to the uh great lake of the future transform. Congratulations, Lance. Welld deserved. >> Thank you. Um, this is a great thing about having a beard is you can't see somebody blush. Um, but but it is definitely definitely welld deserved. And Karen, thank you for the excellent excellent uh for this. >> I thought it was fishy in here. >> I see. I see. That's pretty good. Or I should say that's pretty sweet. Now, we have um uh friends, if you're new to the forum, I'm going to ask our guests a couple of questions to get the ball rolling. Uh but then it's going to be over to you. Uh, so as Gail, Michael, and Lance answer and speak to their work, please think about what you'd like to ask them. You can tell they're very friendly. Um, and so please take to the chat box to start stretching out your ideas or use the Q&A box. So, one one question I I had um is if you could talk about the cohort model, uh, why did you create that and what were some of the benefits that you experienced from it? Now there are few three of you so you can figure out who gets to take turns at this. >> Well the cohort model just in general or the cohort model to support AI >> nor the latter >> the latter. Ah >> you want me to >> Yeah. Go ahead. the uh I think what's what's interesting at least what we've learned we're learning at Nor Eastern is that nor cohort models uh can get pretty far stretch around the uh down the road in terms of um magnifying effect because you're actually you're not trying to do it all you're investing in in faculty who are there doing it uh it all so it's a multiplier but there are inf institutional infrastructure pieces that um need to be in place. Uh so for example uh one of the things that we did uh last year and all of these have been structured around faculty. So, summer 2025, we got together a group of uh faculty to think about, well, you know, what are we after? Like, what does it mean to develop AI readiness or fluency, literacy or fluency in our in our students? Um, and you know, how are we going to look holistically at the curriculum? >> Um, >> and they grounded that in the deck framework. So, Lance, do you have that document? You want to share that? Yeah, I'll put it into the chat. >> And that was a that was a faculty-led group. So you you could say that that was that was kind of a cohort group that was doing something. Um >> likewise they had another group again faculty-led that that tackled the idea of AI and assessment task force and they produced a guidance document. And so those then become kind of these infrastructure things. Uh but they're they're developed collaboratively by faculty. Um and so then the next piece is sort of mapping the curriculum and saying where where do we want AI to be >> um and how do we want it to be and you know >> um and then now with the newest cohort uh groups of faculty faculty directors embedded in each college we've got a readiness framework uh we've got an AI and assessment guidance document and now and we've got identified the courses that people want to be redesigning. So, so then you have a cohort of faculty that are focused one on course redesign and focused one on capacity building and they they've just got some ground to stand on um that is institutional wide that allows them okay we've we've agreed on these things now we're going that way >> and these were so these were all northeastern faculty >> all northeastern faculty yes >> very good very good >> um and it's funny how the first the first cohort program grew the leadership for the all the subsequent work. So we started with the faculty fellows program um and and that was a cohort led by um Mary English on our staff and then that grew this leadership that then became on these the uh the the the fluency group and grew the leadership and they were the ones on the AI and assessment uh group and then now we've got two new sets of directors that are getting into the curriculum and and further capacity building uh all all of which have been supported by Catler. So that's it's kind of fun to see it grow. >> It sounds both fun but also productive. So the the cohort system really seems to benefit by getting downstream you having the first cohort making this stuff which everybody else. So there is a strategic sequence of the types of work that's being done but the the cohort model is sort of the engine the driver of of these kind of larger institutional priorities and strategies >> and in you want to go Mike I was just going to add you know I think from another perspective of uh that we've we've recognized and also have had look thankfully had the capacity is we have the cohort models and the the curriculum transformation as we've been talking about, but we've had two other working elements two other working elements that have been incredibly strong within this. Um Gail and Claudia Akoshi who's also on the call uh have been in and um several Rachel Tonchali who I don't think is on the call. um they are they're other members of of Catler and they have been doing a program around scholarship with faculty and uh having faculty develop I don't want to speak over you Gail but but just wanted to speak to something that you're leading but just I think that has been an important part is we've had another cohort that's been focused on