AI Fellows: A Distributed Knowledge Approach to Advancing Responsible & Ethical Adoption of AI in Teaching
Description
Domain:
Faculty Development & Capacity Building
Challenge Area:
Leadership and Change Management
Status:
Established Best Practice (validated and replicable practices)
Implementation Complexity:
High
The AI Fellows program is a year-long, cohort-based faculty development initiative designed to advance the ethical and responsible adoption of generative AI in teaching and learning across Northeastern University. Rather than concentrating expertise in a single unit, the program adopts a distributed knowledge approach in which faculty participants develop their own AI-enabled teaching practices and act as catalysts for change within their colleges.
The teaching and learning center served as a hub and connector, providing facilitation, scaffolding, coaching, student support, and resources while enabling Fellows to lead conversations, initiatives, and practice-based experimentation within their local contexts. Monthly cohort meetings emphasized community building, modeling, sharing, and reflection, while partner meetings between sessions provided individualized engagement. This approach expanded institutional capacity while supporting deep, context-specific learning for participants.
Practical Implementation
The program has been implemented twice, with two cohorts of faculty Fellows (nine participants in the first cohort and nineteen in the second), selected by College administrators. Each cohort was supported by two to three primary faculty developer facilitators, four to five graduate instructional assistants, and additional faculty developers who mentored the student assistants.
Fellows participated in monthly whole-group meetings focused on generative AI in teaching, faculty readiness for adoption, ethical considerations, and strategies for engaging colleagues. Meetings combined information sharing, breakout discussions, and structured brainstorming, with session plans developed close to meeting dates to remain responsive to participant needs.
Between monthly meetings, Fellows engaged in individualized coaching sessions with assigned faculty developers and worked as needed with graduate instructional assistants. MS Teams and Canvas were used for discussion, coordination, and resource sharing across program runnings.
Each Fellow was required to produce two core deliverables:
(1) documentation of an individual AI-enabled teaching practice, including implementation process, results, and reflection; and
(2) a plan for engaging colleagues in AI-related teaching conversations, accompanied by documentation of activities, outcomes, and reflections.
Individual teaching practices varied widely across disciplines and leveraged generative AI for instructional planning or student learning. These practices were published in the Center’s AI Gallery to provide models and inspiration for other educators. Engagement activities led by Fellows included workshops, webinars, working groups, presentations, one-on-one consultations, and development of websites. Fellows received a stipend upon successful completion of program requirements.
Beyond cohort activities, the program contributed to broader institutional infrastructure through committees and focus groups. Outputs included an online library of guides and tips for educators, a recommended process for AI readiness and curriculum integration informed by the DEC AI Literacy Framework, and university-wide standards and recommendations for the use of generative AI in teaching and learning.
Impact Measurement
The impact of the AI Fellows program was assessed using multiple measures focused on learning, leadership development, and institutional influence. All participants completed a program evaluation survey combining Likert-scale items and open-ended questions addressing personal learning, goal achievement, and the effectiveness of program components.
Facilitators also tracked the number and types of engagement activities implemented by Fellows, as well as participation levels in those activities. Qualitative reflections submitted by Fellows were analyzed to identify lessons learned, evidence of professional growth, and indicators of pride and ownership in their leadership roles.
Together, these measures demonstrate the program’s effectiveness in developing faculty AI expertise, expanding institutional capacity through distributed leadership, and strengthening infrastructure for responsible AI adoption across teaching and learning.
Enablers
- Institutional leadership and college-level nomination
- Teaching and learning center as coordinating hub
- Year-long cohort structure
- Faculty developer and graduate assistant support
- Faculty stipends
- Shared templates and documentation
- Alignment with institutional AI standards
- Central collaboration platforms
Links / Resources
- Write-up of the Practice (formatting intact):
https://docs.google.com/document/d/1ZoRwqM_wsPSBph8qXa5prF8UsF1gvcFB3JS5K_0kPTg/edit?usp=sharing - Faculty Fellows Flow Diagram:
https://drive.google.com/file/d/1MEbfnmxaA7vUodebiFgxnIkHv5PcX7mH/view?usp=sharing - About the Initiative:
https://learning.northeastern.edu/ai/about-the-initiative/ - Profiles of Previous AI Fellows:
https://learning.northeastern.edu/about/previous-ai-fellows/ - AI Gallery:
https://learning.northeastern.edu/ai/gallery/ - Tips and Tutorials for Educators:
https://learning.northeastern.edu/ai/tips-and-tutorials/for-educators/ - University-wide Standards and Recommendations for Generative AI:
https://provost.northeastern.edu/wp-content/uploads/2025/07/Standards-and-Recommendations-for-the-Use-of-Generative-AI-in-Teaching-and-Learning-at-Northeastern-FINAL-07.01.25.pdf