Embracing AI: Dr. Daniel Weissglass on Student Affairs and Academic Integrity in Modern Education

SA Voices From the Field

Artificial Intelligence (AI) is rapidly reshaping various sectors, and academia is no exception. In a recent episode of the SA Voices From the Field podcast, hosted by Dr. Jill Creighton, guest Dr. Daniel Weisglass shared his expert insights on the role, challenges, and potential of AI in higher education. Dr. Weisglass, an assistant professor of philosophy at Duke Kunshan University, delves into academic integrity, student affairs, and the future landscape of education with a particular focus on AI tools.

Rethinking Academic Assessments

At the heart of the discussion is the need for rethinking traditional academic assessments in light of AI advancements. Dr. Daniel Weisglass emphasizes the importance of critically evaluating the types of assignments given to students. He suggests that faculty members collaborate closely with academic integrity units to adapt their methodologies in response to the changing academic environment.

AI, particularly generative models like GPT (Generative Pretrained Transformer), can produce seemingly original essays and content. This poses a significant challenge to traditional assessment techniques, which often rely on evaluating written assignments. Dr. Weisglass advocates for the adaptation of in-person assessments to maintain academic integrity. Such measures echo the early days of Google search usage when educators needed to adapt to a new tool that changed how students accessed information.

The Value of Teaching and Mentoring in Student Affairs

Maintaining the historically valuable elements of student affairs is another critical point discussed by Dr. Weisglass. He underscores the importance of deep, meaningful connections and personal development in education. The role of mentoring and teaching in shaping students' experiences and growth remains as crucial as ever, despite the growing presence of AI in academia.

Dr. Weisglass suggests that while AI can support student affairs professionals by recognizing emotional patterns and raising alerts, it should not replace human interactions. The human aspect of teaching and mentoring is irreplaceable, and AI should serve as a supplementary tool rather than a substitute.

Addressing Modern Challenges in Student Affairs

The current state of student affairs has seen an increased awareness and maintenance of campus cultures. Dr. Weisglass highlights the new challenges posed by AI-enabled academic and student conduct violations. With the advent of sophisticated AI tools, distinguishing between AI-generated and human-generated content becomes increasingly difficult.

To combat these challenges, Dr. Weisglass advocates for developing robust administrative standards for safety and security. He also highlights the necessity of continual responsiveness and adaptation to student needs. As student affairs professionals, it is essential to stay ahead of technological trends and ensure that the academic and personal growth of students is not compromised.

Preparing for the Future: Flexibility and Ethical AI Use

Looking ahead, Dr. Weisglass envisions a future where student growth remains the primary focus, without leaning too heavily on a customer service-oriented approach. He emphasizes that flexibility, continual responsiveness, and reflective responses are key to effectively preparing students for a rapidly changing world.

Incorporating AI into education requires a thoughtful approach to designing prompts and assignments. The goal is to make use of AI tools, like GPT, to support the development of labor-intensive skills such as ethical analysis. Educators need to balance leveraging AI to aid the learning process while maintaining the integrity and authenticity of student work.

Embracing AI: Tools and Techniques

Dr. Weisglass discusses various AI tools and their applications in higher education:

1. Predictive AI: This AI type forecasts trends and flags at-risk students based on data patterns, such as class attendance. It helps institutions take proactive measures in student support.

2. Generative AI: While capable of generating new content, generative AI raises concerns about academic integrity. This type of AI can fabricate information and compromise data privacy.

3. Gamma Tool and Copilot: Gamma converts Word documents into detailed PowerPoint presentations, aiding in educational settings. Copilot, part of the Office 365 suite, helps summarize emails and meetings, streamlining administrative tasks.

4. Cite.ai: This tool assists in generating literature reviews and finding specific articles within academic research, ensuring the accuracy and authenticity of data.

Dr. Weisglass also stresses the importance of ensuring data security agreements with AI tool providers or developing in-house models to safeguard student data.

Conclusion

The insights shared by Dr. Daniel Weisglass underline the transformative potential of AI in higher education, along with its challenges. The integration of AI tools, such as GPT, must be approached with a balance of innovation and ethical considerations. By rethinking academic assessments and maintaining the human elements of teaching and mentoring, educators can harness AI's potential to enhance the educational experience while preserving the integrity and personal growth of students. As we navigate this evolving landscape, the role of AI in academia will continue to be a dynamic and critical area of exploration.

About our guest

Daniel Weissglass is an assistant professor of philosophy at Duke Kunshan University, a Sino-US Liberal Arts institution located in near Shanghai. His work focuses on the ethics of science, health, and technology - with a special interest in the use of artificial intelligence to meet health needs in low- and middle-income countries. He also works in various ways to help DKU make the most of AI as an educational tool, as well as assisting in the development of policies regarding their safe, effective, and ethical use.

TRANSCRIPT (Unedited transcript created by Castmagic)

Dr. Jill Creighton [00:00:00]: Welcome to Student Affairs Voices from the Field, the podcast where we share your student affairs stories from fresh perspectives to seasoned experts. Brought to you by NASPA, we curate free and accessible professional development for higher ed pros wherever you happen to be. This is season 11, the past, present, and future of student affairs, and I'm doctor Jill Creighton, she, her, hers, your essay voices from the field host. Welcome back to a new episode of SA Voices from the Field, where today we will feature Doctor. Daniel Weissglass. Doctor. Weissglass is an assistant professor of philosophy at Duke Kunshan University, which is a Sino US liberal arts institution located near Shanghai, China. His work focuses on the ethics of science, health, and technology with a special interest in the use of artificial intelligence to meet health needs in low and middle income countries.