research and then we've had another project um with Lii Apocalyp Claudia Koshi who have also been working with students to like also help to feed that stream um to feed that to help inform us even more about like what's going on in classrooms, what how are faculty thinking about even doing research to figure out AI's implications and the like. So, uh don't know if you want to say any more about that, Gail, but I just as you were saying, I think that's an important piece also that has been working well. >> I like to say I like to say community is the killer app. I don't think it's original to me, but I repeat it a lot like it is. Um, and that's a cohorts are a mechanism that we've used in Catler for a long time. Caller is the name of our center. Um, and they really do move the needle both with multiplying effect and also um, you know, network building and those those relationships continue u once the cohort experience is over. >> I think there's also the question of of fit. You know, Lance and I have been talking a lot about this idea of a cohort toward what p purpose in what context and what does it me need to be, right? So, for example, and I know Claudia Koshi is on the on the call and she's leading this program uh the AI and teaching and learning scholars. those people are doing research related to the impact of AI um uh social scholarship of teaching and learning studies. Well, we're going pretty deep with that. And so we we only have six people in that cohort. Well, you know, with this other curricular transformation, um, puzzling through all kinds of things and I saw Derek Ruff saying, you know, it's not only where does AI belong, but also where does AI not belong. You know, how do we we build our capacity to make um the the wisest decisions? Well, that that that cohort has 20 people in it because we've got 10 colleges and we've got two positions through the college and we've got this one 20 person cohort. We're going to be in a dayong retreat on the 11th >> and then those are breaking into two subcohorts >> um that are still connecting. So if we think about it's not a cohort program, but how can you rightsize it for the kind of work that you're trying to do within the institution? >> I imagine that you have a whiteboard with this elaborate map of of all the different groups, you know, cross-hatched back and forth. Um, this >> we did it we did in an earlier document. I I think we need to update that doc. We we we did last year and then uh and then we had no idea how much it was going to grow. >> Wow. That's a again friends, you can see why I had to have these folks here. Um then let me ask one more question. Um and I'm grateful to you for your your answers to my my cohort questions. Uh and this is um actually a a kind of this is a more of a curricular question. I've observed over the past couple of years uh two complimentary um drives in a lot of academia when it comes to AI. One is the sense that there should be a baseline understanding for uh all students who graduate. They should all have some kind of minimum understanding of of AI as a technology ethical issues how to use it well and so forth. And you one way of describing this is as AI literacy. And then complementing that is the idea that individual disciplines uh should have their own unique approach. So what does it mean to uh grapple with AI from within English versus within biology versus within economics and so on. And I I hear you talking about curricular uh integration. I'm just curious how did that dynamic play out? Did did you did your were your faculty interested in trying to support and extend uh AI literacy or were they more focused on their individual disciplines? >> Well, I think in in this instance um you know there is a little bit of a a top down with this. You know, President of our our our university wrote the book Robot Proof and >> um Joseph >> Yeah. and he talks about uh human literacies as being um and data literacies and and and technology literacy is that we've got this kind of constellation. And one of the things that that was a commitment was that um no matter what program a student was in that they would have an opportunity. there would be a pathway through which they could graduate with uh foundational understandings uh about in relation to AI because they're they're going into the workforce um sort of what do we what do we need to have? Does that mean that every course has AI in it? Absolutely not. Does that mean that every course is trying to develop all the literacies we need? No. But is there a reasonable pathway through which students can uh can can get this and get this in an experiential way? So it's it's it's grounded in real world practices and and challenges. So that was the conviction and then and then now we're seeing what it's going to take to fulfill that commitment. >> Thank you. That's that's that's a very nuanced and deep answer to my kind of shambolic question. Michael or Lance, did you want to add anything to that? I mean, I think >> I guess I I think some of what we're to Gail's point and and whatnot, like this is in in the first two years, I think we were able to do a lot of like top level like helping people just start wrapping their minds around and this this year feels like we are digging more into the