Multicampus AI Initiative

We have about 66 faculty participants at this  time, and the majority of them are funded by our IITG funding. But we were able to get some  additional funding at Oneonta and Oswego as well. And Oswego, as Stephanie and John, you can attest,  had a really big and enthusiastic cohort. For those that aren't familiar, can  you describe what an IITG grant is?

The IITG grants are innovative instruction  technology grants. They're awarded across the SUNY system, and they're peer reviewed  through a competitive process that is used to distribute funds, especially  focused on educational technology,

and often OER (open educational resources). We are participating with six other SUNY campuses, and those campuses are Alfred,  Morrisville, Oneonta, Orange, Oswego, which is my campus, and Schenectady. Could you talk a little bit about what the participants in this program have been asked  to do as part of their work in the program. Our project began in January, and we started with  a webinar from Anna Mills, who is an advocate for

AI literacy and also for OER or open educational  resources. She teaches writing at the College of Marin, and we made her webinar available to all  of the participants in our grant. But we also had some interest from SUNY, so we advertised the  webinar through SUNY’s Center for Professional Development, and we had a very large group of  participants attend Anna Mills's talk, which really focused on thinking about AI, how it's  affecting the different assignments that we're

giving in our courses in all sorts of different  ways in higher education. She also spoke about some strategies that teachers should consider  as they are really working to either adapt AI into their courses or sort of try to manage how  AI is affecting the classes that they teach.

And I'll just add that one thing I really love  about Anna is she is focused on critical AI literacy, and that was a big piece of our project,  as we'll talk about as we develop the details, but she has been a voice that is kind of guiding  conversations about evaluative engagement with AI and not only ways to integrate and use  it in the classroom, but also ways to think about how to protect learning objectives now  that these tools are widely available.

So we started our project by listening to  Anna Mills, like I mentioned. It was very well attended, and that was in early January. After  that, each of our campuses had a day-long kickoff event where participants in each of our cohorts  gathered in person on the various campuses, and we led them through a series of structured activities  and introduced them to the grant objectives and deliverables. We also shared with them the  Brightspace shell that we had created specifically

to help guide our cohorts through this process. And I'll add that in the initial meetings, one of the things that was really helpful in  getting everyone there, besides requiring people to be there as part of the process, there was also  lunch available, which made it a little bit nicer, and it created a very nice environment for  people to begin their work together.

It was a very nice way to pull everyone  together on our different campuses, introduce them to one another, and also sort of  see what level of knowledge or engagement our different participants had at the very beginning  of the project. We had participants on our campus who were doing all sorts of different  things. Some had already been integrating AI into their courses in various ways. Others were  experimenting with ChatGPT for the very first

time during that day-long kickoff event. So it  was really interesting and kind of energetic and wonderful to see all of these different people in  a room learn what they were doing and also learn about what they wanted to do and what goals  they wanted to achieve with the project. And I'll also note that some of the faculty  were there mostly to learn how to keep students

from using it as an alternative to learning. Once  they started seeing, over the course of that day, ways in which they could use it and perhaps  have their students use it productively, t here was a pretty significant shift  in their attitude towards AI use over just the course of that very first day,  which has continued over the project.

That's a great point, John, we saw that here  at Oneonta as well, and I think that we have a number of faculty on our campus who are quite  enthusiastic about AI and have been adopting it, and some of the other diversity that we  saw in the room that I thought was really interesting. There were folks who were more  skeptical about AI or more invested in kind of protecting learning objectives, and I did  want to make sure that the experience helped

them to think about those problems and questions,  because those are so pervasive. And then there were faculty who also have been adopting  really enthusiastically in different ways, so creating a space for both of those groups to  kind of learn together and hash out different positions, even while maintaining those different  positions, because I think AI's integration, it really varies across disciplines, and I think  that that richness and diversity was a really

valuable and important part of this project. So beyond that first kickoff day, what were some of the other things that faculty have been  required to do as part of the project? So after the kickoff, we moved on to a series  of monthly meetings that we had planned, and those meetings were designed to continue the  conversation and also to offer pedagogy support

