Over the past several months, in addition to the full slate of GenAI innovation and leadership work I’ve been doing at IU, I have had the opportunity to speak with a wide range of audiences about generative AI, education, writing, rhetoric, and the changing nature of work and learning. These conversations have taken me from campus visits and professional development sessions to public forums, parent education events, and community learning spaces.

I have been grateful to join colleagues and communities at the University of Dayton, the MCCSC Parent University event here in Bloomington, South Harrison Community School Corporation, the folks at Arkansas State University–Beebe (which I previously blogged about), and other gatherings scattered throughout that focused on what generative AI means for teaching, learning, communication, and everyday life. I also had the pleasure of participating in a public debate with Northern Arizona University Professor (and IU Alum) Ira Allen here at IUB talking about Rhetoric and AI–a conversation that pushed into some of the deeper questions surrounding language, persuasion, authorship, and human judgment in an age of increasingly capable AI tools.

Each of these events has been different. Some have focused on educators and instructional design. Others have centered parents, community members, administrators, faculty, staff, or students. Some have been highly practical: How do I use these tools? What policies should we consider? How do we prepare students? Others have been more philosophical: What does it mean to write in the age of AI? What counts as learning? How do we preserve human agency, voice, and judgment?

Across all of them, one theme has remained constant: people are not simply asking what AI can do. They are asking how we can use it well…and often: should we use it at all?

These questions were at the heart of my presentation this morning at Indiana University’s Mini University, hosted in the Indiana Memorial Union. Mini University brings together IU alumni and lifelong learners who return to campus for a series of classes, conversations, and intellectual experiences. It is one of those programs that beautifully reflects the spirit of a university: curiosity, community, conversation, and the belief that learning does not end with a degree.

My session was actually meant to be led by Anne Leftwich, but I had the privilege of filling in at the last minute. An so I designed it as a practical introduction for a broad audience. Participants ranged from recent alumni to long-time friends of the university, and they brought with them a wide range of experience with AI tools. Some had used ChatGPT, Copilot, Claude, Gemini, or similar tools. Others were just beginning to explore what generative AI is and why it matters.

Rather than begin with the technology itself, I framed the session around three introductory orientations to how people can think about using generative AI in their lives:

AI as a productivity partner

First, we explored AI as a productivity partner: a tool that can help us plan, draft, organize, summarize, and move from a messy idea to a useful first version.

This is often where people first see the practical value of generative AI. It can help plan a day trip, draft an email, turn notes into a checklist, organize a family event, generate meal ideas, or prepare for a meeting. The point is not that AI produces perfect results. It does not. The value is that it can help us get started, reduce friction, and create something we can revise.

In this part of the session, I introduced a structured prompting approach I call ROCKIT:

• Role
• Objective
• Context
• Knowledge
• Information / Insights
• Task

The basic idea is simple: if we want better outputs, we need to give AI a clearer sense of the situation. Instead of asking, “Can you help me write an email?” we can tell the tool who it should act as, what we are trying to accomplish, what context matters, what information it should use, and what task we want it to complete.

Generative AI works best when we do not treat prompting as a magic phrase. It works best when we treat it as communication.

AI as a thought partner

Second, we considered AI as a thought partner.

This is a deeper and, in many ways, more interesting use. AI can do more than produce text quickly. It can help us think through decisions, surface assumptions, compare tradeoffs, prepare for difficult conversations, and explore ideas from multiple perspectives.

For this, I introduced the idea of the Socratic collaborator. Instead of asking AI to immediately give an answer, we can ask it to ask us questions first.

For example:

I want help thinking through a decision. Do not give me advice yet. Ask me one question at a time to understand my goals, constraints, concerns, and values. After five questions, summarize what you have learned and identify the main tradeoffs.

This shifts the interaction. AI becomes less of an answer machine and more of a structured conversation partner. It can help us slow down, clarify what matters, and consider angles we may not have seen.

Of course, this does not mean AI should make the decision for us. In fact, one of the central messages of the session (and all my work in and around AI, really) was that we should not outsource judgment. The goal is not to let AI think for us. The goal is to use AI to support better thinking.

AI as a learning partner

Third, we explored AI as a learning partner.

This may be the most underrated and yet powerful use of generative AI for lifelong learners. AI can explain difficult concepts, generate analogies, create practice questions, quiz us, give feedback, suggest next steps, and help turn curiosity into a learning plan.

In the session, I emphasized the difference between using AI to produce and using AI to learn. “Write this for me” is different from “Teach this to me” or “Help me develop a pathway to learn more about this.” “Give me an answer” is different from “Ask me questions and check my understanding.”

A basic learning-focused prompt might look like this:

Teach me the basics of {TOPIC X} as if I am a curious adult learner. Start with a plain-language explanation, give me an analogy, identify five key ideas, and then ask me three practice questions to check my understanding.

From there, the learner can continue:

Here are my answers. Give me feedback on what I understand, what I misunderstand, and what I should review next.

This kind of interaction (reductive here as it may be) points toward one of the most promising possibilities for generative AI: not replacing learning, but making learning more active, responsive, and personal.

We also discussed the importance of verification. AI can be helpful, but it can also be wrong. It can invent details, overstate confidence, or produce sources and claims that need to be checked. So I encouraged participants to build a verification habit into their AI use: ask what claims need to be checked, seek sources that can be inspected, compare important information against trusted references, and always consider the stakes of the task.

The best AI users are not the people who trust these tools the most. They are the people who know how to guide, question, revise, and verify them.

A semester of AI conversations

As I look back on this recent series of talks and workshops, I am struck by how quickly the conversation around generative AI is maturing.

A year or two ago, many sessions began with surprise: “Look what this tool can do.” That sense of wonder is still present, and in many ways it should be. These tools are remarkable. But increasingly, the conversation has moved toward judgment, purpose, and practice.

Educators are asking how to design meaningful learning in a world where AI can generate polished text. Parents are asking how to help young people use these tools responsibly. Professionals are asking how AI might change communication, productivity, and decision-making. Lifelong learners are asking how AI can help them explore new topics, understand complex ideas, and stay intellectually engaged.

These are the right questions.

Generative AI is not one thing. It is not simply a shortcut, a threat, a tutor, a writing machine, or a productivity app. It is a new kind of cognitive tool, and like all powerful tools, its value depends on how we use it.

My hope, across these talks and workshops, is to help people move beyond both hype and fear. We need clear-eyed, practical, human-centered ways of engaging with AI. We need to understand what these tools do well, where they fail, and what responsibilities remain with us.

Most of all, we need to keep people at the center: their judgment, creativity, ethics, relationships, and purposes.

This morning’s Mini University session was a reminder of why this work matters. A room full of curious learners, interested, engaged, and asking important questions to better understand the current AI-enabled worlds in which we seem to operate.