disciplinary >> work. Um and I think that's some of uh some of at least across higher ed what I see happening at different places is the like there is something to carry within the disciplines because it will hit and react or it different disciplines have different needs and expectations. Um and so I I think we are that's part of what we're excited in this this next year is the opportunity for more of that space. Um and and particularly the like what what makes sense and what make what makes sense for your departments to hold because a thing that happens always with AI rightfully so is like we start to talk about AI and there's 5,000 problems and they're all valid. But when you start to just think about it, what's what's they're all valid, but what's relevant specifically to the department and what its particular goals and outcomes are. >> They be in those other things. It's not that they're it's not they're no longer relevant. It's where are the other mechanisms to engage with those? Um, and one of the ones I'd like to bring up is just the environment. Like the environment isn't, you know, I'm speaking to Brian who has literally written the book. Um, right. So, that's an incredibly important issue. But the question is, is that a department's curriculum question? It may be if you're like, if you're ecology, it may not be for other disciplines depending on how the institution positions itself. And so, trying to think about well, where does that go? if that's still an issue you want to f keep attention on in an institution. Um that might be something more that is more around governance or other things. And so uh I think that's that's kind of the other piece that's been really hard but I think this starts to give way to this is fantastic. I I I want to turn this over to the audience now because there are all kinds of questions but I did have one quick governance question to ask. Michael um your center is clearly taking a leadership role. To whom does your center report to the academic dean? >> No, actually at Northeastern um we have both a provost and a chancellor. >> The provost is the chief academic officer that that oversees what they call the verticals, which is the colleges and schools, the academic units. The chancellor oversees all the units that serve across those. >> So um so we report to the chancellor's office. Interesting. >> Yeah. And so that we the senior leadership team consists of the chancellor, the provost and a couple other people. >> Thank you. Thank you. I'm I'm very obsessed with how individual campuses structure their AI engagement through which body search committees and that's very helpful. >> Friends, the the chat box is just full of ideas right now. Also, a bunch of great links. Quick shout out to I believe it's Clausia Kawhi uh who shared a link to a a northeastern um resource. Uh and I want to bring in a few questions and u again friends don't be shy uh we're very welcoming and this is all about uh your thoughts and and your questions uh Dylan Murderer uh has a question which u is kind of turns this on its head. He says he observes that the dynamic I observed is that these kinds of cohorts often reach the people who need need them. Do you have success stories or thoughts about how to get non-users or folks who are avoiding AI into the room? Now, before everyone answers in the chat, there's a a wonderful thread that follows that. So, I recommend that to everyone. We'll try both submit that out. But I first want to give Dale, Michael, and Lance a chance to to address that. How do you how do you reach out to people who aren't, you know, the early adopters, the ones that you uh the ones that lead the way. >> I don't disagree. I think that a cohort is an investment of time and energy um and re-calibrating one's calendar and someone's got to be really really into something in order to kind of make that level of commitment. And so I think that there's a lot of there's a lot of middle ground in a cohort of people who are really really leading the >> on the leading edge and some who are really just interested in coming along, but there's a a great a great body of folks who still are just tiptoeing forward. And so I think that um lower bar engagement, smaller bite engagement. There's actually a a a location on campus that's opening up called the AI maker space which will have on ground face-to-face sessions where uh we hope to engage people who are more at that level of of approaching the topic. >> An AI maker space. >> Yeah. >> Oh, very interesting. I I don't think I've seen one yet. Um >> we haven't either. We're about to. It opens in a couple of weeks. We'll tell you after. >> Oh, please please do. If you could share, I'd be glad to uh spread that around. And >> yeah, and I think the other thing, you know, it's the with the cohorts uh some of the cohorts that we're running, they're really um about cultivating embedded leadership. So the faculty who were in the curricula transformation and previously in our fellows program they they they it was in part for them but it was in part we're picking one person from every college or one the college is appointing and now two directors for every college and their sort of task their their challenge is to to say okay how can we uh how can we really as Lance say