around designing learning activities. So in  addition to thinking about AI integration, we were also offering that pedagogy support  of thinking about backward design, thinking about how to begin with course objectives, how to  design an activity that is active and inclusive for students, and then how to think about feedback  and assessment. So we'll talk a little bit about

the rubric that we use. I mean, we did use it  to assess faculty deliverables, but it was more of a tool to guide the conversations about what  a strong learning activity could look like in a range of contexts. So that rubric kind of guided  our monthly meetings. We were focusing on having an AI policy in your syllabus, developing at least  one course objective related to AI. And then we were focusing on designing the learning activity  and finally offering feedback and assessment on

the learning activity. And we're wrapping up  those monthly meetings in April. Some campuses have already hosted them. Mine is actually this  Friday coming up, and at that April meeting, we were giving folks a chance to share  and give each other feedback on drafts of their assignments and learning activities  before they submit those final drafts. One of the things that we tried really hard to do  in those monthly meetings was to model effective

ways of teaching to everyone who was participating  in our program. So in addition to the things that Racheal mentioned, like backwards design, we also  spend a good chunk of time focusing on the TILT approach. For example, they had an option, they  could do an in class learning activity with AI or they could have their students submit something  in an out of class way. So we spent some time talking about what those assignments could look  like, and why that level of transparency in your

expectations as a teacher matters, and why  that's really important. We also spent time, as Racheal said, talking about feedback and all  of the different ways that we give feedback to our students, whether it just be a grade or written  feedback or verbal feedback, using a rubric or

not, using peer feedback as a successful strategy.  So we really tried, during these monthly meetings and through our Brightspace shell, to really model  what effective teaching looks like, in hopes that our participants would take that and then apply  it to the projects that they were developing. And another important piece that  pairs with that, I think, Stephanie, we also tried to be really responsive to  the different needs and contexts that our

faculty were bringing. If you were planning  an in class learning activity, for example, you're not going to grade that or provide written  feedback, and we certainly weren't expecting that. We wanted to allow for a range of approaches, so  we talked about strategies for giving feedback during class or through conversation. We tried  to ensure that a range of different approaches to learning activities and assignments  were represented in the conversation.

And just following up a little bit on that, one  of the reasons we focused on the TILT framework is because, in the current environment, students  may choose to use AI as an alternative to learning material, and we'd like them to be aware of  why we're asking them to do specific things, because if they understand that they're developing  skills or tools that are going to be useful for them later, it's more likely that they'll  actually engage in it actively themselves,

rather than trying to skip the actual learning  by using AI. So we've encouraged faculty not only to do it in these activities, but also  to integrate the TILT approach into all their teaching and learning activities. For those that aren't familiar, TILT stands for Transparency in Learning and  Teaching. We do have a previous episode on that, that we'll put in the show notes. And we'll share a link both to that

and to Mary-Ann Winklemes’s website, which  describes this as well. And just as one more aside related to this, if you're not sure of  how to create a TILT assignment, you could always feed an assignment into AI and ask it  to put it into a TILT framework. And that works especially well if you also share your course  learning objectives with the tool as well. One of the things that I've heard all  three of you mention is this Brightspace

course that you set up for faculty. Can  you talk a little bit about this? So we spent some time as a leadership  team developing the Brightspace shell. We knew that we wanted a common place for  all of these materials to live, especially the support materials, as our participants were  working their way through our different months, or our different modules. So we worked with  members of the leadership team to develop this

Brightspace shell that could kind of serve as  a common place for us all to come together. In that shell, in addition to uploading our monthly  materials that would focus on a different piece as we moved through that rubric that Racheal was  talking about earlier, we also established clear guidelines for all of our participants so that  they could understand what their requirements were as they moved through the program, which  we'll talk more about in a little bit. But that

Brightspace shell also offered opportunities for  our participants to talk to one another. One of the things that we required our participants  to do was engage with one another in what we called our community of practice discussions. So  our participants were required to engage with a small group of their colleagues from different  campuses. This was a little bit interesting, because Oswego and Oneonta had larger cohorts  than some of our other participating campuses.

