I recently listened to an episode of Derek Bruff’s Intentional Teaching podcast about an AI teaching fellows program integrated into a writing course at Boston University, and it really got me thinking about how we approach AI in education. Rather than policing its use, this program empowers both instructors and students to use AI responsibly and creatively. What stood out most was how students actually want to learn how to use these tools well — not to take shortcuts, but to strengthen their work. Below are a few key takeaways I thought were worth sharing.

AI Affiliate Program at Boston University (BU)

• Program structure: Undergraduate “AI affiliates” are paired with writing instructors to support the responsible and effective use of AI in writing courses.
• Mentorship model: Affiliates mentor both students and instructors in the use of AI tools — it’s framed as a collaborative “tools mentorship” rather than top-down training.

Creative Classroom Practices with AI

• Devil’s advocate mini-game: Students used AI to generate counterarguments to each other’s essays, promoting critical thinking and peer feedback.
• Elevator pitch strategy: Students asked ChatGPT to summarize their essays in 50 words — a way to check for clarity, coverage, and alignment with prompts or rubrics.
• Active engagement: These practices led to lively, interactive classroom sessions — showing that AI can boost rather than hinder engagement.

Navigating AI Stigma & Transparency

• Early over-policing: Initial institutional reactions (like bans) contributed to student reluctance and a culture of secrecy around AI use.
• Transparency challenge: Students want to use AI responsibly but fear judgment — this blocks learning opportunities and honest discussion.
• De-stigmatization through affiliates: Programs like the AI affiliate model help normalize thoughtful, transparent use and encourage deeper reflection on AI’s capabilities and limits.

Students’ Attitudes Toward AI in Writing

• Usage is low despite allowance: Even with permission to use AI for up to 50% of a submission, actual use hovers around 20%.
• Voice matters: Students prefer their own writing voice and often reject AI output if it feels generic — they want to produce quality work, not just finish faster.
• Intrinsic motivation: High tuition and personal goals mean students are motivated to improve, not cut corners.

AI Beyond Writing: Curricular Implications

• Multifunctional tool: Students are using AI not just for writing, but to study, clarify ideas, and explore content — it’s becoming a general learning aid.
• Need for disciplinary integration: As AI becomes more relevant across fields, each discipline should consider what AI literacy means in its context.
• Skill evolution & career readiness: Students worry their pre-AI skills are becoming outdated — they want to graduate ready to talk about AI in their future fields.

To hear more from a master lecturer and student AI Affiliate discussing their experiences with and approaches to integrating AI into the classroom at Boston University, please listen to this episode of the Intentional Teaching podcast: AI Teaching Fellows with Christopher McVey and Neeza Singh!