Educational Technology Fellows
The Ed Tech Fellows Program brings together instructors from across Northwestern to explore how emerging technologies are shaping teaching and student learning. During the 2026–2027 academic year, Fellows will focus on one central question: How is AI affecting your teaching and your students’ learning?
To investigate this question, Fellows will choose one of two pathways:
- Assignment Clinic Pathway: Design, test, implement, and evaluate assignments, activities, assessments, and AI policies with feedback from peers and TRAIL Guides. TRAIL Guides are undergraduate students trained in the fundamentals of generative AI and its impacts on teaching and learning.
- AI Teaching Playbook: Collaborate with colleagues, TRAIL Guides, and TLT staff to identify, document, and share effective approaches to teaching and learning in an AI environment. Through discussion, classroom experimentation, and reflection, participants will contribute examples, case studies, and practical recommendations to a growing AI Teaching Playbook for Northwestern instructors.
What Makes This Program Different?
The Ed Tech Fellows Program centers on teaching practice and student learning. Faculty will be able to choose between Assignment clinic or Playbook.
Participants will:
- Join a cross-disciplinary community of instructors from schools across Northwestern.
- Collaborate with TRAIL Guides who bring student perspectives on AI and learning.
- Participate in assignment clinics where activities, assessments, and AI policies can be tested with students outside their own courses.
- Implement and evaluate classroom activities throughout the academic year.
- Share findings with peers and contribute to a growing collection of teaching practices and resources.
- Help shape Northwestern's understanding of teaching and learning in an AI environment.
Option A: Assignment Clinics
Faculty interested in improving teaching practices through classroom experimentation will participate in Assignment Clinics.
Working with peers and TRAIL Guides, Fellows will:
- Test assignments, activities, assessments, and AI policies before implementation.
- Receive feedback from students who are not enrolled in their courses.
- Explore how students interpret assignment instructions and AI expectations.
- Revise course materials based on student and faculty feedback.
- Implement and evaluate classroom activities during the academic year.
This pathway is ideal for instructors who want to make practical changes to their courses and gather evidence about what supports student learning in an AI-rich environment.
Program Deliverable
- A revised or newly developed assignment, activity, assessment, or AI-related teaching intervention.
- Documentation of implementation and student feedback.
- A brief reflection on lessons learned.
Option B: AI Teaching Playbook
Faculty interested in documenting and sharing effective teaching practices will participate in the AI Teaching Playbook Working Group.
Working collaboratively with colleagues, TRAIL Guides, and TLT staff, Fellows will:
- Collect examples of AI-related teaching practices from across Northwestern.
- Identify strategies that support learning, engagement, and student agency.
- Share successes, challenges, and lessons learned from classroom experimentation.
- Contribute examples, reflections, and recommendations to a growing AI Teaching Playbook.
- Help build a practical resource that instructors can use when making decisions about AI in their courses.
The AI Teaching Playbook will serve as a living collection of teaching strategies, assignment examples, policy language, student insights, and faculty experiences that can support instructors across Northwestern.
This pathway is ideal for instructors who enjoy reflecting on their teaching, sharing practices with colleagues, and contributing to resources that can benefit the wider teaching community.
Program Deliverable
- Contributions to the Northwestern AI Teaching Playbook.
- At least one teaching artifact, case study, classroom example, reflection, or set of recommendations informed by classroom experience.
- Participation in discussions and synthesis activities that help identify promising practices for teaching and learning with AI.
Program Structure
Program Dates: All meetings will be held on Fridays from 11:00 AM to 12:00 PM.
|
Date |
Time |
Focus |
Deliverable |
|
Friday, November 6, 2026 |
11:00 AM-12:00 PM |
Explore & Launch |
Identify an AI-related teaching question or challenge and select a pathway. |
|
Friday, December 4, 2026 |
11:00 AM-12:00 PM |
Build & Design |
Draft an assignment, activity, policy, case study, or Playbook contribution with feedback from peers and TRAIL Guides. |
|
January 2027 |
Independent Work |
Implement Activity A |
Pilot a classroom activity or collect initial examples, reflections, and student feedback. |
|
Friday, February 5, 2027 |
11:00 AM-12:00 PM |
Reflect & Refine |
Share preliminary findings and revise plans based on implementation results. |
|
Friday, March 5, 2027 |
11:00 AM-12:00 PM |
Share Findings |
Present outcomes, identify challenges, and discuss emerging themes across disciplines. |
|
Friday, April 16, 2027 |
Independent Work |
Synthesize & Prepare for TEACHx |
Finalize teaching artifacts, Playbook contributions, and key insights. |
|
May 2027 |
TEACHx |
Community Showcase |
Share lessons learned through a panel, workshop, poster, or interactive session. |
How to Apply
No prior expertise in AI is required. Curiosity, reflection, and a willingness to experiment are the most important qualifications. We welcome instructors from all Northwestern schools and disciplines who are interested in:
- Understanding how students are engaging with AI.
- Exploring new approaches to teaching and assessment.
- Learning alongside colleagues from across the university.
- Experimenting with small, meaningful changes in their courses.
- Contributing to a campus-wide conversation about teaching and learning.
Apply by October 23.