Dr. Jill Creighton [00:00:49]: He also works in various ways to help DKU make the most of AI as an educational tool, as well as assisting in the development of policies regarding their safe, effective, and ethical use. So today's episode is gonna be focused on the use of AI in higher education. Daniel, welcome to student affairs voices from the field.

Dr. Daniel Weissglass [00:01:06]: Thanks so much, Jill. Glad to be here.

Dr. Jill Creighton [00:01:08]: It's great to see you. Daniel and I have known each other for a couple years now, and we're coming to you from across massive time zone difference. I'm sitting here in the UK and Daniel's sitting over there in China. So I can see the sun setting on his end and the sun rising on mine.

Dr. Daniel Weissglass [00:01:22]: There's maybe a metaphor, appropriate to the the topic of today's meeting about that.

Dr. Jill Creighton [00:01:26]: Oh, I'm excited to get into that. Daniel, you make your livelihood as a philosopher. And so I always like to ask our guests kind of how you got to your current seat, but we're speaking with you mainly today because of your burgeoning career in academia and artificial intelligence AI. So, yeah, tell us about how you got here.

Dr. Daniel Weissglass [00:01:46]: Well, interestingly, I can kind of weave those 2 together. Actually, part of what brought me into philosophy was an interest in AI. Now this was back before the the big data science boom even. So what AI meant at the time was a very different concept, one that had more to do with replicating human capacities and building something human like or assisting human in those performance is and and less to do with something like the large scale statistics that we see today. And the questions I kept finding myself asking was, well, if we're gonna talk about replicating something like human intelligence, I need to know better what that is. So I went into study in the philosophy of mind, and I also double majored in psychology. And I think the interest I have had in AI throughout my career is part of what brought me to where I am at DKU. DKU, Duke of Kunshan University, where I work, is a very interdisciplinary institute.

Dr. Daniel Weissglass [00:02:34]: It doesn't really follow traditional divisional or disciplinary divisions. We don't have departments. Right? We have these big, houses. I say for the audience, Gil, you know all of this. And when I presented my initial research presentation, it was actually about not artificial intelligence, but artificial emotion and the possibilities that might bring for things like moral control of AI. So I think this has been sort of a natural path for me. And then with the recent explosion in AI interest post large language models, the place for someone who can think critically and with some sort of baseline informative informedness about AI technologies, about the values that we have in using those technologies, has become more and more central to the mission of academic institutions worldwide. And I was very fortunate to find a community here that supports me pursuing that path.

Dr. Jill Creighton [00:03:26]: You mentioned something that I've not heard in these conversations a lot, which is not artificial intelligence, but artificial emotion or the the mimicization of human emotion in AI. And typically, we're talking about AI ultimately being barely stupid because it's only as good as what we input into it. Talk to us a little bit more about that emotion component.

Dr. Daniel Weissglass [00:03:47]: Oh, yeah. Absolutely. So there's a small literature on what's called synthetic emotion usually. And there are a couple of ways of understanding what that means. One is being able to respond to emotional cues of users appropriately. You can almost see this in chat gpt when it says, oh, I'm sorry. I made a mistake. Right? And that's important for a lot of reasons.

Dr. Daniel Weissglass [00:04:07]: One being that people are more likely to maintain systems that apologize when they fail. This was an interesting set of studies on that. But what I was more interested in is systems that try to replicate the you might call input output mapping, the sort of function in the mathematical sense that human emotions have. So ideally, a system that is capable in this way would be able to, for instance, look at an image and identify the emotion that that image would produce in most viewers. So if it showed an image of a person in suffering, right, it would it would identify that this would produce sorrow or sympathy. And this is really important, this kind of input output mapping, to producing morally correct responses in some cases. Emotions play a huge role in how we make moral choices and how we decide to respond to morally loaded events. And so there's a hope that we can make artificial moral agents, is the term that gets thrown around in the in the literature, that would be able to adequately replicate these components of moral reasoning, which I think must include something like emotion, so that they can regulate themselves more effectively.

Dr. Daniel Weissglass [00:05:10]: Now, presumably, we wouldn't want to take people out of the loop entirely. But if you don't ask them to regulate themselves based upon these basic presuppositions that we have captured in our emotional systems, you get behavior that can be very dangerous and and very much outside of what we call alignment. You get systems that are willing to lie, hate, steal, harass people, all of these things. And so the hope was and and what I was working on at the time, and it still is sort of on a back burner, is that synthetic emotion might be able to improve this, provide some sort of safety by allowing them to analyze allowing AI tools to analyze morally loaded instances in a way that's more similar to the way that humans do. There are a lot of challenges to that. But in context of something like an academic environment, this might involve something that's emotionally sensitive and responsive to student users, for instance, right? So imagine as we've been kicking around here the idea of an advisor bot. So you've got a first run chatbot that interacts with students. You don't just want the chatbot to be able to recognize the question and its meaning in a literal sense.

Dr. Daniel Weissglass [00:06:11]: You might also want it to note certain emotional patterns that could emerge in the way students are responding. Right? You might want it to note that, the questions this person is asking and the way they're asking them as the semester goes on really seem to indicate that this the student's not doing so well. Right? And maybe it could raise a flag there. Now this would be a, you know, a much more complex system most likely than what we're dealing with in the near future, but that was the idea. And I think synthetic emotion is an under explored space in education in the same way that emotion in general, in pedagogical context and advising context is underplayed. Right? We focus so much on cognitive expertise. We sometimes forget that this is broadly speaking a care profession, and we underplay the importance of that sort of emotional intelligence and emotional engagement, I think.

