Category: Generative A.I.

Stephanie Laggini Fiore

While we have dealt with many aspects of AI and teaching in this blog series, we want to end the series with the most important aspect—talking to your students about AI and learning. One of the realities of the present moment is that we are all in the midst of a disruptive change, one that neither we nor our students fully understand how to navigate. Therefore, whether or not we decide to allow the use of AI in our classes, it is vitally important to discuss these tools with our students in productive ways. 

At the CAT, we have seen plenty of draconian language on syllabi over the years (“Don’t even think about cheating; you will be caught!!”), but the old adage about catching more flies with honey than with vinegar stands true here as well.  Establishing trust in the learning environment, having clarifying conversations about AI and the choices you have made for the course, engaging students in thinking critically about the use of these tools and what they mean for society and for learning, and welcoming students’ thoughts will be far more effective than setting up an adversarial dynamic. We recommend dedicating time to discussing generative AI during the first week of the semester and then re-engaging students briefly before each written assignment. You should, of course, take some time to go over your AI syllabus statement, explaining your reasons for the decisions you have made, but it is important to go beyond that conversation to allow space for students to reflect on what it means to use these tools for learning. 

Here are some thoughts on how to speak to your students about AI:

• Consider using an anonymous poll that asks the extent to which your students have used these tools. This will provide a window into how familiar your students are with generative AI.
• Begin the conversation by asking students what they know about generative AI. You may be surprised about what they do (or don’t) know. Continue with a clarifying conversation on how generative AI tools work, including their benefits and pitfalls. It will be most effective if you can show examples of those benefits and pitfalls—for instance, an example of a hallucination (inaccuracies) or biased content that it might reproduce. 
• Engage students in thinking about how your assignments help students to achieve the goals of your course. We often recommend using Bloom’s Taxonomy for this exercise. If, for example, you have a goal that reaches the level of evaluation on the taxonomy, how will the assignments (if completed by the student) aid in their attainment of that goal? 
• Think about how to connect your students to the value of what they are learning. Often students see our courses (especially our required courses) simply as hoops to jump through on the way to a degree. Can you articulate for your students the reason why what they are learning will benefit them? What relevance will it have for their professions, personal growth, future academic work, or communities? Helping students to find meaning in what they are learning will be key to managing AI use.
• Include a discussion about AI and academic integrity. Why is academic integrity important? How can we think about the use of generative AI in ethical terms? Uses case studies to have them ponder whether those uses are ethical; for instance, how they would feel if you offloaded all student feedback to an AI? Would that be an ethical use of the tool or would it be a breach of your responsibility as an instructor?
• Ask students to discuss important philosophical questions that will get them thinking about the nature of learning, thought, and voice, such as:
  • Why do we write? What kinds of thinking happens when we write? Query students about how they use writing outside of class: do they keep a journal, write their opinions on social media, text friends when something important happens? Why might they turn to writing to express their thoughts? 
  • What does it mean to cede our thinking and our voice to non-sentient machines? Do we want to live in a world where none of our passions and ideas are expressed in the way that we want to express them, and where originality of thought is replaced by a process of scraping a dataset for answers? 

Talking to a student when you suspect cheating

You’ve followed our advice above and talked to your students about AI from day one of the semester, clarifying permissible use in your course. Still, you suspect that a student in your class has used AI in ways that you have not allowed. The first step is always to talk to the student. Here are some tips for tackling this discussion: 

• Don’t take it personally! Cheating can often feel like a personal attack and a betrayal of all the work you’ve put into your teaching. Remember that a student’s decision to use AI to take shortcuts is probably about them, not about you. 
• Check your biases. Is your suspicion of your student’s work well-founded? Would you have the same concerns if the work had been handed in by other students? 
• Beware of falsely accusing students outright. As was established in a previous post, our ability to accurately identify the use of AI generative tools at present is quite weak.  
• Ask the student to meet with you. Simply say something like “I have some concerns about your assignment. Please come to see me.” 
• When you meet with the student, try not to be confrontational (remember that you may not be certain they used AI in an unauthorized manner). Instead, start by asking them questions that will give them a moment to tell the story of their writing process, such as: How were you feeling about the assignment? What do you think was challenging about it? Why don’t you tell me what your process was for getting it done. If there is research involved, you can ask what research they used. If they were writing on something they were supposed to read or visit (an art exhibit, for instance), ask pointed questions that get at whether they actually engaged in that activity.  
• Then state your concerns: I’m concerned because the writing in this assignment doesn’t seem to match the writing in your other assignments, and the AI detector tool said that it is AI written. Point out any inconsistencies, odd language, repetition, or hallucinated citations with the student.  
• Use developmental language. Remember that your student may have used generative AI without realizing it is considered cheating, or there may have been factors that made them feel that they needed to cheat. A conversation with your student can be a learning opportunity for them. 
• Discuss with the colleagues in your department what a reasonable penalty might be for unauthorized use of generative AI. Consider also when it might be necessary to contact The Office of Student Conduct and Community Standards. (Remember, however, that speaking with your student is always the first step before taking further action.) If your conclusion is that the student cheated, you’ll have to decide whether you allow them to complete the assignment again on their own (perhaps with a penalty) or whether you’ll give no options to right the ship. Consider that we are in a developmental stage with these tools and it might be good to give the do-over if the student owns up to it.
• Self-reflect. Given that students often take shortcuts for reasons related to the course structure, review our blog post on academic integrity and AI in order to take steps to promote academic integrity and consider whether your course is designed to reflect these best practices.

In a world in which AI is here to stay, it is essential that we support students’ ethical and productive interaction with these tools. No matter the discipline, we need to take on the responsibility of developing our students to adapt to this new reality with full awareness of the implications of AI use for learning, for work, and for society. 

