Tag: Teaching

A growing share of colleges and universities are embedding artificial intelligence tools and AI literacy into the curriculum with the intent of aiding student success. A 2025 Inside Higher Ed survey of college provosts found that nearly 30 percent of respondents have reviewed curriculum to ensure that it will prepare students for AI in the workplace, and an additional 63 percent say they have plans to review curriculum for this purpose.

Touro University in New York is one institution that’s incentivizing faculty to engage with AI tools, including embedding simulations into academic programs.

In the latest episode of Voices of Student Success, host Ashley Mowreader speaks with Shlomo Argamon, associate provost for artificial intelligence at Touro, to discuss the university policy for AI in the classroom, the need for faculty and staff development around AI, and the risks of gamification of education.

An edited version of the podcast appears below.

Q: How are you all at Touro thinking about AI? Where is AI integrated into your campus?

A: When we talk about the campus of Touro, we actually have 18 or 19 different campuses around the country and a couple even internationally. So we’re a very large and very diverse organization, which does affect how we think about AI and how we think about issues of the governance and development of our programs.

That said, we think about AI primarily as a new kind of interactive technology, which is best seen as assistive to human endeavors. We want to teach our students both how to use AI effectively in what they do, how to understand and properly mitigate and deal with the risks of using AI improperly, but above all, to always think about AI in a human context.

When we think about integrating AI for projects, initiatives, organizations, what have you, we need to first think about the human processes that are going to be supported by AI and then how AI can best support those processes while mitigating the inevitable risks. That’s really our guiding philosophy, and that’s true in all the ways we’re teaching students about AI, whether we’re teaching students specifically, deeply technical [subjects], preparing them for AI-centric careers or preparing them to use AI in whatever other careers they may pursue.

Q: When it comes to teaching about AI, what is the commitment you all make to students? Is it something you see as a competency that all students need to gain or something that is decided by the faculty?

A: We are implementing a combination—a top-down and a bottom-up approach.

One thing that is very clear is that every discipline, and in fact, every course and faculty member, will have different needs and different constraints, as well as competencies around AI that are relevant to that particular field, to that particular topic. We also believe there’s nobody that knows the right way to teach about AI, or to implement AI, or to develop AI competencies in your students.

We need to encourage and incentivize all our faculty to be as creative as possible in thinking about the right ways to teach their students about AI, how to use it, how not to use it, etc.

So No. 1 is, we’re encouraging all of our faculty at all levels to be thinking and developing their own ideas about how to do this. That said, we also believe very firmly that all students, all of our graduates, need to have certain fundamental competencies in the area of AI. And the way that we’re doing this is by integrating AI throughout our general education curriculum for undergraduates.

Ultimately, we believe that most, if not all, of our general education courses will include some sort of module about AI, teaching students specifically about the AI-relevant competencies that are relevant to those particular topics that they’re learning, whether it’s writing, reading skills, presentations, math, science, history, the different kinds of cognition and skills that you learn in different fields. What are the AI competencies that are relevant to that, and to have them learning that.

So No. 1, they’re learning it not all at once. And also, very importantly, it’s not isolated from the topics, from the disciplines that they’re learning, but it’s integrated within them so that they see it as … part of writing is knowing how to use AI in writing and also knowing how not to. Part of learning history is knowing how to use AI for historical research and reasoning and knowing how not to use it, etc. So we’re integrating that within our general education curriculum.

Beyond that, we also have specific courses in various AI skills, both at the undergraduate [and] at the graduate level, many of which are designed for nontechnical students to help them learn the skills that they need.

Q: Because Touro is such a large university and it’s got graduate programs, online programs, undergraduate programs, I was really surprised that there is an institutional AI policy.

A lot of colleges and universities have really grappled with, how do we institutionalize our approach to AI? And some leaders have kind of opted out of the conversation and said, “We’re going to leave it to the faculty.” I wonder if we could talk about the AI policy development and what role you played in that process, and how that’s the overarching, guiding vision when it comes to thinking about students using and engaging with AI?