At Oswego, we were lucky enough to receive some  additional funding and support from our Chief Technology Officer, Sean Moriarty, which we really  appreciated. And I know that Oneonta had some extra funding as well. Is that right, Rachael? That’s right. We had initially requested funding for 10 faculty to receive stipends of $600 each,  but our Faculty Center for Teaching, Learning, and Scholarship was able to provide additional  stipends for, I think for us, it was six more

members to participate in the program. And I think  for you, you were able to double that number. So, Oswego ended up with 24 participants total,  which was about 14 more than what we had initially planned for. So in the community of practice  discussions, it was a little bit heavier with membership from Oswego and Oneonta, but we were  still able to design these discussions based

off of what our participants requested. So some  wanted the opportunity to speak with people from other campuses who were in similar disciplines as  themselves, and some wanted to be in groups with people who were from totally different disciplines  to sort of learn what other people were doing. I think we're bringing out here the two tracks  that ran parallel throughout the course of this

experience. On the one hand, we had our in-person  cohort meetings on each of the six campuses, and that allowed people to connect across departments  from a range of disciplines with folks on their own campus. And then we had this second element  that Stephanie has been talking about, that was the cross-campus community of practice element,  and those were virtual meetings with three or four faculty members per group. And we asked  those cross-campus groups to meet for a minimum

of two times and then to post their takeaways to  the discussion area in Brightspace. That way we could track and see what our faculty were talking  about. We could take a look and answer questions that might have come up. And we also, as a  leadership team, responded to those discussions on

Brightspace, and we really saw the groups talking  about a range of topics. One of the reasons that we wanted to connect faculty across campuses and  not just run these cohorts on individual campuses, is that we're seeing faculty bring a lot of  different approaches and attitudes to AI,

and a lot of that is place specific. A lot of it  is related to institutional cultures or resources, and so the ability for faculty to meet from  across the system and to share what their institutions are doing to support professional  development related to AI, as well as to share what different departments or what conversations  around AI might be happening in those different spaces. We wanted to create space for people to  come together and have those conversations.

It was also really interesting, because  participants in the cross-campus groups, in addition to having these other  conversations about AI and pedagogy, began to really work well together as a unit as  they worked on the deliverables for this project. So they worked together to share their ideas for  their learning activity, their learning objective, their syllabus statement. And then as we got  deeper into the project, they started to share

drafts with one another and to ask each other,  is this meeting the criteria for the grant? Are we making a learning activity that's critical  and active and inclusive? Do you have feedback for me? So as we talked about earlier, we had the  meetings on each of our individual campuses with our campus groups, but it was really nice that the  cross-campus groups were engaging with one another beyond what we had initially imagined, which I  thought was really a nice bonus from this.

I completely agree, and to try to facilitate  some of that conversation as a leadership team, we didn't want to impose structure on these  meetings, but we also wanted to provide some resources for getting those conversations  started. So we created on Brightspace question banks that faculty could choose to draw from  to guide those conversations. And so with those

suggested structures, I think we were able to  leave room for that creative exchange. And each group was able to kind of determine how they  wanted to use that cross-campus group time. We also had, in addition to the cross-campus  group discussion boards, we also made additional discussion boards that were optional,  so participants could choose whether or not they wanted to participate in a board called  community conversation, which was very informal,

just a way for people to share their thoughts  or ask questions to one another. And again, this was not required, but we did have  participants posting there, engaging with one another in that way. Then we also had another  discussion board that we called news and tools, which was basically the opportunity for  participants to share new tools that they had stumbled across, or post things related to things  that were happening as new AI tools were developed

and came out. As we know they're changing so  rapidly. Things are coming out every single day, so participants had the opportunity to share  that information with each other as well, and I think it was nice to give them the opportunity to  participate in more than just one forum, because, as Racheal said, we had several members  who were very engaged in the project, and I think valued other spaces to collaborate. What are some of the examples of learning

activities under development by faculty? So, materials for this project are not due for another two weeks or so, but we have had several  participants submit their materials already, getting things all squared away before the madness  of final exams week here. So we did have a few faculty who were thinking about engaging with  AI tools as part of the brainstorming process.

