AI in the Classroom: Home

An important empirical study capturing student perspectives. It validates the usefulness of AI for brainstorming and overcoming writer’s block, while confirming students still value human feedback for in-depth learning. Supports integrating AI as a process aid.

Provides a roadmap for institutional policy. It argues that successful AI integration requires a transparent, inclusive process involving both faculty and students to set clear, ethical guidelines, ensuring institutional buy-in and legitimacy.

A foundational text for understanding the broader ethical landscape. It helps faculty move beyond plagiarism to address crucial issues like bias in LLM data sets, data privacy, and the moral responsibility of preparing students for an AI-integrated professional world.

Direct evidence countering the "critical thinking" anxiety. This study demonstrates that AI-generated feedback can significantly improve students' ability to refine their argument coherence, evidence quality, and rhetorical strategies, reframing AI as a tool for critical revision.

Offers a practical framework to manage AI use. It categorizes LLM engagement into five defined levels (from proofreading to full drafting) with suggested permissions and prohibitions, helping faculty establish clear, enforceable boundaries for specific assignments.

A study arguing that AI can be a positive force for creativity. It suggests that AI use significantly enhances student creativity when instructors focus on student engagement and explicitly teach AI literacy (the competence to use and evaluate AI tools).

Explores the emerging "human-AI collaboration" model, advocating for pedagogy that teaches students how to be effective managers and collaborators with AI tools, viewing the final work as a joint product that still requires human intellect to steer.

A high-level governmental report that encourages educators to leverage AI for equity and personalized learning by focusing on AI's ability to provide instant, individualized feedback. It acts as an institutional call for proactive, thoughtful integration.

A broad review of recent literature concludes that when used correctly, AI tools demonstrate promise in enhancing writing abilities, boosting student confidence, and increasing productivity. It provides a strong evidence-based endorsement for responsible integration.

This working paper is for faculty development. It categorizes effective ways to incorporate AI into assignments. The Mollicks organize methods into seven categories, ranging from "AI as a Tutor" to "AI as a Simulation," and provide specific prompts for each. The paper emphasizes a guided, transparent use of AI by illustrating how instructors can design activities where AI does the low-stakes, repetitive work (like summarizing or brainstorming), thereby freeing up student time for higher-order tasks (critical analysis, refinement, and ethical judgment).

A collection of ten data visualizations demonstrating how generative AI is shifting job roles, workflows, and required skills across professional industries. McLees argues that the primary impact of AI will not be replacement, but the delegation of low-value, repetitive tasks, freeing up human workers to focus on high-value, cognitively demanding tasks such as critical judgment, emotional intelligence, and complex problem-solving.