Dr. Jill Creighton [00:07:00]: For student affairs professionals, that's where we spend most of our time is working with students in that high EQ space, in that high empathy space. And the thought of having an AI bot to help us support that work is a really fascinating one. On my end, I'm teaching currently the technology module for masters in student affairs through NASPA and LUMSU University. And I just had my first lecture about a week and a half ago, and it was all about introducing student affairs professionals to user and AI tools. We're not talking about the technological side of machine learning and how we're feeding large language models and things like that. But really, what can these things do for us to help support our work? Because at the end of the day, when we're working with students, it's a human centered profession. And I don't believe any sort of technological replacement that can get us to a place where we don't need human interaction is the core of what we do in a university setting. I think that there's cognitive development that can happen through these bots or even quick answers.

Dr. Jill Creighton [00:07:55]: But when you're having a really hard day, talking to a bot is probably not going to help you find a space of resilience or thriving. But we also have, I think, jumped ahead quite a lot. We're already speaking from a at least a novice perspective on AI. So I want us to back up a little bit to, just give some primer and basics for someone maybe who has heard of AI but has never tried using a large language model. Maybe they're using predictive AI and they don't know it. Maybe they're a little fearful of the tools that are out there because they don't know much about them. So let's start with the super basic, which is can you describe for us the difference between predictive versus generative AI?

Dr. Daniel Weissglass [00:08:34]: So, I mean, the to some extent, the answer is in the name. So a predictive AI is focused on predicting. This might be making a sort of, like, quantitative prediction, right, where it says, you know, given recent trends in the financial market, this is what we expect these things to go. Right? You might see these even with very, very simple forms, like expected grades could arguably be something of this kind. They'd be very simple. So predictive AIs attempt to predict. They might also try to protect things like categories. You might look at patterns of enrollment or something and say, well, this student is likely to be a major in philosophy.

Dr. Daniel Weissglass [00:09:05]: This student is likely to be a data science major. And that could help you maybe plan staffing right down the road. They could also maybe identify students who we've noticed if you had the adequate data collection system. We've noticed this student has just missed 3 classes in a row across 2 different courses. Let's raise a flag. Something might be worth noting here. This is now a high risk student, maybe the classification would be. Generative AI works in a very different way.

Dr. Daniel Weissglass [00:09:28]: What it does is is generate something. It produces, and there's some debate about how novel the outputs of these things are, but a novel output based upon usually a description of the desired output. So you go in and you say draw me a picture of a bird, and it draws you a picture of a bird. You go in and you say, and this is the kind of thing that tends to worry academics, Write me a 10 page paper about the role of Rene Descartes' mind body dualism in creating a a sort of, individualistic conception of the self which results in all these problematic ways of viewing one's connection to society. I'm getting down a path here. Sorry. I'll back up a little bit. But you, you know, you ask it your your essay prompts, it will write an essay.

Dr. Daniel Weissglass [00:10:09]: Not always a good essay, but they're getting better. And so what generative AI does that predictive AI don't doesn't do is produce novel outputs, a novel at least in the sense that they're not just copy and pasting from somewhere. What raises concerns for academics, things like turn it in won't work, at least not as well.

Dr. Jill Creighton [00:10:25]: That generative AI space, I think, is that scariest space for academia, particularly in the academic integrity front as you just mentioned. But I think it also requires that we reevaluate how we're assessing student learning. We've been relying on the essay for 100 of years in terms of the way that we measure if a student's critical thinking skills have evolved in the course that the way that we want them to. But if I put this prompt into AI about my image of self through the lens of Descartes, I could also ask it to do a niamic pentameter, and it's gonna spit out something, but it may also invent sources. It may also just make stuff up that is not relevant. It could insert a number of different factors that, maybe the end user doesn't know that it's inserting. But it's also going to take what I input into that model and use it to continually train. So none of my data is private when I go into these models because it is collecting it and then using that to continue to synthesize on its own.

Dr. Jill Creighton [00:11:25]: But I I think what the most interesting piece is to me is that ultimately what we're looking at is math. We're looking at, how these machine learning components are taking language, which is ultimately just a variable for it, and then creating stories, full stories. So when you think about where we are right now in this moment in higher education, how do you believe that professors should be looking at these language tools, large language model tools in their work, in their assessment of student learning?

Dr. Daniel Weissglass [00:11:53]: Excellent question. I think this is the one that most faculty are really struggling with. And I think there are a couple of things to say. One is these tools are widely available and often without charge, which allows for effectively every student to be doing what what our wealthy students, who were maybe or or less scrupulous wealthy students anyway, might have been doing in the past, which is hire someone to write an essay for them. Everybody gets to do that. Now that was always a problem. It always existed. But there was enough of a barrier that we kind of just let it slide, I think.

Dr. Daniel Weissglass [00:12:24]: At least many of us did. Now we must respond. And the way we respond is gonna depend upon what your priorities are. Right? If you want to know that somebody knows something off the top of their head, you should be asking it in a classroom in front of you, maybe with a proctored test in some cases, if you're especially dealing with the increasing number of students who are dealing with with things like remote learning in some way. So there's this sort of, you know, if you wanna stick to the old old style and there are places where that's the right thing to do, you need to be doing it in person. But we also need to be thinking more broadly about what the world we're preparing our students to engage in is going to be like. These will be tools that they will have and be interacting with for the rest of their life. All of us will be whether we like it or not.