So for example, one of our participants here  gave an assignment where students were asked to use Perplexity, which is a free AI tool that also  can access the web and provide suggested sources. He's asking his students to use Perplexity to help  them continue brainstorming for an assignment that they're working on in the class. So students  already have some general ideas of what they

want to do, and he's asking them to engage with  Perplexity to deepen their brainstorming. So for example, ask Perplexity to provide some suggested  sources to the topic that they're considering, and then evaluate the sources that Perplexity  is suggesting. He also asks them to prompt Perplexity, to give them a counter argument  to what they're exploring. So those are some examples. T hat would be an example of a learning  activity. It's something that he's going to have

them do in class. And the nice thing about his  prompt is that he gave his students four different sample prompts that they could take and then put  into the tool, but they would have to fill out the details of their particular brainstorming.  So that's one example from our campus. Racheal, would you like to give one example from  yours, and then I can do another one?

Absolutely, that sounds great.  I'll share an example from Oneonta, while saying as well that the leadership  team will be taking a look at the examples across campus once folks are uploading them, as  Stephanie has said, but we haven't done that yet, so we only have the knowledge of folks we've been  working with on our own campuses. So one faculty member in sociology here has been developing a  project that I think is really interesting and

exciting. He's going to be teaching a course in  the fall that has a unit focused on conspiracy theories and thinking specifically about how  to debunk or counter conspiracy theories. And so he has identified an AI chatbot called  DebunkBot, which has been developed, I think, out of Cornell. And he's going to ask students  to identify a conspiracy theory of their choice, engage with DebunkBot to see how the AI counters  that conspiracy theory with factual information,

and then they're going to evaluate those AI  outputs. And I think within the conversations he's having with students, too, is the question  of, do facts work against conspiracy theories? There are various opinions on that, so not only  evaluating DebunkBot’s outputs, but also thinking about it as a tool to counter conspiracy theories.  What are its strengths and weaknesses? And what I really like about this project is, I think that it  exemplifies that critical element that we've been

emphasizing through this project. By critical, we  really mean evaluative. We are asking that all of these learning activities and assignments take up  that AI literacy task of helping students evaluate AI outputs and evaluate AI as software. Tagging on to the evaluative piece, one of our colleagues here is working on an assignment that  asks students to sort of reverse write a paper.

So she's giving her students a prompt and asking  an AI tool of their choice, probably ChatGPT I imagine… one of the components of our grant that  we really emphasized was trying to make these assignments or learning activities as inclusive  as possible. So we're thinking about access, who has access to this technology. And since ChatGPT  doesn't require users to make an account, there

is a free version, a lot of faculty are leaning  toward those sorts of tools for this. So anyway, one of our faculty here is asking her students to  have ChatGPT generate an essay on one of the books that they're reading for the course, and students  are then going to look at the essay and evaluate the output that ChatGPT has created. So they're  going to look at things like whether or not the

material is accurate, if there are hallucinations,  if it is in fact doing what it was asked to do. So I think they're being asked to analyze, “So how is  the quality of ChatGPT analysis?” And this faculty member also has a series of follow up prompting  questions to have the students continue to engage with this essay that was generated and see if they  can make it better, or see if that's it, if it's

as good as it's going to get, and if there are  still holes in the analysis or with the argument. As a writing teacher, I thought that that was a  really cool way to look at what this technology can do, really consider its limitations as well,  and think about how are we going to do writing, teach writing as we keep going, as more  students rely on these sorts of tools. I love that you were emphasizing the inclusivity  piece there, Stephanie, which has come up in our

conversation where you were mentioning especially  how you emphasize TILT on Oswego’s campus. On Oneonta’s campus, we talked a little bit  about UDL, universal design for learning, but those were frameworks that we wanted to keep  in the mix as part of the conversation, we didn't necessarily have time in our progression to really  dive deeper focus. So we set some basic guidelines for inclusive activities that we encouraged  everyone to meet. And those were, as you said,

