AI Literacy for Higher Education Faculty

This project addresses the critical challenge of cognitive offloading and academic integrity in higher education as Generative AI (GenAI) becomes a primary tool for student productivity. Designed for university faculty, the initiative moves beyond traditional surveillance-based detection to focus on intentional assessment redesign.

The core of the intervention is a four-phase curriculum built on a tri-lens theoretical framework:

• Conceptual Change Theory: Utilizing "Cognitive Dissonance" through live AI failure demonstrations (e.g., forced hallucinations) to dismantle outdated mental models and create a felt need for new pedagogical strategies.

• Social Constructivism: Facilitating "Collaborative Sense-Making" via gallery walks and peer-to-peer policy drafting, allowing faculty to co-construct institutional norms.

• Metacognitive Scaffolding: Implementing structured "Journal Reflections" and "Synthesis Worksheets" that force faculty to isolate high-order human cognitive processes—such as judgment and contextual evaluation—that AI cannot reliably replicate.

The project delivered a full institutional "plug-and-play" suite, including a Strategic Rationale Report, Facilitator Field Guides with scripted talk-tracks for handling faculty skepticism, and Interactive Participant Worksets. This comprehensive approach ensures that the intervention is not just a technology tutorial, but a research-grounded blueprint for sustainable institutional transformation.

Why AI Literacy for Professors?

Higher education faculty often enter the AI conversation with strong but conflicting intuitions—ranging from high anxiety to over-optimism. The core problem identified in this project was not just "learning how to use tools," but re-evaluating what "academic integrity" means when cognitive offloading becomes effortless.

The Goal: Move faculty from a mindset of detection and prevention to one of intentional assessment redesign.

From Awareness to Institutional Transformation

I architected a modular, four-workshop series designed to lead faculty through a psychological and pedagogical shift:

1. Workshop 1: Foundations & Reflections – Surfacing mental models and confronting the "hallucination" risks of LLMs through live demonstrations.

2. Workshop 2: Assessment Redesign – Moving from "AI-vulnerable" to "AI-resilient" tasks.

3. Workshop 3: Policy & Communication – Developing transparent classroom frameworks (Green/Yellow/Red zones) for AI use.

4. Workshop 4: Equity & Transformation – Addressing the digital divide and ensuring AI doesn't widen existing educational inequalities.

Turning Research into Experience

To engage a skeptical academic audience, the design utilized Conceptual Change Theory. I didn't just tell faculty that AI is unreliable; I designed experiences where they could see it fail in real-time.

• The "Dolphin" Experiment: A live demo where AI is prompted to explain why "dolphins are fish."

• The "Coffee" Hallucination: Forcing the AI to generate fake peer-reviewed citations to prove coffee cures anxiety.

• The "Human-in-the-Loop" Worksheet: A structured reflection tool that forces participants to identify which cognitive processes (e.g., evaluation, judgment, contextual application) AI cannot replace.

This project provides a "plug-and-play" solution for university departments, featuring a full library of implementation materials.

05. Key Insights & Professional Takeaways

• Assessment as the Anchor: AI isn't a threat to education; it’s a threat to outdated assessment. By anchoring the workshop in "Assessment Redesign," we transform faculty anxiety into creative agency.

• Psychological Safety: Workshop 1 focuses heavily on "surfacing intuitions." Before faculty can learn new tech, they must feel that their fears about academic integrity are heard and normalized.

• Scalability via Documentation: By creating a robust Facilitator Guide, this project ensures that the intervention can be scaled across an entire university without losing the nuances of the pedagogical research.