have things very very embedded and and know the faculty and and know um you know who's um uh in a not participating in a very principled way. They have the literacies and fluencies they need. They've just they're just choosing to not be involved and who kind of hasn't really been connected with yet. Well, that those people are embedded in the college. They're faculty. they're respected people. We're convening the cohort of them to help them have a support network with each other. Um so that's sort of the the theory in terms of uh impact. Um >> there's one more piece I just like to add. Oh yeah. So there's there's the cohorts um which are the the directors in each college that we'll be convening monthly meetings of but uh we are also assigning liaison from our center to each of the colleges. So there's 10 colleges there's five liaison. So each of our liaison will be connecting with a couple of colleges the directors from those colleges in a regular way to help >> be shoulder-to-shoulder with them in creating and leading these cohorts maybe co-f facilitating something actually digging into the instructional materials in ways that um because we're a little bit on the outside there's that creative space between the insider and the outsider where things insights can be had uh that is super juicy for us >> and and I want to you know at the risk of being the Jinsu knife or whatever. Uh it's But wait, there's more. Um we've got a student faculty partnership component to this which is the AI instructional assistance. So those are people they're in master students who are hired by Catler. Um Lori Pup and Claudia Kawoshi handle an an orientation program uh for them. So there AI proficiencies are there the understanding of teaching and learning has been cultivated. Um and then as these these directors these people the faculty directors who are embedded in their colleges as they identify things that they need um they can kind of come up and connect with Catler and connect with the IAS. So it's a a more kind of projectoriented. So maybe they're running a fe focus group, maybe they're providing feedback on assignments, maybe they're um doing any of a number of thing, maybe they're doing a landscape analysis of what uh uh what is happening in the workplace that is relevant to the disciplines uh in relation to AI. So that's that's an important uh piece of it. We're not doing this without students. >> Excellent. Excellent. We we we thank you for these. We we have a question that comes right on top of this uh from our good friend in Maryland uh Steve Airman uh who asks roughly what fraction of NU faculty uh tenure track contract have been engaged with your center over some recent period of years like last year or the past two years. >> I think we ballpark between 20 and 30%. >> Yeah. Thank you. Thank you. Good question, Steve. >> And we can add that's the ones we know directly. We also know we like our website and that's harder to figure out, but we we see a decent amount of traffic on our website from uh it's from a variety of people, but we also know it's from faculty because we'll hear back from them about it occasionally. So uh there's that direct and indirect that is also sometimes hard to to capture >> you know and this is another thing that's hard to capture is okay once you move to an embedded model so we've got these cohorts of directors they're embedded within their colleges they're doing whatever they're doing they're maybe going to a faculty meeting they're maybe hosting lunch and learns they're uh one school has developed its own AI council and that's been something that's developed another school has got something called dash of a network group. We we're impacting that because the people who are in our programs are are kind of cohort programs are running that but we're not there to take attendance. So we don't know and nor would it be appropriate for us to take attendance but um but that's kind of where some of the people who would not ever come to a Catler program connect around AI. So I think that's the value of the embedded the embedded model that is really kind of responsive to the disciplines. They're people you know who are who have been at the faculty meeting with me therefore I want to talk to them. I'm curious an AI council stood up. >> Uh yeah, that was the college of of engineering. Uh the and then one of them two um in the college of arts and media design, they had an application form the college had for the director position and they had far more people apply than they thought were going to apply. >> And so the people who didn't get the director position, they're standing up a council out of that. So that becomes the cohort that the the director can be working with. So it surfaces interest that you didn't know was there. >> Thank you. Thank you. We have another quick exchange in the chat from uh Taim Olsen uh and who asked what is the funding for faculty participating cohorts and then she found it. Uh that was $5,000 and Michael added uh details about how that's funded and and how that's sponsored. By the way, uh Ta um uh you asked Lance a question and he asked a question back to you. So he want to know which of those um uh posts that you wanted him to uh to share. We have >> Oh, please go ahead. >> Yeah. One