making sure that whatever AI tools you're asking  students to use they have equal access to. So that would look different on different campuses as  well. Here at Oneonta, we're a Microsoft campus,

and that means that we have Co-Pilot for all  students over 18. And if you're thinking about, when you're introducing students to AI tools,  not only making sure that everyone can access it, but also making sure that you're thinking  and talking about who might have access to paid versions, because students are sometimes  subscribing to ChatGPT at that higher level,

and just making those conversations about what  tools you're using part of the conversation. The other inclusive piece that we really emphasized  was just that providing instructions to make sure that you're aware that people… students,  specifically… are bringing different levels of knowledge of technology to the classroom. Some  students are using AI all the time, others have

never used it and don't want to. I think we've had  those conversations in our group. So just being aware of the diversity of student capabilities  as part of that inclusivity and accessibility

piece was really important to us too. Just to kind of tag on, I thought it was really interesting that, as we talk about access and  who has access to a paid tool versus a free tool, I'm sure you have seen, I know John and I  have spoken about this a little bit that, right now ChatGPT has a free trial of their paid  version that they're marketing specifically to students. So it's ongoing from now until the end  of May, which I thought was really interesting.

And I know that they're not the only tool  that has done that. Is that, right, John? Gemini and Claude have also done it, and one of  them I forgot which one, actually is extending it until next year. So there's a little bit of  competition goizg on to get more students using their particular models, because student use does  make up a remarkably large share of the use of all the AI tools now, for reasons that perhaps  are not always as positive as we'd like.

I think it will be really interesting to see how  that continues to develop as we move forward. They're doing this two month free trial.  Now. What does that mean for the beginning of the fall semester and beyond that? One thing we should also note is that this podcast is coming out in June.  We're recording this in late April, which is why snow was as recent as last week, and  why many of the references are towards the last

stages of the semester. This is a rare occasion  for us to be this far ahead in our recording. We often record things a week or two before their  released, but we've happened to be able to get

a lot of podcasts scheduled within a fairly short  period of time. Along the lines of the assignment that Racheal mentioned in evaluating sources, Mike  Caulfield has been developing a prompt for SIFT, which is his approach for analyzing the  veracity of online claims, and as of last night, he's developed an expanded version of that  prompt, and we'll share a link to that in the show notes as well, because we can use AI tools  to try, not only to create false information,

we can also use it to try to verify claims. One of the things I've heard you all talk about quite a bit, and I'd like to hear a little  bit more about, is the rubric that you were using to evaluate the assignments and  things that faculty were creating. Yeah, the rubric that we developed, as I mentioned  earlier, we really thought of this more as a tool

to guide our conversations, rather than as an  assessment piece. But we've run at SUNY Oneonta, similar cohorts devoted to different learning  activities and aims in the past, and we knew from those experiences that being really clear about  what the deliverables are and what elements we're privileging and would like to see, that's going  to help faculty design learning activities that then can move as we'd like to move them, to our  public facing repository. So we focused on three

categories of evaluation on the rubric. First,  we were asking folks to develop an AI policy for their syllabus. That was a big part of this  project, too. As we are thinking about helping faculty and students navigate the emergence of AI,  students are coming into a range of classes that all have different expectations for them. So some  faculty might be saying, “if you use AI at all, you are cheating.” Others might say, “I want you  to use AI. You have to use it. And here are some

of the ways that I'll be evaluating your use of  it.” So just thinking about how we as a leadership team could support students by encouraging AI  policies through this experience was important to us, and those policies would capture a wide  variety of approaches to AI in the classroom. Some policies that we've seen have given  guidelines assignment to assignment, where some

assignments require AI use, some ask students not  to use it. We've seen policies that outline the specific ways students might use AI throughout  the semester, and we've also seen policies that said you can't use AI except in these very  specific ways that I ask you to on this learning activity. So we've really seen a range there. And  the rubric begins with that AI policy element and

with the course objective. So one of the elements  that we're collecting as part of the deliverable with the learning activity is the syllabus  with an AI policy and a learning objective. And I think it's important to emphasize as well  that regardless of how faculty feel about AI, like whether they're embracing it or they're  saying, “I really would prefer you not use AI in this course.” It's really important for us  to be transparent with our students about what

