Beyond Academic Integrity: While cheating remains a concern, the conversation is shifting toward AI literacy as an essential learning outcome to prepare students for an AI-integrated workforce.
The “Calculus” of Cheating: In high-stakes environments, students often feel a competitive disadvantage if they don’t use AI.
Pedagogical Transparency: If faculty ban AI for specific assignments, they must explain the “why” (e.g., building foundational skills) to encourage student compliance
Backward Design: Eddie advocates for starting with the desired learning outcome and engineering assignments and instruction from there.
Learning to Write vs. Writing to Learn: AI’s role should differ based on whether the goal is mastering writing mechanics or using writing to process course content.
Durable Skills: While technical skills like prompt engineering may change quickly, mindsets like metacognition and critical thinking remain essential.
“Ground Truth” Bots: Using tools like NotebookLM or Small Language Models (SLMs) allows students to interrogate specific, vetted data sets like OER textbooks.
Efficiency vs Engagement: The episode concludes with a look at the “Efficiency vs. Engagement” binary. While institutions may use AI to automate grading and increase class sizes, the real opportunity lies in reinvesting saved time into “signature pedagogies”—mentoring and fostering a sense of student belonging, which are the greatest predictors of student success.
Quotes:
“The one who does the work is the one who does the learning. How do we make sure our students are doing the work, because that’s where the learning occurs?” — Eddie Watson
Timestamps:
00:00 – Introduction & Welcome Back
00:55 – The Innovation Cycle: Second Edition of “Teaching with AI”
01:41 – Eddie Watson’s Background & Role at AAC&U
03:32 – The Shift: From Academic Integrity to the World of Work
05:10 – Complexity of Academic Integrity & Student Pressures
07:42 – Evolving Assessment Strategies & Motivation to Cheat
10:55 – Backward Design: Aligning AI with Learning Outcomes
12:54 – Writing to Learn vs. Learning to Write
14:43 – Agentic AI & Modernizing Assessments
18:50 – Creating “AI-Resistant” vs. AI-Transparent Assignments
24:43 – Developing a Meta AI Literacy Model
28:00 – Durable Skills: Metacognition & Managing AI
33:50 – Custom Chatbots, SLMs, and Ground Truths
46:40 – The Future: Efficiency vs. Engagement
49:00 – The Human Element: Mentorship & Student Belonging
51:00 – Closing Remarks
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