thing it's really interesting about the compensation because this this year we've been really trying to embed it in the cultures of the colleges in the academic uh life of the colleges and as we say kind of everything is an exception. We've got 10 colleges. We've got 10 10 culturalism lives. But some of them the the uh the compensation is a course release. Uh some of them the compensation is a stipen. >> Uh we've leveraged some money to have block grants. So for example, the person who's doing course redesign directing also has a a a funding source. So if they want to give stipens to the faculty for for doing that work. >> So um so there so maybe they're not getting that much money but they're getting access to these to these grants that they can then uh leverage change for. So whatever the incentives are can be very different >> also. Yeah. And also for some of the college embedded uh and we don't know how this is going to work. So there's a big disclaimer about this, but some of the cohorts that are being formed within the colleges, they're getting that to count as committee work. So people are getting service points for being involved in the cohorts. >> Oh, that's really smart. That's really smart. >> Um, questions are just coming in thick and fast now, so I want to make sure we get to as many of them as possible. Uh, and there was an exchange in the chat between Heather Derell and u and Shelley Furnus. Um, uh, Heather prompted it by asking, "Has anyone offered a forum where individual faculty members can determine their own AI ethical use statement?" And so, first, Michael Gail Lance, let me just ask, um, do do you have a venue for that in your work? >> I guess I' I'd ask a or a clarifying question. a former uh faculty can de develop like an individual faculty member or a group a faculty or >> oh I think it's individual faculty member um Heather went on to say she was thinking more along the lines of taking the institution expectation of providing others with the framework has anyone worked with faculty on their individual views prior to how those fit with institutional expectations and as I'm reading that in zoom Heather just sent me a shower of thumbs up So she was agreeing with you Michael. >> Um so first I mean uh so an invitation to anybody uh here on the on this conversation who has done that please chime in on the chat but for Michael Gaya and Lance uh do you have a a structure for that kind of um development? >> Not specifically that I'm aware of. >> Yeah. I mean last year um within the cohort that Mary led there was a a process of them developing a shared uh definition. And I'm I'm trying to remember what the prompt was. I know Mary's on this call. Um um and I think the dialogue around that was pretty pretty interesting. I think it included more than more than ethics. Um but Mary, if you're here, you could uh put what that whatever the guiding question was for that. Ask them to define Yeah, we asked them to define what ethical use of AI was. So the the cohort that Mary led and then >> individually they thought about it and then they they developed kind of a shared >> shared statement about as part of their cohort work. So it's it's there there was the moment of individual definition because you kind of had to do that to get to the group definition >> in I'll just I put into the chat you know uh >> one of my earlier pieces of work and continued sustained work is the AI syllabi policy collection and we do like we've done a workshop that is I agree isn't specifically the ethical question but it is the like like how how do you plan to talk and engage with your students about AI? Uh me and Larry Paclo who's also on the call uh have done this a couple times and and within that is some of that questioning helping them think about like how is how is it going to show up for them and how does that help them think about how they want to show up for students. So it's not the exact thing that um uh sorry I lost the person was asking for Heather >> but that thank you uh that Heather was asking for but it is within the vein of trying to think about like how are faculty showing up with this because if they're going to engage students with uh about the appropriateness the the ethical quandies for students to use it where where do they sit with it? >> Thank you. Thank you. And uh I admire by the way the three of you multitasking like mad um adding uh so many uh so many comments in in the chat. Um and there are a lot of resources right now. Um in fact uh Mary English um adds that she asked them to define what ethical use of AI was and uh one participants quote was or quote how can we use a tool ethically when the tool itself is not ethical. uh which is a kind of conversation that I've been I've been seeing quite a bit. Um more questions I want to make sure everyone gets a chance to ask. Uh our dear friend in Houston area Tom Hayes asks a typically uh provocative question. Can you give us some concrete examples of the kinds of changes people are making to their assignments? Are they about making the assignments more AI proof or people doing something more fundamental than that? >> Yes. Yes. Well, nice question. No, >> I mean it's both. It's both and and so we see some faculty who are um and I shared the the link to our AI