the rules are. As Racheal said, our students  are generally taking five different courses or more with five different faculty who have a  whole bunch of different feelings about this. So some people might think I don't want them to  use AI in my course, so I'm just going to not put anything on my syllabus. It's so important  for our students that we are transparent, regardless of how we feel about it. Yes, and that was one of our aims with

including that syllabus element. So  the second rubric element focused on the learning activity itself. And again, that  had the critical, active, and inclusive piece. I think we've done a good job talking about how we  define critical as evaluative, and how we define inclusive as tool access and instructions. And I  guess I can just briefly define active as students are using the tools themselves. So we're not just  asking you to demo AI in a lecture. We're saying,

“Get students in there evaluating outputs for that  AI literacy piece.” And I do think it's important to mention here that we had many conversations  about students who want to opt out of AI tool use, students who might have environmental reasons  for not wanting to use the tools or other ethical reasons, and we did talk about strategies for  that, including alternatives that would allow students to maybe engage with an output that  the instructor created from AI or alternative

assignments that aren't actually asking them  to engage with AI, but might be asking them to evaluate AI in a broader conceptual way. So those  alternatives did come up for us. And Stephanie, maybe you could talk about that last rubric  piece, the assessment and feedback element. Sure. So we thought that it was important for our  faculty, as they are designing this assignment, to

really think about how this assignment or learning  activity would be assessed. How are they going to give feedback to their students as they experiment  with these tools, as they evaluate these tools, and what does that assessment piece mean? So  we have some participants who are just doing

one assignment or one learning activity, so they  have to just think about that one piece. But we've had several participants in this project who have  become inspired by the one part and have decided to make lots of changes to their assignments or  their learning activities, and then think about what assessment means for them and for their  students. So as we talked about earlier, the feedback piece is very important, as our students  are critically and actively engaging with these

tools, because we have to guide them through what  they are learning. If we are really focusing on AI literacy and helping our students understand or  begin to understand how these tools might impact, not only the work they're doing in the classroom,  but their future jobs. Every career is going to look a little bit different. Some are really  going to be embracing these tools and expecting our students to understand how to use them, where  others perhaps not so much. An example, I teach

creative writing courses. Right now. In those  courses, I am not emphasizing AI use because, for many of my students, they're looking  to submit their work to different journals, and most creative journals at this time are not  accepting AI-generated work. So it's important to acknowledge that, and it's also important  to focus on why we're going to continue to do

things the way that we've done them a little  bit for the past, right? But other courses, if I'm teaching English composition, and I'm  teaching them different ways that they can outline or draft or brainstorm, how they can use AI to get  feedback on their own work and their own writing. Those are important skills that will come in handy  for them later on, as we talk about things like efficiency and making more polished drafts and  getting feedback before you turn something in.

Those skills will come in handy more so for those  students than for some of the creative writers. We have a student advisory board for our teaching  center, and they met last week, and one of the things that came up was the topic of AI, and two  concerns that students discussed pretty actively was that a lot of faculty have not made clear yet  what their expectations are about whether AI use is allowed, when it is allowed, and so forth, and  they noted that it was more rare than the norm for

people to actually have made those policies. We've  obviously been encouraging faculty to do that for a couple of years now, but it hasn't made it out  to all of our classes yet. But our institution will be requiring an AI policy statement in the  syllabi beginning next fall. So this transparency

issue and being clear about your expectations  is really important. The other thing they were concerned about is being falsely accused of AI  use, and this is something that is very concerning for students, and we have a lot of cases where  some faculty are just completely ignoring AI, and others are assuming that AI is being used by  all students all the time, and accusing students of using it without any real proof in many cases,  and it's a somewhat troubling environment.