A: That’s a question that we have struggled with, as all academic leaders, as you mentioned, struggle with this very question.

Our approach is to create policy at the institutional level that provides only the necessary guardrails and guidance that then enables each of our schools, departments and individual faculty members to implement the correct solutions for them in their particular areas, within this guidance and these guardrails so that it’s done safely and so that we know that it’s going, over all, in a positive and also institutionally consistent direction to some extent.

In addition, one of the main functions of my office is to provide support to the schools, departments and especially the faculty members to make this transition and to develop what they need.

It’s an enormous burden on faculty members to shift, not just to add AI content to their classes, if they do so, but to shift the way that we teach, the way that we do assessments. The way that we relate to our students, even, has to shift, to change, and it creates a burden on them.

It’s a process to develop resources, to develop ways of doing this. I and the people that work in our office, we have regular office hours to talk to faculty, to work with them. One of the most important things that we do, and we spend a lot of time and effort on this, is training for our faculty, for our staff on AI, on using AI, on teaching about AI, on the risks of AI, on mitigating those risks, how to think about AI—all of these things. It all comes down to making sure that our faculty and staff, they are the university, and they’re the ones who are going to make all of this a success, and it’s up to us to give them the tools that they need to make this a success.

I would say that while in many questions, there are no right or wrong answers, there are different perspectives and different opinions. I think that there is one right answer to “What does a university need to do institutionally to ensure success at dealing with the challenge of AI?” It’s to support and train the faculty and staff, who are the ones who are going to make whatever the university does a success or a failure.

Q: Speaking of faculty, there was a university faculty innovation grant program that sponsored faculty to take on projects using AI in the classroom. Can you talk a little bit about that and how that’s been working on campus?

A: We have an external donor who donated funds so that we were able to award nearly 100 faculty innovation challenge grants for developing methods of integrating AI into teaching.

Faculty members applied and did development work over the summer, and they’re now implementing in their fall courses right now. We’re right now going through the initial set of faculty reports on their projects, and we have projects from all over the university in all different disciplines and many different approaches to looking at how to use AI.

At the beginning of next spring, we’re going to have a conference workshop to bring everybody together so we can share all of the different ways that people try to do this. Some experiments, I’m sure, will not have worked, but that’s also incredibly important information, because what we’re seeking to do [is], we’re seeking to help our students, but we’re also seeking to learn what works, what doesn’t work and how to move forward.

Again, this goes back to our philosophy that we want to unleash the expertise, intelligence, creativity of our faculty—not top down to say, “We have an AI initiatives. This is what you need to be doing”—but, instead, “Here’s something new. We’ll give you the tools, we’ll give you the support. We’ll give you the funding to make something happen, make interesting things happen, make good things for your students happen, and then let’s talk about it and see how it worked, and keep learning and keep growing.”

Q: I was looking at the list of faculty innovation grants, and I saw that there were a few other simulations. There was one for educators helping with classroom simulations. There was one with patient interactions for medical training. It seems like there’s a lot of different AI simulations happening in different courses. I wonder if we can talk about the use of AI for experiential learning and why that’s such a benefit to students.

A: Ever since there’s been education, there’s been this kind of distinction between book learning and real-world learning, experiential learning and so forth. There have always been those who have questioned the value of a college education because you’re just learning what’s in the books and you don’t really know how things really work, and that criticism has some validity.

But what we’re trying to do and what AI allows us to do [is], it allows us and our students to have more and more varied experiences of the kinds of things they’re trying to learn and to practice what they’re doing, and then to get feedback on a much broader level than we could do before. Certainly, whenever you had a course in say, public speaking, students would get up, do some public speaking, get feedback and proceed. Now with AI, students can practice in their dorm rooms over and over and over again and get direct feedback; that feedback and those experiences can be made available then to the faculty member, who can then give the students more direct and more human or concentrated or expert feedback on their performance based on this, and it just scales.

In the medical field, this is where it’s hugely, hugely important. There’s a long-standing institution in medical education called the standardized patient. Traditionally it’s a human actor who learns to act as a patient, and they’re given the profile of what disorders they’re supposed to have and how they’re supposed to act, and then students can practice, whether they’re diagnostic skills, whether they’re questions of student care and bedside manner, and then get expert feedback.