Dr. Daniel Weissglass [00:13:05]: And so we need to think about the ways that within our discipline, we can utilize these tools both to leverage learning about our disciplinary skill sets and our disciplinary topics, and also that we can train students to use the tools. Right? So there's using the tool to teach, for instance, philosophy, and then there's also teaching students how to use the tool. We're kind of in the early days of Google search again, where every class suddenly had to have a discussion about how to use Boolean search operators and that kind of thing. And while sometimes some of the stuff you're seeing out there isn't really legitimate, you should know that people make stuff up and lie online, and here's how we identify good sources. And now we have to do that with generative AI systems. Right? You should know they hallucinate is the term that gets thrown around. Right? They they make up facts. You need to learn how to prompt them in ways that help you avoid that.

Dr. Daniel Weissglass [00:13:55]: You need to learn which systems can be trusted for which kinds of things and generally best practices. I'm most excited about using them as tools to teach skills that are often labor intensive. So, again, as a philosopher and particularly in teaching ethics courses. Right? So there's a lot of skills that are important there about analyzing the ethical dimensions of a given case, about working through problems and reasoning effectively, and monitoring students while they do that is a wonderful thing to do and is possible in sort of a live action action way. But providing a chat GPT, a custom GPT that's been written to prompt my students to go through a certain set of steps, right, can provide them with maybe not quite the same quality, but a much more available version of this sort of prompt. Now I would never suggest that you could replace your your assessment that way or replace your your direct education that way. Right? There's still a place for for sitting in the room with me and working through it because I might notice problems that KKPT doesn't. But especially over time and with practice, as we learn how to use these tools ourselves, we can build these really cool interactive systems, sometimes called interactive tutor systems in the older literatures that help respond to our students where they are, guide them through complicated processes, and really have a lot of promise.

Dr. Jill Creighton [00:15:13]: You said GPT a couple of times, so I just wanna clarify definition. So when we say chat GPT, that GPT stands for generative pretrained transformer. I think a lot of people don't know that it's an acronym or just haven't gone to the depths of understanding what that means. And so the generative pretrained transformer means that it's taking the information that is already been fed that pretraining component and then transforming forming it into the output that we see as human beings. But we have different versions of chat gpt that have evolved over time. 3.5 was the one that a lot of people were using for a very long time. Now for Omni is out, which is a paid service. And so for Omni is better for sure in terms of the input it's been given and the output that it will give you.

Dr. Jill Creighton [00:15:55]: And when we look at what students are doing, it's it's not unaffordable to to become a paid member of, for Omni. And so you can use that to your advantage. The models aren't necessarily at a place right now where they can continuously self learn in the same way that we might expect them to, like a human brain can. But the information it's getting fed is is much more interesting these days. I was teaching the use of GPT 3.5 in my course the other day. And one of the things that I love about the course that I'm currently teaching is that students come from a multitude of countries. I think we had at least 7 countries represented in that space. And so we also learned a lot about bias, in the prompts that we were using and who trains the models, whose values are inputted into the models, what assumptions are made.

Dr. Jill Creighton [00:16:41]: One of the examples we looked at is how to respond to a highly critical email. And so what we had folks do is input the email into chat gpt, and then on the other side, ask it to craft a polite and salient response that covers these three points. We made sure to de identify any names. If your institution has a confidentiality clause of some kind, if you're trying to observe FERPA, you need to be really careful that you're not putting student identifiable information into these models because that data can be used. But what we got spit out was an extremely Americanized version of what that email can look like. And so it, again, raises the question, who is it for and whose biases are integrated into the system? And so the student that was representing work in Ireland said, I can't use this because it's too American and it doesn't meet my cultural needs. So we asked ChatCPT to transform that response to make it more culturally Irish, which I was real scared of that prompt. I'm not gonna lie.

Dr. Jill Creighton [00:17:38]: I thought we were gonna border into some very racist territory, and we were breathing a bit of a sigh of relief when it transformed it into something that the student identified as a little more usable. We tried the same thing in Lithuanian, and it did not give us what we needed because we had a student representing Lithuania. So the limitations of these models is very real, and that happens for for student learning as well. And I I think this is also true for things like Copilot. So the I think the 2 most ubiquitous tools right now are Chat GPT and Copilot for the everyday user. The other one that I've recently really taken to is Gamma. Have you played around with Gamma much?

Dr. Daniel Weissglass [00:18:14]: No. I don't think I have.

Dr. Jill Creighton [00:18:15]: Gamma is great. I actually designed my lecture using Gamma. It is a tool that you can take a Word document of, like, just an outline and upload it, and it will generate a PowerPoint for you based on what you've put into the Word document. If you want it to, it will also generate a ton of detail. Inocuous or not, it will also generate images that sometimes are really funky, but we we can get into images in a second. But what it did, I asked it to make a sandwich as an example of of how to do this. So I put in 10 lines of what I think are the basic instructions on how to make a sandwich. And then you can choose if you want it to give you basic output, kind of middle output, or thorough output, and it will just go to town about sauces and vegetables and slicing and toasting bread and types of cheese and things like that.

Dr. Jill Creighton [00:19:01]: So I think the sandwich example is an easy one because it can show you what it will take, which is make a sandwich, take some bread, add some veggies, add some protein, add some cheese, eat your sandwich, which is basically what I gave it. They turned it into a 10 slide PowerPoint, elaborate, elaborate, elaborate PowerPoint. So check out Gamma if you get a chance. What other tools are you using that people should know about?