Yeah, I think that's such a good point, John,  about the range of approaches that students are taking to AI, as well as the different assumptions  that faculty members are bringing. And I think that there's sort of a spectrum of assumptions,  where, on the one hand, you have the idea that AI should be completely left out of the classroom and  any use of it is cheating. On the other end of the spectrum you have we should embrace AI and use it  for everything in the classroom, and if we're not

teaching that, we're being irresponsible. So we  are seeing very polarized views on our campuses, and I think that a professional development  opportunity like the one we've been running needs to respect and honor and speak to  both of those positions, while also making

the conversation a little bit more nuanced. And I  think one of the ways that we've done that is to show that bringing AI into the classroom actually  addresses the problem of students using it for cheating and plagiarism, because students are:  A) aware that the faculty member knows about and is actively engaged with those tools, so they  might be less likely to just copy and paste,

which is the use that most people do regard  as cheating or plagiarism. And in addition, it not only shows that faculty members are  engaged with and knowledgeable about these tools, but in my experience, students are really hungry  for guidance from faculty on how to engage with them in an ethical and informed way. So we're  helping them build those AI literacy skills by

confronting and talking about these tools in our  classes. And to me, that might lead to them using the tools more in specific ways that we're guiding  them to, but it can also lead to them using them

less in situations where we want to protect  those learning objectives. Like Stephanie, I teach in the writing classroom, and I have  been devoting this semester to engaging with and thinking about AI, but that has meant we have  plenty of writing and activities where I'm asking them not to use it, and then we have activities  where I'm asking them to use it in specific,

guided, critical ways. And so I think that that  more nuanced approach than we need to totally keep it out or we need to totally embrace it, that's  the area that a lot of our faculty are working in,

and I think it serves our students really well. And Stephanie and I have also, in a series of workshops here, encouraged faculty to co-develop  their AI policy statements with students, so that students are involved in that  conversation, and if they help develop the guidelines for the class, they're much more  likely to abide by them and to buy into them. To piggyback off of that as well, in our  cohort, especially, I mentioned earlier

that we had a range of experience levels with  AI tools. And what's been really interesting is that for participants who are trying these  tools for the first time, they're having some important realizations about what these tools  are or are not capable of, and I think that's really important. If we are teaching and we're  saying, here is my AI policy, and here's why, if we are familiar with what the AI tools can do,  I personally feel that our students are going to

be more receptive to that. If we are making  assumptions about what the AI tools can do, or how well they can complete a task, but we don't  really know, then how are we going to recognize an AI's work when we come across it? So for example,  this is a dated example, but I think it works still really well, in a writing classroom, for a  little while after ChatGPT came out, the solution to avoiding AI-generated work was to ask students  to write reflectively, because they'll be writing

from their own personal experience, and an AI tool  can't replicate that. And I remember leading a workshop, and I showed an example of a reflective  essay that was generated by AI, and this was like two years ago. All I had to do was prompt it  a little bit. I asked it in a revision of the reflection to include a sad story about how the  tool had a childhood dog that they really loved, and that relationship with their dog is the reason  that they wanted to pursue veterinary studies. And

it generated this beautiful reflective essay that  you would have never known from anything else. As I continued to prompt it, I asked it to include  volunteer activity, and it made up a name of an animal hospital… at the time that didn't exist,  but now it would, because AI tools have gotten so much better. So it's just really interesting  as we think of what the limitations are, and how there are not as many limitations in so many  different fields. That is, of course, not the case

for all fields, but the tools continue to evolve  so quickly. So I think it's important for us to recognize what those limitations and capabilities  are, and then if we know, I feel that our students are more likely to trust our judgment, and then  believe us when we say, “Yes, perhaps you could use AI for this, but let's not because I want  you to develop this skill that will help you as you continue taking more classes here, or as  you graduate and start looking for a job.”