gallery. I'll share another series that we do which is one thing to try with AI where they are getting students to critically engage with AI and its outputs to build some you know to build critical insight within the particular discipline. Um and then we have other faculty who are trying to think of like re that that big question of what does assessment look like that isn't blue books uh but isn't uh isn't blue books but isn't also um I shouldn't say blue books or uh proctoring but is is something else and I think some of it is is playing around or I've seen we've seen some faculty playing around with uh doing more things like galler walks or code walks live in the classroom. >> We've had a resurgence of faculty exploring flipped learning with a with a more focus on AI and that's a uh we have a resource that we're updating as a as a result of that. Um so those are the couple I don't know if Gail or Michael want to speak to others. I mean, Gail played a lead role in the development of a guidance document about assessment in a ubiquitous world. I don't know Gail. >> And that's Did Lance, did you post that? >> Um, let's see. Was that the Yeah, the the assessment document. >> Yep. >> Yeah. I'll reshare it in the chat because >> Yeah, >> this chat is amazingly filled. >> Yeah. And I'm I'm back on the uh the call. But I think one of the things that was interesting uh about that project was we began with a a a briefing document. So we the when the faculty group came together once again it was there was a representative from each of the 10 colleges and we realized that there was kind of a need to level set. So we started with a briefing document that just sort of laid out this the state of the field. Um kind of included a lit review. >> Um and then out of that uh we talked as a group and then each of the faculty went back to their colleges and they did some data gathering. Some held faculty focus groups, some did surveys. We had data in all different we didn't define what they needed to do. We just told them they needed to find out uh uh you know kind of perspectives, concerns um you know what people were discovering. One one person said our our approach to assessment is changing. It is not necessarily changing strategically. it's it's evolving and so what can we learn about you know about it from that pulled that back and through the magic of AI we're able to kind of reconcile very very different data sets um into some kind of findings uh and then pushed that back into the group of the 10 uh faculty members who then wound up uh working together to to help form the the things that we wanted to say in in that that guidance document. Um, so it was it was quite a process, but boy, I tell you what, the faculty who were on that task force felt pretty proud of it uh once they got they got through with it. So, I think attending to the process when you're developing documents like that is really really really important. It may take a little longer, but >> it's important. >> I do want to recognize the question asked for concrete examples of instructional practices, right? Did I get that right? >> Yeah. So, so two that that are I like to talk about are pretty early ones actually. One is um a nursing faculty member who was never able to give her students practice using a specific kind of interview protocol. This one happened to be for um teenage uh patients who were drug abusers. There's like a specific interview protocol that is recommended for the use of that. She was never able to get she could tell them they could maybe try it once in class. So, she created an AI bot that was a character was a was a a 17-year-old drug abuser that they that they actually now since there's voice, they can actually interview and um it will react the ways that it's been programmed to react and they can practice this protocol over and over and over again and then they get feedback on how they did. Um, and someone in law got super excited about that because that person has been training has never been able to give his students enough practice on the the interview protocols for um determining whether there's domestic violence involved in the situation and um and so now he can give them practice with that before sending them out into the world. So that's one example. Another example I like very concrete. Another example I like to give is fellow who led the project management courses. He was a course director for multiple sections. And project management management has students coming from all over the university from engineering, fashion, design, whatever. And so what he was able to do is rather than choosing one context that might fit a few people in terms of what they cared about in his class, he has AI for a capstone project generate scenarios, data sets, fake email exchanges and stuff like that from the fields that each student is actually interested in. and they have to execute the data or the the project management efforts um that they've learned in class along a shared learning outcome and using a shared rubric, but then it's it's stuff they care about. >> I bet they do. Oh, that's fabulous. Thank you. >> I think >> Yeah. And Mary's got the link to the AI gallery. And it's the the gallery doesn't just have examples of what they did. It has reflections