Dr. Daniel Weissglass [00:19:20]: So like you said, the biggest sort of general purpose are CAT KPTN being Copilot right now. And they have got a different focus. Copilot's really working to integrate to the Office 365 suite in some interesting ways that I think have a lot of promise for administration, especially at universities. As we've all been on email chains with 45 professors and really, really wish that we could have an instantaneous summary of what's been happening, Copilot can do that. It can summarize everything that that that's been going on. It can even summarize the text of ongoing meetings less well, but from recordings identify what was said and give you the bullet points. So I think the administrative side will see a lot of Copilot in these applications in particular. Another prominent sort of general model is gonna be Anthropic's Claude model, which is like OpenAI's CATGPT in effect.

Dr. Daniel Weissglass [00:20:06]: And at various times, they pulled ahead of one another and which one produces, in some sense, the best quality output. So these are sort of the major commercial general use systems. There are specialized systems. So I use one called site dotaiscite.ai. Maybe I should ask for, like, a a free month.

Dr. Jill Creighton [00:20:26]: We are not sponsored by site.ai.

Dr. Daniel Weissglass [00:20:27]: No. We are not. Right? But what it does is solve the problem that ChatTPD has or at least tries to solve the problem of making stuff up. So it is designed to look around a large corpora of published academic work and identify articles that relate to various topics. It can even write you a sort of general overview of our topic that has these articles. And for researchers like myself, when I when I do research, it can be very helpful in a lot of ways. One is I need to find a quick literature review, essentially. Give me 10 articles that talk about this topic in contrasting ways, and it will generate a pretty decent list.

Dr. Daniel Weissglass [00:21:04]: The other and the one that is very much time saving as I'm sure you've encountered this too is, you know, when you're writing a a long paper, you've read 45, 50, a 100 articles on some topic, And you remember that one of them said something like this. And your options are pretty much to control f and go through every document looking for keywords. But if you get the keyword wrong, you're just gonna have to keep doing this over and over again. So you can give Cite a list of articles and ask it to make inferences based just on those articles. And you can say things like, where which of these articles is likely to have said something like this? And it can give you some direction there. So it's been a very interesting tool, and I think one that a lot of people in the academic areas will will look at. Another thing to keep in mind is that there are also open source versions of these tools. So things like Hugging Face is is a prominent I know it's a weird name.

Dr. Daniel Weissglass [00:21:57]: A prominent provider of these sorts of sources which allow people to make custom tools and tools that might protect data in ways that are really important. So there are 2 ways to go. You keep you you brought up the data security point which is really important. There are 2 ways for an institution to go here. 1 is to work with a provider to develop a data security agreement and to ensure that your institutional data will not be used for training a model. We can do that, and and institutions have done that. And I believe Duke has done this with Copilot, can do this with CAT KPT, and sometimes you'll set up a sort of private instance of one of these models where you put it on a server that is sort of isolated from the rest of the system. So this is one way that institutions can handle the privacy issue.

Dr. Daniel Weissglass [00:22:36]: Another though is to build one in house. Now these models tend to be not as well fine tuned. They tend to be based on sort of the base model. So when we talk about chat to GPT, right, the GPT refers to a foundation model, which is a general purse purpose model, which can be used in various ways by various tools to create whatever output you want. ChatGPT is a specialized tool made by a specific company. Right? It's a packaging of that for sort of client use. There's other ones like, Lambda is another prominent foundation model. And so you can use one of those, take it yourself to, be adequate to your purposes, but then you're going to be dealing with the need to maintain that system more in house.

Dr. Daniel Weissglass [00:23:15]: You won't be automatically keeping up with improvements that are becoming standard elsewhere in the world like you would with a commercially mainstream model. And the process of fine tuning and improving performance can be really expertise and time intensive.

Dr. Jill Creighton [00:23:29]: You've mentioned prompting the models a couple of times. I think this is an important point for us to get to. The philosophy I've come to adopt after watching, you know, hundreds of YouTube videos on how to prompt these systems well is garbage in, garbage out. That is, I think, the best way that we can encapsulate how how to prompt one of these systems. Meaning that the more specific that you can get with your prompt, the more likely you are to get a usable reply. And if you are putting in nonsense or garbage, you're going to get nonsense or garbage back on the other side. So for example, if I want to write a paper, and don't do this for your academic integrity reasons, but if I wanted to write a paper on the future of student affairs and I put in to the GPT program, write me a paper on the future of student affairs, it's gonna go every which way. But if I put in write me a paper on the future of student affairs that covers the integration of artificial intelligence and the replacement of human jobs with AI and make sure that it is in a professional style and uses at least 10 sources, I'm gonna get a much different output than if I just said that very simple thing at the front.

Dr. Daniel Weissglass [00:24:37]: Learning how to prompt is an important part of learning how to use these tools, both for us and for our students. Right? So this is when I when I talked about the need to teach students how to use these tools. I teach research methods in some of my classes that are now based around the effective use of these tools. We need to learn how to prompt them and how to interpret their output in ways that are helpful. And there are a lot of different approaches to crafting prompts that produce a sort of certain desired behaviors. Generally, this is called prompt design, which can be contrasted with prompt engineering, which has more to do with efficiency of a performance of a system. But when you design a prompt, there's a lot of different ways to do this. I use sometimes cane of thought or instruct models, but the basic idea for both of these is to deal with a problem that most of these systems have, which is that they don't follow rules very well.

