Case Study 1: Priya's AI Policy — Drawing the Line

The Setup

Priya Sharma is halfway through her second year as a political science major. She is carrying 16 credit hours, working 15 hours a week at the campus bookstore, serving as treasurer of the South Asian Student Association, and trying to maintain the GPA she needs to keep her scholarship. She is not lazy. She is stretched thin.

When generative AI tools became widely available, Priya — like most of her classmates — started experimenting. At first cautiously: asking a chatbot to explain concepts from her statistics course. Then more ambitiously: brainstorming essay topics, generating outlines, checking grammar. She noticed something important: AI tools did not make her work easy, but they made it faster. And speed, for someone juggling Priya's schedule, was everything.

The trouble started in October, during what Priya privately calls "the week from hell." She had a policy brief due on Wednesday, a midterm on Thursday, and a presentation on Friday. She was running on four hours of sleep and three cups of coffee. At 1 a.m. on Tuesday night, she opened a chatbot and typed: "Write a policy brief about the effectiveness of renewable energy tax incentives in the United States."

The chatbot produced two pages of polished, professional-sounding prose. Priya read through it, fixed a few awkward phrases, added a personal anecdote about her uncle's solar panels, and submitted it. She got a B+.

She felt relieved. Then she felt uneasy. Then she felt something she was not expecting: disappointed in herself.

The Dilemma

Priya is now wrestling with several overlapping questions:

The immediate question: Did she cheat? Her university's academic integrity policy prohibits "submitting work that is not your own" and "unauthorized assistance." But it does not specifically mention AI tools. Her professor's syllabus says nothing about AI. If the rules do not explicitly prohibit something, is it fair to punish it?

The skill question: Priya knows she can write a policy brief. She has done it before, and she got an A. But this time she did not write it — she edited someone else's (something else's?) draft. She got a lower grade than she usually does on her own work, which raises an ironic possibility: the AI actually made her work worse, not better, because she was editing a generic draft rather than building an argument from her own understanding.

The fairness question: Priya knows that some of her classmates use AI extensively — more extensively than she does. A few have told her they submit AI-generated work routinely without modification. If Priya stops using AI and they do not, she is disadvantaged. But if everyone uses AI, what is the point of the assignment?

The development question: Priya chose political science because she wants to work in policy. Policy analysts need to write clearly, argue persuasively, and think independently. If she outsources those skills to AI now, what happens when she enters the workforce and her job requires her to do them on her own?

The Turning Point

Two weeks later, Priya attends a workshop on "AI and Academic Integrity" hosted by the university's Center for Teaching and Learning. The facilitator — a professor of computer science — says something that sticks with her: "The question is not whether AI can write your paper. Of course it can. The question is whether writing the paper is the point, or whether learning to write the paper is the point. If it's the latter, AI cannot do it for you — because the learning happens in the struggle."

The facilitator introduces the concept of a personal AI policy and challenges every student in the room to write one. Priya takes this seriously. Over the following weekend, she drafts her policy:

Priya's Personal AI Policy (First Draft)

  1. I will use AI for understanding, not for producing. I can ask AI to explain a concept, but I will not ask it to produce work I submit as my own.

  2. I will use AI after my first attempt, not before. I will try to draft, outline, or solve the problem on my own first. If I am stuck, I can use AI to get unstuck — but only after I have genuinely tried.

  3. I will protect my core skills. Argumentative writing, policy analysis, and statistical reasoning are skills I need for my career. I will not outsource them to AI, no matter how busy I am.

  4. I will always disclose. If I use AI in any academic work, I will note how I used it, even if the syllabus does not require it.

  5. I will not enter personal or sensitive information. I will not paste classmates' work, private conversations, or identifying information into AI tools.

  6. I will revisit this policy every semester. As AI tools change and I develop new skills, my policy should evolve too.

The Test

Priya's policy is tested almost immediately. The following week, she has another policy brief due — this time on immigration reform. She is again pressed for time, though not as severely as during "the week from hell."

She opens her chatbot, then pauses. She looks at her policy. Rule 2: try it yourself first.

She spends 45 minutes writing a rough outline based on her class notes and two articles she has read. It is messy and incomplete, but it is hers. Then she opens the chatbot and types: "I am outlining a policy brief about immigration reform in the United States, specifically the economic arguments for and against expanded work visas. Here is my rough outline: [pastes outline]. What important arguments or counterarguments am I missing?"

The chatbot suggests three points she had not considered: the impact on domestic wages in specific sectors, the fiscal contribution of visa holders through taxes, and the brain-drain concerns in sending countries. Priya looks up each point, finds two of them well-supported and one overstated. She incorporates the two strong points, in her own words, with sources she found herself.

She gets an A-. More importantly, she feels like she earned it.

Discussion Questions

  1. The Line: Was Priya's first use of AI (the renewable energy policy brief) academically dishonest? If her university's policy does not specifically mention AI, does that change your answer? What about the fact that her professor's syllabus was silent on the issue?

  2. The Fairness Problem: Priya knows other students use AI more extensively. Is this a valid reason for her to use it too? How should universities handle the uneven adoption of AI tools?

  3. The Second Attempt: Priya's approach to the immigration policy brief involved using AI after her own first attempt, for feedback rather than generation. Is this meaningfully different from her first approach? Where would you place each attempt on the spectrum from Section 14.4?

  4. Policy Critique: Read Priya's six-rule personal AI policy carefully. Are there gaps? Are any rules unrealistic? What would you add, remove, or modify?

  5. The Bigger Picture: Priya's personal AI policy is an individual solution to a systemic problem. What institutional changes — from universities, professors, or policymakers — would make individual policies less necessary?

  6. Your Policy: After reading Priya's story, draft or revise your own personal AI policy. How does it differ from hers? What does the difference reveal about your values, circumstances, and goals?