uh from the faculty about kind of what they gained from it or what their insights were through it. And I think that uh that piece of it is is really helpful. And it also has artifacts. But going back to the uh Tiffany Kim's the the nursing example, once it's once that exists, then other people can imagine how that might Oh, wait a minute. My students are getting more practice. >> Not if they're not it's not that I can't give them the opportunity to do 10 interviews. I can't I I can't do all of that. I can I only have time as myself as a faculty to to to do three interviews. Well then then they're getting more of it. So it it jumped kind of from nursing thinking oh this is really interesting the model is in and then the people in the school of law were like wait a minute we we do something like that too. Um that that kind of larger structure of rethinking assessment. >> Oh which is terrific. Uh and that kind of simulation was one of the great superpowers I think of of AI. But thank you Gale. Um we have a question from one of our friends in Scotland uh Wii Demecki who asks this challenging one. Given the different academic disciplines rely on distinct epistemological foundations such as the empirical and algorithm logical computer science versus the interpretive contextbound analysis of the humanities. How effectively can a single overarching AI framework accommodate these various paragological needs without becoming overly generic? >> Furthermore, what domain specific parameters must researchbacked frameworks include to account for how different fields assess knowledge, original thought and cognitive offloading? >> Great question. Yeah. And that's one of the reasons why the deck uh model, digital education model, council model, it has a category for discipline specific. So it doesn't try to be everything to every discipline, but it almost kind of has a placeholder for kind of your your discipline needs to fit to fit here. Um I don't know if that addresses it. it doesn't solve it. But um but if without that placeholder, I think it would be a diminished um product. >> And and I can add to that and I put them into I put the framework for folks that might have missed it earlier into the chat and our own adaptation of that. One of the things that's important when you look at that weaving AI readiness is remembering that this is across this is aimed at across the curriculum and not necessarily in one given course. Um, and this, you know, very similar to how we like in general assessment and even if you're looking at like Danny Lou's two lanes, uh, it's really about kind of across the across the entire curriculum of a program like where are these different things showing up? Not that it shows up in every course. And so I think that's also part of how to address some of the nuances that you're going to find in the individual disciplines and how that like I think it's you can it's like it's a lot of the same it's it's many of similar pieces but they're just going to be arranged differently across a curriculum map. >> Yeah, I can see how that would be that would be distinct. Um >> I think that's one of the reasons why with the AI readiness plans um they this they were the weaving document gave you this general framework and and then what they said is within your colleges you get you get together and you figure out where this needs to be how it needs to be. So, so um the the contextualizing that's not being done. The all the work of that is not on the framework. The work of that is on the people who are figuring out how they're going to use the framework in looking at their at their curriculum and saying kind of what this what this what does this really need need to be what Curry the computer science you know they they're saying well wait a minute you know what is what does this need to be in in our context when oftentimes the AI is the stuff of what we're teaching as opposed to to over in you know Tiffany Kim in nursing very different. >> Well, thank you for the really really good question. Uh I mean this this difference between individual or the spectrum between individual faculty, individual departments, units above departments for a big school like Nor Eastern and the overall um uh framework is is really really important. Um there there are a lot of people volunteering their experience and their work uh in the chat. I just want to give a quick shout out to Janet Balffor who mentions the University of British Columbia has a very active AI student council which works closely with leadership. Excellent. Uh glad to hear that. We have a question from Akia Dixon. Uh two questions but they're closely related. Uh first she says as AI enabled and connected devices become more common in classrooms and learning spaces. Have you seen cases where device access or connectivity issues have disrupted instruction or created equity gaps for students? Related to that, uh how do you handle students who are at different levels of engagement, learning proficiency when it comes to AI? Do how do you gauge that or level set if you have significant gaps? H. >> So I'm I'm guessing the connectivity issue thing is a question about look, I'm requiring you to use this now and then the technology just doesn't behave itself. Yeah. >> Yeah. Or like even