Dr. Daniel Weissglass [00:25:26]: So, again, let's bring back the case of adviser GPT. Right? If I ask it, how do I major in philosophy without taking logic? It might say, oh, go. Yeah. Here's how you would do it. You would have to take all these other courses and talk to your adviser and get these substitutions, but what it ought to say is you can't. The systems are designed to be helpful. They will find you an answer even if it's wrong unless you tell it not to. And so with careful sort of design methodologies, you can say, okay.

Dr. Daniel Weissglass [00:25:53]: Well, you don't you know, first, you review the bulletin and look for an answer to the question, then you craft an answer to the question, then you make sure that the answer you are about to give me is correct. If you don't if if you cannot find a citation in the bulletin, do not give the answer and instruct me instead that this is you don't know. Right? This is a really important thing, actually, teaching them how to tell you they don't know. And so prompt design really radically changes things. It's also one of the things that makes it, in some sense, more dangerous for academic integrity than people realize. It's very common for people to sit down with this tool and take the vanilla out of the box ChatPpt and say, well, I asked it my questions. It gave terribly bland answers. That's fine.

Dr. Daniel Weissglass [00:26:33]: I would either be able to tell this or it wouldn't do well in my class anyway. But a student who knows what they're doing could upload your syllabus, the rubric for the assignment, any samples you've given, could upload work they have written in the past and say, match this style so that it's gonna sound like them writing for you. And and that's a a thing we need to understand.

Dr. Jill Creighton [00:26:53]: I'm sorry. I I think this is a this is the critical juncture right now of where especially student affairs is with academic integrity and AI because a lot of universities put the AI and I use AI doubly here because we say academic integrity is AI as well as artificial intelligence. But the responsibility for academic integrity falls into student affairs spaces at a large number of universities. So what is your best pro tip on how to identify whether an essay was generated by a large language model in part or in whole?

Dr. Daniel Weissglass [00:27:25]: So I I'll return to the analogy earlier that having your paper written for you by CAT GPT is kind of like hiring someone to write it for you. You will not, in most cases, be able to use automated tools to identify effectively whether or not a paper has been written by a large language model or Any Script generative AI. In fact, OpenAI pulled their tool down. Now there's some word that they might have a tool that does this, but the way their tool was intended to work was specifically with reference to work it produced. It would encode essentially a watermark in the way it codes words that would be undetectable to most readers, but they could detect in the statistical properties of the way the words are related. But, of course, that wouldn't work if someone used Claude. Right? If they used a different system, you no longer have that watermark system. So my big my first message is do not rely on automated detection of AI content.

Dr. Daniel Weissglass [00:28:17]: It will not work effectively based on my understanding, and you're really risking unfairly penalizing students in ways that are not productive. The second is talk to them the same way that you would if you thought someone they had paid someone to write this paper. Say, you know, I find that your your use of this, William's 1998 paper really interesting. How did you come across that paper? They should have an answer, right, especially if this is reasonably close for the period when they wrote it. Right? Or, you know, you use this this term a lot. Can you explain to me what you mean by this term? These kinds of questions can give you a better sense. But in a lot of ways, we are now back to a period of academic integrity that many of our younger faculty, including myself, have never existed in before, which is there is not going to be certainty any long. Right? I'm used to only penalizing students for academic integrity when I went on Turnitin and went, oh, yeah.

Dr. Daniel Weissglass [00:29:11]: Or I, you know, pulled a phrase and went to Google and looked it up and, well, there it is. Right? That's probably done at least for the foreseeable future. So get comfortable with ambiguity, return, and think very seriously about the standards of evidence that you are using to assess academic integrity, what degree of certainty you need to feel you have to feel confident in leveling certain types of penalties, and understand that this is going to become a more intensive investigating procedure than was often the case in recent years.

Dr. Jill Creighton [00:29:39]: This is such a tricky space because I feel like it's a losing battle for those of us who work in the academic integrity space of whack a mole. Right? Is this one generated? Is this not generated? And the tools are only gonna get better. So, again, the question is really what are we trying to assess from our students and why are we trying to assess it? And now the third question is how are we going to assess it in a way that ensures that they're they're learning? And so we we do have, generative tools that can do voice. We have generative tools that can do writing. We have generative tools that can do images, and these are all getting more sophisticated. So, you know, in 5 years' time, there may not be a discernible difference, and we will see what happens. Right now, these programs can't do hands. It's the oddest thing.

Dr. Jill Creighton [00:30:21]: If you ask a program to generate you an image of a human hand, they they somehow can't figure that one out. And the other one I saw interestingly the other day was that no large language model can correctly tell you the number of r's in the word strawberry because of the way that the algorithm is broken down.

Dr. Daniel Weissglass [00:30:38]: So so there are some definite limitations. They're also bad at teeth. Anything that requires them to see inside of what they're doing, they are bad at. So they can't count, for instance. Right? So this is the number of r's in strawberry. They'll often struggle because they see that as a single unit and they can't crack it open to look at what's inside of it. So they're just very confused. If you ask them word counts, they can even struggle with that sometimes.

Dr. Daniel Weissglass [00:30:58]: I think one thing you brought up is really important here is academic integrity is associated with the kinds of assessments we're using. And in fact, its function to some large degree is to maintain the authenticity of those assessments. And a lot of what's going to have to be communicated here is that we need to rethink assessment like we said earlier. And this is going to push back in some sense on faculty. Faculty need to be working with their academic integrity units to understand what can still be meaningfully assessed, what can still be meaningfully maintained in the classroom. If you really need to know that a student knows the date of some event or can analyze some text off the top of their head. Again, you should be doing that in class where such concerns are simply gone. Right? The blue book is gonna make a fierce comeback, I predict.

