This past week, I had the privilege of working the faculty from the School of Arts, Sciences, and Education at Ivy Tech Community College in Bloomington, co-leading a workshop on generative AI. I was especially grateful to my “partner in crime” (co-lead) Anne Leftwich, as we worked with the faculty to explore the opportunities and challenges of becoming AI-ready mentors in higher education … and to help situate the urgency of the matter.

What We Explored Together

Our workshop invited faculty to consider not only how AI is changing learning and teaching, but also how they might guide students to navigate these shifts responsibly.

One of the most striking trends we shared with them is the rapid pace of AI adoption among students. According to recent studies:

• 92% of undergraduates now use AI tools regularly — up from 66% just a year ago (HEPI).
• 88% of students use AI for assessments—a dramatic increase from 53% in 2024 (HEPI).
• Tools like ChatGPT, Gemini, Copilot, and embedded courseware AI are becoming part of the daily workflow for many students (and it doesn’t appear to be slowing anytime soon).

By contrast, faculty adoption looks very different:

• While most have experimented with AI, nearly 88% use it minimally in instruction (Digital Education Council).
• Only 28% of institutions have a formal AI policy, though another 32% indicate they are developing one (Inside Higher Ed – CIO Survey).
• And faculty (a clear majority of faculty anyways) express uncertainty about how to address ethics, reliability, and pedagogy when it comes to AI.

This gap between student adoption and faculty readiness highlights an urgent need: educators must not only understand the tools, but also help students use them wisely, ethically, and effectively. In other words, it’s not just about being AI-ready—it’s about becoming AI-enabled mentors. 

Inside the Workshop: Building GenAI Literacy

To bridge this gap, our workshop focused on not only contextualizing the higher education / GenAI landscape, but equipping faculty with practical strategies and a starting point for their GenAI literacy journey. Key elements included:

• Prompt Engineering & Best Practices – We provided frameworks and examples to help faculty design effective prompts, demonstrating how small changes in wording can dramatically impact outcomes.
• Hands-On Activities – Faculty had opportunities to experiment with generative AI tools in real time, testing use cases relevant to their disciplines.
• Collaborative Challenges – We presented scenarios for faculty to work through both individually and in groups, encouraging them to grapple with ethical, pedagogical, and practical questions.

The emphasis was not on mastering a tool, but on building confidence, curiosity, and critical thinking skills that will enable faculty to engage with AI thoughtfully and effectively.

As is nearly always the case when delivering a talk and/or leading a workshop, I once again left inspired by the Ivy Tech faculty, who were committed to leaning into these things together (even if leaning with noticeable hesitation).

It is moments of shared exploration like these workshops that remind me that the future of higher education will be shaped not by the tools themselves, but by the educators and learners who imagine new ways to use them—and to do so responsibility and with intentionality.

A heartfelt thank you to Dean Annie Gray for the invitation, to the faculty who engaged so fully, and to Anne Leftwich!