networking on campus being an issue >> like you guys are back to campus right now and there's that influx of like help desk tickets and things like that and I don't I don't know but I was just curious if that like has a ripple effect impact on your instruction. Well, I mean we are we have Claude, you know, campuswide and um and the how the token usage that students are allowed um has become an open question um because um we we can't necessarily give all you can eat to everybody and at the same time we have faculty who are asking students to use AI. So what does a student do if they run out of tokens partway through the semester? Um, so that's that's an example of the system not quite yet being set up to make things free and easy. Um, from our side, >> there was a second part question. >> The second part was about um equity and like the baseline that you were all were speaking at about at the top of the conversation. >> Um, say students don't have as much experience coming in like how do you gauge that baseline and how do you like how does that impact instruction? I was just curious about that. >> My guess is that's going to be addressed similarly to any kind of proficiency or skill or knowledge that um that a faculty member needs to differentiate instruction around. I'm not sure that well I I'm not sure that that there's something difference but Lance is making the face he makes when he's got something to say. >> Let's have all of I do not well at I do not do well at poker. Uh so I think that there's there's a couple things. There has been a a course made available for students on learning AI, right? Like many other institutions, whether they take it or not is is always the like up in the air thing. I think we're like because of the ubiquitousness of AI and how quick it happened like we're in a even more compact experience than digital or social media in that one of the hardest things right now is it there is going to probably need to be a lot of deliberateness in each and every course for that base setting because we're we're like four years in and it keeps changing and there's there's an element of like there's some things we have to reset every semester right now because we're still at, you know, like what we say AI could do six months ago. Some of those things have changed or in certain contexts are are less true. And so I think that's that's something that everybody across higher ed is reckoning with. And I don't think there's any good answer other than like it's uncomfortable, but if like if we don't have the conversation, we can't guarantee that they will anywhere. And so that that's one of the things that like I I've certainly I know we're all grappling with and it's it's incredibly important in this in this time. >> Thank you. >> Well, thank you for the great question and uh Aia, thank you for weighing in uh out loud. It's good to hear you and to see you. Uh well, speaking of time, I'm afraid we are out of time. Uh we have raced through this hour uh at top speed with all kinds of knowledge, all kinds of good questions. Um Lance, Michael, Gail, what an absolute pleasure to learn from you and your innovative, impressive, ambitious, and very practical work. Um quick question for the three of you. How do we keep up with what you're all doing? >> Well, >> well, we can keep up with it. We'll let you know. I was about to say >> that's the answer. I didn't say that but I thought it would come come across as >> uh well yeah thank um the you can keep up with it on the LinkedIn of course but also uh learning.norththeastern.edu edu is the website for our center. Um, and within it there's a teaching with AI link that's got a lot of great resources um that will be pointing to all of the stuff that we're developing um wherever it's housed on the university >> transformation site is is kind of in development. This is hot off the presses and it it's sponsored by the chancellor and the provost. So, it it will live outside of uh of the Catler site and more to come. >> Well, um I' I'd love to hear more about that and all of your projects including your AI maker space and uh if someone can toss Lance Eaton's uh Substack link into the chat. Um that would be really good because I recommend that as well. Um and thank you uh thanks to the three of you and thanks to everybody who participated in this conversation. You've shared so many good questions, so many good thoughts. It's wonderful to see so many people working on trying to work through the AI revolution. Uh friends, we have uh sessions scheduled out. I have scheduled our first 2027 session for January. Uh so go to the forum website, forum.futureofeducation US. us if you'd like to learn more about sessions. Of course, we have nearly 500 recordings of previous sessions, including quite a few on AI andor professional development and faculty support. Uh for everybody who is going back to classes, um including my first full class, which starts in about 56 minutes, um good luck everybody. Uh have a great fall semester. Um I your students, your faculty, your staff, your colleagues are all fortunate to have brilliant people like all of you. Please above all everybody take care and be safe. We'll see you next time online. >> Byebye. Thank you so much. >> Thanks Brian. Appreciate it.