Dr. Daniel Weissglass [00:31:44]: So, otherwise, we need to just be more critical about the kinds of assignments we use. A lot of us are operating on tradition. Same things with how we understand academic integrity is largely influenced by a long academic tradition that operated under a form of intellectual productivity that may no longer be a form that we will be operating under. And so we need to adapt to those changes even in concepts as basic as what does it mean to have academic integrity and what am I doing in this ethics class.

Dr. Jill Creighton [00:32:11]: Which is an ethical question. Indeed. Very meta. Well, Daniel, this season's theme is the past, present, and future of student affairs. And knowing you're on the faculty side of things, you might have an interesting perspective for us. So I'm gonna ask you our 3 questions on our theme for the season. So focus on the past, What's one component of the history of the student affairs profession or tradition that you think we should continue to carry forward or to let go of?

Dr. Daniel Weissglass [00:32:33]: I think looking at the things I wanna carry forward is again, I think I said this a bit earlier, but I I think of teaching and student affairs and the university as a whole more and more of as a care profession and as a mentoring process. This is work intensive. It can be exhausting and frustrating, but I think it's the important thing. And this is something we see, I think, taken more seriously in some sense in prior iterations of what the university meant. Now in some sense, that's because they didn't have the large class sizes that we're dealing with. They didn't have huge universities that sprawled in this way and had as their mission to bring education to a large number of people. Right? Education was an elite thing. But if we can capture that, that sort of deep powerful connection, that deep mentoring, then we still have a value add.

Dr. Daniel Weissglass [00:33:24]: Right? Then we're still contributing somehow to their development as a person. And in a sense, answering the question that the university has been facing since the dawn of the printing press, which is if I can just go read this there, why do I need to talk to you?

Dr. Jill Creighton [00:33:36]: Conan Gutenberg. Okay. Alright. So our question on the present. What is happening in the field of student affairs or higher ed right now that's going well for student affairs in general?

Dr. Daniel Weissglass [00:33:45]: I think we're becoming much more aware of campus cultures and the way that they need to be maintained effortfully. There are debates and reasonable ones to be had about what exact boundaries we want to set on our cultures, but I think for much of our history, we haven't really been engaging with that question as substantively and as effectively as we have recently. To connect back to the AI concern, one thing that we need to think about very seriously is that these tools not only enable academic integrity violations, but student conduct violations of the kinds that we may never have seen.

Dr. Jill Creighton [00:34:17]: Automated harassment is a very real possibility now.

Dr. Daniel Weissglass [00:34:17]: We've already seen this in some it in the broader world. We've seen things like revenge porn being fabricated with AI tools. We've seen falsified videos and audio using other people's voices. These are questions that we are gonna have to start figuring out both how to protect our students from administratively, right? What standards of safety and security we put in play and also how we react to these sorts of things.

Dr. Jill Creighton [00:34:47]: And moving towards the future, in an ideal world, what does the field of student affairs need to do to thrive going forward?

Dr. Daniel Weissglass [00:34:54]: Oh, that's a big question. I suppose, I mean, the short and easy answer is to continue to focus on students. I do think there's a version of focusing too much on students that can be problematic for universities where we become too customer service oriented. We need to avoid that. I think the analogy that I find more effective is the gym. Right? Which is, look, we're here to help you learn, help you grow, but you have to come and still have to do the work. Right? We're not gonna lift the weights for you. And so I think student affairs institutionally and, you know, faculty as well, we need to think a lot about how to promote and prepare the student for the world world that is coming.

Dr. Daniel Weissglass [00:35:26]: And that is always changing. Right? And in a sense changing maybe faster these days than it was in history. And so maybe it made hitting a sense of flexibility and continual check-in and continual responsiveness is an aspect of this. So like a continual reflective response to students needs and the likely future realities that they will face. That might be my answer, I think.

Dr. Jill Creighton [00:35:47]: It's time to take a quick break and toss it over to producer Chris to learn what's going on in the NASPA world.

Dr. Christopher Lewis [00:35:53]: Welcome back to the NASPA World! I'm really excited to be able to share some of that with you today. Every October, NASPA celebrates the profession of student affairs. It's a month long celebration of careers in student affairs. In this month long celebration, the NASPA community comes together to share knowledge, network, and uplift the student affairs profession. There's a number of great activities that are happening throughout the month that you can take advantage of, that you can get involved in and encourage you to go into the NASPA online learning community to check out all of the resources that have been brought together in one place for careers and student affairs month. And think about ways in which you can talk about our career with people on your campus, with undergraduate students, graduate students, and more. There's a couple of opportunities for you to be able to submit proposals for a few of the upcoming symposiums and institutes that are happening within our community. The 2025 NASPA International Symposium proposal submission deadline is October 15th.

Dr. Christopher Lewis [00:36:56]: The International Symposium serves as a dynamic platform for student affairs professionals globally to share insights, engage in meaningful dialogue, and network, as well as practitioners interested in further developing their global competency skills. The international symposium is happening on March 15th 16th, and program submission deadlines are available on the NASPA website. And you can do a proposal for a flash lightning talk, a general intersession, poster session, or scholarly paper. Highly encourage you to submit a proposal today. Also, the 2025 NASA Community College Institute Institute proposals are due on October 18th. The 2025 Institute will focus on celebrating the achievements of student affairs professionals, equipping new generations for success in transforming the field through collaboration and mentorship. As mentioned, the deadline for proposals is October 18th, and I hope that you will submit a program and help shape the future of our profession. The NASPA public policy division award applications are due October 12th.

