AI Teaching & Learning Collaborative

Generative AI is changing quickly, creating both opportunities and challenges for teaching and learning. As the technology continues to evolve, faculty need ongoing opportunities to learn from one another and adapt their teaching.

Faculty adaptation to generative AI is not just about learning the technology. It also happens through conversation, observation, experimentation, and the gradual development of norms. The AI Teaching & Learning Collaborative is designed as a context-specific and ongoing faculty peer-learning initiative, creating a sustained space for faculty to explore, experiment, share what works and what doesn’t, and learn together as the technology continues to evolve.

We welcome SPH faculty from all disciplines, teaching styles, and levels of AI experience. No AI expertise is required.

How It Works

The Collaborative brings together two connected components:

Faculty Learning Community
Faculty come together regularly to exchange experiences, questions, challenges, and practical approaches to teaching in a rapidly changing AI environment. The emphasis is on peer learning, thoughtful experimentation, and learning from both successes and challenges.

AI Teaching Commons
A growing collection of practical resources, examples, and approaches contributed by SPH faculty. The Teaching Commons extends learning beyond the meetings and allows faculty experiences and ideas to benefit the broader SPH community.

Together, the Faculty Learning Community and AI Teaching Commons create a shared learning system in which faculty experiences inform the broader community, while new ideas and resources support continued experimentation and learning.

What We Hope to Accomplish

Through the Collaborative, we aim to:

• Build faculty capacity to continually adapt teaching as AI evolves.
• Create opportunities for faculty to learn from one another across disciplines and educational contexts.
• Encourage thoughtful experimentation and sharing of both successes and challenges.
• Develop practical resources and examples grounded in faculty experience.
• Foster shared norms and effective educational practices for an AI-enabled world.

Example Learning Topics

The Collaborative will explore foundational questions facing faculty while remaining responsive to new issues and participant interests as AI evolves. Topics may include:

Additional topics will emerge from participants’ questions, experiences, and evolving developments in AI throughout the year.

AI Teaching Commons

The AI Teaching Commons will launch in September as a growing resource for SPH faculty, extending the learning of the Collaborative beyond individual meetings by capturing and sharing what faculty are learning and trying in their teaching.

The Commons will include practical resources contributed by faculty, such as syllabus language, assignments, classroom activities, prompts, and assessment approaches, along with selected lessons and insights emerging from Collaborative discussions. Over time, it will create a shared resource so that faculty across SPH can benefit from the experiences and learning of their colleagues.

2026–2027 Learning Community Meetings

The Learning Community meets throughout the academic year on Thursdays from 1:00–2:00 PM. Meetings are interactive and discussion-based, with participants sharing questions, experiences, approaches, and lessons from their own teaching. Sessions may include brief faculty examples of what they have tried, facilitated discussion of what is working and what remains challenging, and opportunities to identify practical lessons and resources to share through the AI Teaching Commons. Peer learning and exchange are at the center of each session. Lunch is provided for participants who register in advance.

Interested in Participating?

The Collaborative welcomes faculty who are curious about AI in teaching, whether you’re just beginning to explore it or are already experimenting with AI in your courses. Faculty are welcome to join at any point during the academic year; participation does not require a full-year commitment. Participants are encouraged to bring their questions, experiences, and ideas and contribute to the shared learning of the community.

Faculty can register for upcoming Learning Community meetings here. For questions or additional information, please contact chdatascience@bu.edu.