Office of the Provost

Faculty AI FELLOW

2026-2027

Program Overview

The Faculty AI Fellows program is a one-year, formative experience designed to equip faculty to generate insights and practical models for engaging with AI at Boston College. 

This initiative aims to support faculty development not only through thoughtful experimentation with AI and teaching, but also through innovative forms of collaboration and co-inquiry, whether with department colleagues, with other university partners, or with our students.

It also invites faculty to explore what formative AI pedagogy might look like at Boston College—using AI not merely for efficiency, but as a scaffold for student agency, ethical reasoning, personal reflection, and vocational discernment in ways that align with our Jesuit, Catholic mission.

The Faculty AI Fellows program is an opportunity to: 

  • Reflect on how AI affects you personally and professionally, and draft an approach to AI that can ground you during a time of rapid change.
  • Increase fluency with AI as an emergent design material—not just what it can do with it, but how to think about it as a teaching tool.
  • Build a teaching-related project that integrates AI in a way that fits your discipline and what you actually care about in your teaching.
  • Connect with others to extend your work and your learning beyond what you could do on your own. That could be colleagues in your department, collaborators in other disciplines, or even your students as partners.
  • Contribute to what BC knows about what works, what doesn’t, and what questions we need to keep asking.

The Faculty AI Fellow is an inaugural initiative of the AI Collaboratory, an emerging, university-wide effort sponsored by the Provost’s office to cultivate a shared culture of inquiry, experimentation, and reflection around AI and its implications for education at Boston College. The work of the Fellows will help shape the direction of the Collaboratory over the next year as it takes shape. 

Core Elements

The program is structured around three interconnected layers of inquiry that evolve over the year.

1. Reflective Experimentation: Who am I in relation to AI?

Before implementing tools in the classroom, faculty are invited to reflect on their own attitudes towards AI, whether as enthusiasts, people with significant critiques of AI, or somewhere in-between, and to explore their approach through training and hands-on experimentation. 

Goal: To move from second-hand, abstract views to an informed, personal position.

2. Pedagogical Experimentation: How does this change how I teach in my discipline?

Designing and testing discipline-specific interventions that prioritize “Capacity Extension” (scaffolding student effort) over “Capacity Replacement” (outsourcing student thinking).

Goal:To produce knowledge through experimentation (successes and failures) that can be shared with peers.

3. Collaborative Experimentation: How do we learn together?

Fellows will explore how to connect their individual insights to the wider community—whether through a departmental/disciplinary initiative, a cross-disciplinary partnership, co-inquiry with students, or simply sharing their story.

Goal: To act as a connective node in an emerging network of local expertise.

Program Timeline

The year is structured to provide a flexible runway for design and experimentation. While the phases generally align with the academic calendar, the progression is meant to provide flexible scaffolding for developing projects rather than a rigid sequence or timeline.

Summer 2026: Play & Reflection

Focus: Personal Formation & Fluency

This initial phase is meant as a sandbox for individual exploration and learning. Fellows are not expected to build a full project yet, but to develop AI fluency and draft a personal approach, grounded in hands-on experience.

Activities:

  • Hands-on Play: Workshops such as the AI Test Kitchen to explore tools beyond the basics (building custom agents, exploring RAGs, AI bot tone).
  • AI Position Statements: Fellows draft a short reflection on their evolving stance toward AI in their field.
  • Consultation: Individual meetings with CDIL/CTE staff to identify a potential focus area.

Outcome: 

A draft proposal for a pedagogical experiment (e.g., a specific assignment, a policy change, or a research question) and a personal statement about AI. 

Fall 2026: Design & Prototyping

Timing: Fall | Focus: Pedagogical Experimentation & Co-Design

This phase serves as a collaborative design studio. Fellows meet regularly to test ideas and think with each other to develop their project plans. 

Activities:

  • Prototyping: Developing and refining the specific intervention identified in Phase 1.
  • Testing: Implementing small-scale experiments in current classes (if appropriate) or testing ideas with colleagues.
  • Finding Partners: Fellows begin identifying a collaborator or collaborators for the next phase. This could be colleagues in their own department, partners from the working group/another discipline, or even their own students as co-inquirers. 

Outcome:

A finalized design for a collaborative experiment and a “Mid-Year Share-out” (sharing initial learnings).

Spring 2026: Engagement & Connection

Focus: Collaborative Experimentation & Storytelling

In this final phase, Fellows turn outward by exploring a collaborative experiment and sharing the narrative of what they learned from the year.

Collaborative Experiment Options:

  • Departmental: Launching a small pilot within their department (e.g., a faculty book club, a lunch and learn, or a shared assignment with a colleague).
  • Cross-Disciplinary: Partnering with a Fellow from another school/department to explore a shared theme (e.g., “AI & Ethics in Theology and Nursing”).
  • Faculty-Student Partnership: Engaging students as co-designers in a “students-as-partners” inquiry project to understand AI’s impact on their learning.

Sharing/Storytelling Options:

  • The Narrative: Presenting a “Story of Practice” at a university event (e.g., AI Lunch and Learn, Excellence in Teaching Day, or BC Talks AI).
  • The Resource: Writing a blog post, white paper, or brief strategy proposal for their Dean/Department Chair.
  • The Artifact: Creating a shared resource (e.g., a prompt library, a policy guide) for their discipline.

Outcome: 

A collaborative engagement and a shared artifact or narrative that contributes to the university’s shared knowledge around AI and learning.

Who We Are

The application process invited faculty who are:

  • “AI curious” (regardless of skill level) and willing to work with it hands-on
  • Willing to experiment and explore their identities as learners as well as teachers
  • People who are interested in experimenting with AI within and beyond our scheduled meeting times. We want you to come with things you are excited about exploring with a group also eager to explore
  • Open to sharing their work with the wider university

We also welcomed people are skeptical of AI but who open to engaging deeply with the technology to critique it from a more informed position.

Facilitation and Support

The Center for Digital Innovation in Learning will take the lead in facilitating and organizing the initiative but will be working in close collaboration with colleagues from the Center for Teaching Excellence throughout. 

The goal will be to help faculty better access the different, but complementary, resources each group offers, and explore ways our groups can coordinate our efforts in new ways. In particular, we’ll be looking for ways to connect the Faculty AI Fellows with the GenAI Cohort that the CTE will be facilitating at the same time. 

In addition to CDIL and CTE resources, we also will draw on partners around the university to provide training, consultation, and technical support with the goal of strengthening our collaborative connections to better support faculty in doing the same. 

Questions? 

Feel free to reach out to Tim Lindgren (timothy.lindgren@bc.edu) from CDIL with any questions.