Dr. Christopher Lewis [00:38:02]: The NASPA public policy professional award honors exceptional leadership and commitment in student affairs through public policy. Nominate a deserving colleague with a letter of nomination to support letters and their resume. Don't miss this chance to shine or to shine a spotlight on an exemplary colleague. Every week, we're going to be sharing some amazing things that are happening within the association. So we are going to be able to try and keep you up to date on everything that's happening and allow for you to be able to get involved in different ways. Because the association is as strong as its members. And for all of us, we have to find our place within the association, whether it be getting involved with a knowledge community, giving back within one of the the centers or the divisions of the association. And as you're doing that, it's important to be able to identify for yourself, where do you fit? Where do you wanna give back? Each week, we're hoping that we will share some things that might encourage you, might allow for you to be able to get some ideas that will provide you with an opportunity to be able to say, hey, I see myself in that knowledge community.

Dr. Christopher Lewis [00:39:14]: I see myself doing something like that. Or encourage you in other ways that allow for you to be able to think beyond what's available right now, to offer other things to the association, to bring your gifts, your talents to the association and to all of the members within the association. Because through doing that, all of us are stronger and the association is better. Tune in again next week as we find out more about what is happening in NASPA.

Dr. Jill Creighton [00:39:44]: Chris, thank you so much for another great addition of NASPA World. It's always great to know what's going on in and around NASPA. And, Daniel, we have reached our lightning round where I have 7 questions for you in about 90 seconds. Are you ready to go?

Dr. Daniel Weissglass [00:39:58]: We'll find out.

Dr. Jill Creighton [00:39:59]: Alright. Question number 1. If you were a conference keynote speaker, what would your entrance music be?

Dr. Daniel Weissglass [00:40:04]: Time For Tea. I don't know. It's a weird song I really like.

Dr. Jill Creighton [00:40:07]: Number 2, when you were 5 years old, what did you wanna be when you grew up?

Dr. Daniel Weissglass [00:40:11]: A father, husband, and a good man.

Dr. Jill Creighton [00:40:12]: Number 3, who's your most influential professional mentor?

Dr. Daniel Weissglass [00:40:15]: Walter Sinnott Armstrong at Duke.

Dr. Jill Creighton [00:40:17]: Number 4, your essential higher education read.

Dr. Daniel Weissglass [00:40:20]: Why don't students like school?

Dr. Jill Creighton [00:40:21]: Number 5, the best TV show you've binged lately.

Dr. Daniel Weissglass [00:40:24]: The Sopranos. A little out of date, going back to the classics there.

Dr. Jill Creighton [00:40:27]: Number 6, the podcast you've spent the most hours listening to in the last year.

Dr. Daniel Weissglass [00:40:31]: Recently, it's going to be History of Philosophy Without Any Gaps, a fantastic podcast for anyone interested in the history of philosophy without any gaps.

Dr. Jill Creighton [00:40:38]: Number 7, finally, any shout outs you'd like to give, personal or professional?

Dr. Daniel Weissglass [00:40:42]: I oppose to everyone here at DKU who's been so responsive and helpful as we move forward towards an AI enabled future. We really had a lot of people who've been supporting these kinds of efforts. Noah Pichis and and Ben van Overmeijer, has been engaged in a lot of I I would have I would have to think, Ying Chong. Really, just everybody here has been very on board, I feel like, with this effort. And, you know, that's been very influential in getting this going.

Dr. Jill Creighton [00:41:05]: Well, Daniel, it's been a pleasure to speak with you on this topic. I think this is a conversation we're gonna continue to have in higher education for many, many years to come. If anyone would like to connect with you on your expertise on AI or philosophy, how can they find you?

Dr. Daniel Weissglass [00:41:17]: So the easiest way is to email me at [e-mail]@dukecoonshaun.edu.cn. You can also find me on my website, danielweissglass. That's danielweiss blast.com. It's just my name, which I suppose will be in the show note.

Dr. Jill Creighton [00:41:37]: Those show notes are partially generated by AI.

Dr. Daniel Weissglass [00:41:40]: Fantastic. And I really am happy to talk about any of this stuff, and I expect to have even more interesting things to say in the near future. There's some interesting stuff happening here, and I think we'll we'll soon be in a position to continue the conversation.

Dr. Jill Creighton [00:41:52]: Well, Daniel, again, a pleasure to have you on the show to talk with you about this area of subject matter expertise, and thank you so much for sharing your voice with us today.

Dr. Daniel Weissglass [00:42:00]: Thank you, Jill. It was a lot of fun.

Dr. Jill Creighton [00:42:06]: This has been an episode of SA Voices from the Field brought to you by NASPA. This show is made possible because of you, the listeners. We continue to be grateful full that you choose to spend your time with us. If you'd like to reach the show, you can email us at sa voices at naspa.org or find me on LinkedIn by searching for doctor Jill L Creighton. We welcome your feedback and your topic and guest suggestions. We'd love it if you take a moment to tell a colleague about the show and leave us a 5 star review on Apple Podcasts, Spotify, or wherever you're listening now. It truly does help other student affairs pros find the show and helps us to become more visible in the larger podcasting community. This episode was produced and hosted by doctor Jill Creighton.

Dr. Jill Creighton [00:42:44]: That's me. Produced and audio engineered by Dr. Chris Lewis. Special thanks to the University of Michigan Flint for your support as we create this project. Catch you next time.