We now have, to a large extent, AI systems that can do this, whether it’s interactive in a text-based simulation, voice-based simulation. We also have robotic mannequins that the students can work with that are AI-powered with AI doing conversation. Then they can be doing physical exams on the mannequins that are simulating different kinds of conditions, and again, this gives the possibility of really just scaling up this kind of experiential learning. Another kind of AI that has been found useful in a number of our programs, particularly in our business program, are AI systems that watch people give presentations and can give you real-time feedback, and that works quite well.

Q: These are interesting initiatives, because it cuts out the middleman of needing a third party or maybe a peer to help the student practice the experience. But in some ways, does it gamify it too much? Is it too much like video games for students? How have you found that these are realistic enough to prepare students?

A: That is indeed a risk, and one that we need to watch. As in nearly everything that we’re doing, there are risks that need to be managed and cannot be solved. We need to be constantly alert and watching for these risks and ensuring that we don’t overstep one boundary or another.

When you talk about the gamification, or the video game nature of this, the artificial nature of it, there are really two pieces to it. One piece is the fact that there is no mannequin that exists, at least today, that can really simulate what it’s like to examine a human being and how the human being might react.

AI chatbots, as good as they are, will not now and in the near, foreseeable future, at least, be able to simulate human interactions quite accurately. So there’s always going to be a gap. What we need to do, as with other kinds of education, you read a book, the book is not going to be perfect. Your understanding of the book is not going to be perfect. There has to be an iterative process of learning. We have to have more realistic simulations, different kinds of simulations, so the students can, in a sense, mentally triangulate their different experiences to learn to do things better. That’s one piece of it.

The other piece, when you say gamification, there’s the risk that it turns into “I’m trying to do something to stimulate getting the reward or the response here or there.” And there’s a small but, I think, growing research literature on gamification of education, where if you gamify a little bit too much, it becomes more like a slot machine, and you’re learning to maneuver the machine to give you the dopamine hits or whatever, rather than really learning the content of what you’re doing. The only solution to that is for us to always be aware of what we’re doing and how it’s affecting our students and to adjust what we’re doing to avoid this risk.

This goes back to one of the key points: Our whole philosophy of this is to always look at the technology and the tools, whether AI or anything else, as embedded within a larger human context. The key here is understanding when we implement some educational experience for students, whether it involves AI or technology or not, it’s always creating incentives for the students to behave in a certain way. What are those incentives, and are those incentives aligned with the educational objectives that we have for the students? That’s the question that we always need to be asking ourselves and also observing, because with AI, we don’t entirely know what those incentives are until we see what happens. So we’re constantly learning and trying to figure this out as we go.

If I could just comment on that peer-to-peer simulation: Medical students poking each other or social work students interviewing each other for a social work kind of exam has another important learning component, because the student that is being operated upon is learning what it’s like to be in the other shoes, what it’s like to be the patient, what it’s like to be the object of investigation by the professional. And empathy is an incredibly important thing, and understanding what it’s like for them helps the students to learn, if done properly, to do it better and to have the appropriate sort of relationship with their patients.

Q: You also mentioned these simulations give the faculty insight into how the student is performing. I wonder if we can talk about that; how is that real-time feedback helpful, not only for the student but for the professor?

A: Now, one thing that needs to be said is that it’s very difficult, often, to understand where all of your students are in the learning process, what specifically they need. We can be deluged by data, if we so choose, that may confuse more than enlighten.

That said, the data that come out of these systems can definitely be quite useful. One example is there are some writing assistance programs, Grammarly and their ilk, that can provide the exact provenance of writing assignments to the faculty, so it can show the faculty exactly how something was composed. Which parts did they write first? Which parts did they write second? Maybe they outlined it, then they revised this and they changed this, and then they cut and pasted it from somewhere else and then edited.