We know that this is all new and it is not easy—the CAT is here to help. To book an appointment with a CAT educational developer or educational technology specialist, go to catbooking.temple.edu or email cat@temple.edu.

Dana Dawson, Ph.D

In previous posts in this series, we noted how generative AI can perpetuate biases and exacerbate the digital divide. Here, we will explore in more depth the potential for these tools to widen the institutional performance gaps that impact learning in higher education, but also the potential for generative AI to create a more equitable learning environment. We conclude with suggestions for what you can do to minimize possible negative impacts of generative AI for students in your courses. 

Rapid improvements in the capabilities of generative AI have a tendency to provoke doom spiraling, and there are indeed some very real concerns we will have to grapple with in coming years. While generative AI at times produces helpful summaries of content or concepts, it is prone to error. Students with tenuous confidence in higher education or their capabilities of succeeding in their studies are less likely to deeply engage in their coursework (Biggs, 2011; Carver, 1998) and may rely excessively or uncritically on AI tools. Over-reliance on generative AI to reduce effort, and not as a mechanism for jumpstarting or supporting conceptual work robs students of opportunities to practice and develop the very creative, critical thinking and analysis skills that are likely to become increasingly valued as AI is more widely available. In addition, where we neglect to carefully vet content created by AI, we run the risk of repeating erroneous information or perpetuating disinformation. The prospect of bias and stereotypes impacting students’ experience in higher education arises not only from the content generative AI produces (Bender et al., 2021; Ferrara, 2023), but from the challenge of determining whether a student has appropriately used the tools. AI detectors cannot reliably differentiate human- from AI-generated content. Faculty must be aware that judgments of whether students relied excessively on AI may be influenced by assumptions that have more to do with factors such as race, gender or spoken language fluency than student performance. Finally, faculty who wish to encourage students to experiment with and integrate the use of AI tools must be aware that inequitable access to broadband internet connection and digital tools, along with varying levels of preparation to effectively use the tools, may differentially impact students. Variable access to broadband prior to, or during their postsecondary studies raises digital equity concerns. Some students will come to our classes well-equipped to engineer prompts and vet generated content while others will be encountering these technologies for the first time. That high quality AI applications are often behind paywalls compounds these issues. 

On the other hand, some scholars and policy-makers have pointed to ways that these tools can be productively used to support student learning and success. AI tools such as ChatGPT can be used to fill in knowledge gaps related to a field of study or to being a college student more generally that are particularly salient for first-generation students or those whose previous educational experiences insufficiently addressed certain skills or topics. GPT3 responses to prompts such as “What are the best ways to study?” and “How do I succeed in college?” generate strategies that are useful and can be expanded upon with additional prompts. Warschauer et al. point out that for second language learners, the ability to quickly generate error-free email messages or to get feedback on one’s writing reduces the extra burden of studying disciplinary content in a second language. Students can prompt generative AI tools to explain concepts using relatable analogies and examples. For students with disabilities, generative AI can serve as an assistive technology, for example by improving ease of communication for those who must economize words, assisting with prioritizing tasks, helping practice social interactions or modeling types of communication. 

RECOMMENDATIONS

1. Reduce the potential for bias to impact your assessment of unauthorized student use of generative AI tools by determining the following before the start of the coming semester:

• Which assessments have the most potential for unauthorized use?
• Is there an alternative mechanism for assessing student learning for those assessments most prone to unauthorized use?
• What are my guidelines for appropriate use of generative AI tools in this class?
• Can I reliably detect inappropriate use?
• Is my determination of inappropriate use subject to bias? 
• What will my next steps be if I suspect inappropriate use?

If you’re not sure whether to allow use of generative AI tools, review our decision tree tool.

2. Clearly communicate your classroom policies on use of generative AI and talk with (not to) your students about those policies, ensuring they understand acceptable limits of use.

3. If you are encouraging the use of generative AI tools as learning tools, consider questions of access by:

• Assessing the extent to which your students know how to use and have access to the tools; and
• Showing students how to use the tools in ways that will benefit their education (for example, using follow-up prompts to focus initial queries). Temple University Libraries has created an AI Chatbots and Tools guide to help our students learn to judiciously use these tools.

4. Educate students on how generative AI tools may be biased, can perpetuate stereotypes and can be used to increase dissemination of mis- and dis-information.

• See the ideas in our previous blog post in this series and check out Temple Libraries’ “Fake News,” Misinformation & Disinformation library guide and How to SIFT Through Misinformation online tutorial.

5. Help students find their own voice and value a diversity of voices in writing and other content that has the potential to be generated by AI tools.

• Discuss the risks of homogenization of ideas and ways of expressing ideas that arise with the use of generative AI tools.

6. Consider a SoTL project.

• Cecilia Ka Yuk Chan points out that, “Regular assessments of AI’s impact on teaching practices and student performance will help identify areas for improvement and ensure that the technology is being used effectively and ethically.” Make use of the CAT’s umbrella IRB and other Scholarship of Teaching and Learning resources to support your projects.

In the next (and final) installment of our series, we’ll focus on how to talk to your students about generative AI. In the meantime, if you’d like to discuss AI or any other topic related to your teaching, please book an appointment for a one-on-one consultation with a member of the CAT staff.
 

Works Referenced

Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021, March). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623).

Biggs, J. (2012). What the student does: Teaching for enhanced learning. Higher education research & development, 31(1), 39-55.

Carver, C. S., & Scheier, M. (1998). On the self-regulation of behavior. Cambridge, UK: Cambridge University Press.

Ferrara, E. (2023). Should chatgpt be biased? challenges and risks of bias in large language models. arXiv:2304.03738.

Dana Dawson serves as Associate Director of Teaching and Learning at Temple University’s Center for the Advancement of Teaching