How good (or bad) an influence is GenAI on our society and culture? In most important ways, the jury is still out. These are open questions for lively debate, and cogent and reasonable arguments are being made. Most of these debates concern macro-level issues affecting large populations, and it’s not always easy to determine how any individual should respond. Rather than offering answers, this page offers constructive questions as points of departure for an ongoing conversation.
It bears repeating (and we will): These are open questions! As you ask them, keep an open mind, and be ready to go “off script” and follow the conversation where it takes you. Your students’ answers may surprise you.
How are we debating GenAI Ethics?
Last year, our colleague Jason Johnston pointed out that most GenAI ethical conversations do not examine the ethical framework they use—and therefore we tend to default to a form of utilitarianism, where “usefulness” is the underpinning ethical standard. Here, we invite you to start a conversation about what we mean by “ethics” and what systems we use in debates about GenAI.
- Starter Questions: This document suggests some useful ways to begin a conversation about ethics, to ground further debates on the issues below.
- AI & Me: This workshop series, starting in spring 2026, includes Dr. Johnston’s presentation, in which he and others propose alternative ethical frameworks to utilitarianism.
What is the environmental impact of GenAI?
GenAI systems use energy directly and indirectly, and their data centers consume water to cool vast server farms. But is the use of GenAI really the biggest drain on these resources?
- Starter Questions: This document offers useful ways to start a conversation about environmental issues and GenAI. It includes some evidence-based information, but the situation is changing rapidly, and it may be more productive to consider how we accept or defer responsibility for large-scale socio-economic activity.
- What Uses More: This resource is based on the best existing data about how resource-intensive digital technologies are. It lets you and your students compare different ways of using GenAI—and other kinds of digital technology, like streaming video or collaborative meetings.
Was GenAI designed ethically, and if not, what should we do about that?
In brief, all of the major GenAI creators engaged in a race to build a functional product, and in their hurry, they cut corners ethically, morally, and legally. Now everyone is using their products daily. How should we think through their obligations and ours here?
- Starter Questions: This document suggests some useful ways to start a conversation about how to regard a widely used product created under dubious circumstances.
- Background Resource: A New York Times podcast explored the origins of GenAI LLMs.
- Additional Support: The staff members at the Judith Anderson Herbert Writing Center have in-depth experience with questions about originality in writing and offer consultations with faculty.
To what extent will GenAI benefit or harm our society and culture?
Most technologies can cause both benefit and harm, but not always in equal measure. Some of the earliest predictions—that GenAI will lead to immediate and catastrophic job loss, for example—do not appear to be happening, but it’s an open question whether GenAI will, in balance, make things better or worse.
- Starter Questions: This document suggests useful ways to start a conversation about the impact of GenAI, beginning right here, right now, in UT classrooms.
- Sample Resource: The IMPACT RISK framework, created by Jon Ippolito, explores potential negative impacts GenAI could have on society and culture.
How can I prohibit GenAI in my class?
The short answer is, it’s not easy, and depending on how you do it, the results may not turn out the way you hope. While we should help students do the work they need to learn (rather than letting GenAI do it for them), flat-out prohibitions often do more harm than good.
- Use Role-Based Framing: Rather than issuing blanket prohibitions or permissions, show students what role GenAI should take on an assignment-by-assignment basis.
- Sample AI Assessment Scale: Furze et al. (2026) have devised a scale that offers a more nuanced alternative to the “stoplight” model (no AI / some AI when permitted / all AI).
- Assignment Permission Icons: Jason Johnston and the instructional designers at UTK’s office of Digital Learning have created simple icons to use with individual assignments, suggesting how GenAI may be used (or not).
- Clarify Roles: You may find it useful simply to stipulate what role GenAI is to play, based on a list created here at TLI in collaboration with faculty.
- Consider Course Culture and Climate: You don’t want to become the AI police—as you’ve no doubt already realized. Treating students as “potential cheaters” undermines a positive learning culture and climate, and can drive problematic behaviors underground, where you’ll always be guessing about them. But cultivating trust with students around how they do their work takes time, effort, and thought. TLI provides some resources on building trust here, and the Judith Anderson Herbert Writing Center can also help you think through managing trust with writing assignments.
- Use Detection Tools with Caution: In general, detection tools have shown only limited utility, as they frequently generate false negatives (misidentifying AI generated material as human in origin), and false positives (misidentifying student work, especially when edited with Grammarly, as whole-cloth AI creations). A detection tool can suggest an area of concern, but it’s up to you and your students to clarify expectations and set up ways for students to “show their work.”
- Additional Support: The staff members at the Judith Anderson Herbert Writing Center have in-depth experience with questions about originality in writing and offer consultations with faculty.