All of those kinds of things that gives the faculty member much more detailed information about the student’s process, which can enable the faculty to give the students much more precise and useful feedback on their own learning. What do they perhaps need to be doing differently? What are they doing well? And so forth. Because then you’re not just looking at a final paper or even at a couple of drafts and trying to infer what the student was doing so that you can give them feedback, but you can actually see that more or less in real time.

That’s the sort of thing where the data can be very useful. And again, I apologize if I sound like a broken record. It all goes back to the human aspect of this, and to use data that helps the faculty member to see the individual student with their own individual ways of thinking, ways of behaving, ways of incorporating knowledge, to be able to relate to them more as an individual.

Briefly and parenthetically, one of the great hopes that we have for integrating AI into the educational process is that AI can help to take away many of the bureaucratic and other burdens that faculty are burdened with, and free them and enable them in different ways to enhance their human relationship with their students, so that we can get back to the core of education. Which really, I believe, is the transfer of knowledge and understanding through a human relationship between teacher and student.

It’s not what might be termed the “jug metaphor” for education, where I, the faculty member, have a jug full of knowledge, and I’m going to pour it into your brain, but rather, I’m going to develop a relationship with you, and through this relationship, you are going to be transformed, in some sense.

Q: This could be a whole other podcast topic, but I want to touch on this briefly. There is a risk sometimes when students are using AI-powered tools and faculty are using AI-powered tools that it is the AI engaging with itself and not necessarily the faculty with the students. When you talk about allowing AI to lift administrative burdens or ensure that faculty can connect with students, how can we make sure that it’s not robot to robot but really person to person?

A: That’s a huge and a very important topic, and one which I wish that I had a straightforward and direct and simple answer for. This is one of those risks that has to be mitigated and managed actively and continually.

One of the things that we emphasize in all our trainings for faculty and staff and all our educational modules for students about AI is the importance of the AI assisting you, rather than you assisting the AI. If the AI produces some content for you, it has to be within a process in which you’re not just reviewing it for correctness, but you’re producing the content where it’s helping you to do so in some sense.

That’s a little bit vague, because it plays out differently in different situations, and that’s the case for faculty members who are producing a syllabus or using AI to produce other content for the courses to make sure that it’s content that they are producing with AI. Same thing for the students using AI.

For example, our institutional AI policy having to do with academic honesty and integrity, is, I believe, groundbreaking in the sense that our default policy for courses that don’t have a specific policy regarding the use of AI in that course—by next spring, all courses must have a specific policy—is that AI is allowed to be used by students for a very wide variety of tasks on their assignments.

You can’t use AI to simply do your assignment for you. That is forbidden. The key is the work has to be the work of the student, but AI can be used to assist. Through establishing this as a default policy—which faculty, department chairs, deans have wide latitude to define more or less restrictive policies with specific carve-outs, simply because every field is different and the needs are different—the default and the basic attitude is, AI is a tool. You need to learn to use it well and responsibly, whatever you do.

Q: I wanted to talk about the future of AI at the university. Are there any new initiatives you should tell our listeners about? How are you all thinking about continuing to develop AI as a teaching and learning tool?

A: It’s hard for me to talk about specific initiatives, because what we’re doing is we believe that it’s AI within higher education particularly, but I think in general as well, it’s fundamentally a start-up economy in the sense that nobody, and I mean nobody, knows what to do with it, how to deal with it, how does it work? How does it not work?

Therefore, our attitude is that we want to have it run as many experiments as we can, to try as many different things as we can, different ways of teaching students, different ways of using AI to teach. Whether it’s through simulations, content creation, some sort of AI teaching assistants working with faculty members, whether it’s faculty members coming up with very creative assignments for students that enable them to learn the subject matter more deeply by AI assisting them to do very difficult tasks, perhaps, or tasks that require great creativity, or something like that.

The sky is the limit, and we want all of our faculty to experiment and develop. We’re seeking to create that within the institution. Touro is a wonderful institution for that, because we already have the basic institutional culture for this, to have an entrepreneurial culture within the university. So the university as a whole is an entrepreneurial ecosystem for experimenting and developing ways of teaching about and with and through AI.