We've talked a bit throughout the conversation  about how faculty have engaged with the program over the course of the year so far, in creating  assignments and in the discussion boards, et cetera. Is there any additional things that  you want to share about how faculty have responded to participating in this program. I think that some of the feedback we've received is that it's been really nice to have  institutional support to help faculty do work

that they felt the need to do in their classes.  Faculty want to respond to AI's emergence, and oftentimes we know faculty are overworked.  There's so much to do, and so being able to get that institutional recognition through a stipend  to say “this is important, and we're offering support for developing these activities.” We've  heard faculty report that they're happy that the opportunity is available to do things that  they've already wanted to be doing, but here

they can see the institution supporting that. I agree, and it's also been very helpful as we have these conversations with our participants to  think about effective practices on the whole with AI. So for example, there has been conversation  everywhere about using AI tools to grade student work and to assess student work. So we've  had opportunities to have those sorts of conversations about why, perhaps, it's not in  our best interest as teachers to use AI to give

feedback to student writing, for example. And it's  been nice to hear what other people are doing, like Racheal said, with that support  aspect, but also the conversations allow us to sort of come across those topics  that might just be neglected otherwise. And I'll just jump in, Stephanie, because your  comments about using AI to assess and grade has also been an important part of our conversations  at Oneonta. And again, we're seeing really

polarized views, where some people are saying,  “I would never do that.” Some people are saying, “I'm already doing that in these specific ways.”  And we've talked there, for example, about the

Student AI Bill of Rights, which circulates  online. And one of the elements of that AI bill of rights for students is that students should  have the right to know when faculty are using AI for feedback or other forms of engagement, and  so I agree that this has been a really great place to have those conversations which might be  taking place informally or in departments, but in this interdisciplinary setting, people are able to  talk about those issues and share their views.

So we always end by asking, and this is something  particularly appropriate with AI, what's next? As we're wrapping up this project, we are looking  to move the learning activities that our faculty will be submitting to Brightspace into an open  access repository that my colleague Ed Beck here at SUNY Oneonta is helping to develop, not only  for our grant, but for another IITG-funded project

out of Albany that is generating some K-12  resources. So we are creating that repository space via SUNY Create and after we collect our  faculty learning activities on Brightspace,

we'll be guiding them to move revised versions  of those activities into the repository. So we also have our presentation at CIT, which is the  Conference on Instructional Technology that SUNY convenes every year across the system, we  will be presenting at CIT on this project, and by that time, we will have more information on  the website and how to access it, so that faculty, not only across SUNY, but across the internet,  will be able to view the learning activities

that we've been talking about today. And we should mention that SUNY Create is basically an instance of a system-wide license  for Reclaim Hosting’s domain of your own. And after CIT, we're hoping to extend this work,  maybe in new ways. We're thinking about ways to

bring campuses together for communities of  practice. And one thing that's been really important to me about this project has been the  value of working with colleagues in educational development, whether we're talking about  faculty or staff at all of our six campuses, all of whom have brought different skills,  knowledge, expertise, passion to this project. We've had leadership team members who work in the  social sciences and have brought that qualitative

research and assessment knowledge to the project.  We have created pre- and post-assessment surveys that are helping us collect data on the success  of the project, and we couldn't have done that without Deepa Deshpande, who's at SUNY Alfred.  We also have leadership team members who are bringing expertise in the classroom, that  includes Stephanie, John, and Laura Pierie, who is with us from Morrisville, and having that  faculty insight from a range of disciplines has

really grounded the project in the needs of our  participants. We've also had Dana Salkowski and David Wolf join us from community colleges, and  having their input as directors and leaders in teaching and learning has been really crucial to  helping us think about how our project could serve faculty at all of our different campuses that  we've been working with. So I think that it's been a really amazing group effort, and I'm so  grateful for the leadership work of that entire

team. I think together, we've been able to really  support faculty in some of this work, and I'm excited to think about the collaborations that we  might be able to pursue going into next year. Well, thank you so much for joining us. It's been  a great conversation, and it's a great project to share with others that they might want to  implement something similar on their campuses. Thank you. It's been great working  with both of you and I’m looking

forward to future collaborations. It's been wonderful chatting with you all. Thank you for having us, and it's been  wonderful talking with you today. If you've enjoyed this podcast, please  subscribe and leave a review on iTunes or your favorite podcast service. To  continue the conversation, join us on our Tea for Teaching Facebook page. You can find show notes, transcripts and other materials on teaforteaching.com.  Music by Michael Gary Brewer.