Generative AI is accelerating a profound transformation in teaching, learning, and assessment. As AI systems become increasingly capable of producing essays, code, summaries, analyses, and other forms of academic work, institutions are confronting difficult questions about what assessment should measure, how learning should be demonstrated, and what forms of intellectual work remain most valuable in higher education.
For many faculty members, the rapid emergence of AI has destabilized long-standing assumptions about assessment. Some instructors have responded by adopting surveillance-oriented approaches to academic integrity, including the use of AI detectors and increasingly restrictive classroom policies. Yet the unreliability of detection technologies and the growing ubiquity of AI tools have exposed the limitations of enforcement-centered strategies while contributing to mistrust between faculty and students. At the same time, other instructors see generative AI as an opportunity to rethink assessment more fundamentally: shifting away from tasks centered on memorization and routine production toward assessments that emphasize critical thinking, creativity, reflection, collaboration, and authentic problem-solving.
To help institutions move from piecemeal adaptation to sustainable assessment transformation, Ithaka S+R is organizing a cohort of staff from centers of teaching and learning to develop practical and contextually relevant resources and strategies to support teaching in an AI-mediated learning environment.
Over the course of 12 months, participants will work with Ithaka S+R AI experts and researchers to investigate how AI impacts classroom assignments and assessments and how faculty can and should respond to the changing educational environment. Together, participants will build tools and develop long-term strategies for assessment innovation.
Ithaka S+R will guide the cohort through four distinct project phases. Each phase will result in specific milestones and moments for collective reflection, meaning-making, and adaptive planning.
Project phases
- Learning. In this phase, participants will focus on understanding how AI impacts classroom assessment, including the opportunities and challenges it presents for learning outcomes, assignment design, and evidence of learning.
- Data collecting. Participants will use research instruments co-created with Ithaka S+R to better understand instructor attitudes toward classroom assessment and engaging with faculty development.
- Design. Participants will design resources, including a campus-specific assessment diagnostic tool, based on the data previously collected.
- Sharing. Participants will share the resources they previously developed and consider how to implement the resources locally.
Activities in each phase of the project will be oriented towards expanding each participant’s capacity to meaningfully engage with instructors across the full spectrum of AI knowledge, personal use, and course integration.
How will it work?
Ithaka S+R will convene 15-20 centers of teaching and learning from a diverse range of institutional types. Each center will assemble a local project team of three to four participants drawn from center staff, academic affairs, instructional design, faculty leadership, and assessment support. Project team members should be interested in supporting instructors in the revision and implementation of assessment for course-based student learning. All meetings of the cohort will be held on Zoom (or Lumen Circles).
Project Timeline
Assessing Assessment (months 1-2)
The cohort will begin with an exploration of the rapidly evolving landscape of AI in teaching, learning, and assessment. Participants will examine how generative AI is reshaping assessment practices, student learning behaviors, faculty expectations, and institutional policy conversations.
At the initial kickoff meeting, participants will develop a shared vocabulary and conceptual framework for discussing AI-mediated learning and assessment transformation. Participants will also reflect on the state of AI adaptation at their own institutions and identify emerging opportunities and tensions.
Workshop 1: AI, Year Five: What is Coming Together, What is Falling Apart?
Participants will examine:
- how generative AI is changing student learning processes;
- the implications of AI for traditional assessment models;
- emerging instructor responses to AI;
- and the pedagogical assumptions underlying existing assessment practices.
Participants will share current institutional and instructor approaches, challenges, and experiments related to AI and assessment.
Campus output: Idea bank of recommended practices and activities from institutions across the cohort as well as nationally.
Workshop 2: Assessment in the Age of AI
Participants will explore established and emerging assessment redesign frameworks.
Participants will also consider how existing faculty development resources and institutional structures might be adapted to support assessment transformation efforts.
During this phase, each project team will begin developing a faculty outreach strategy designed to engage instructors at their institution, including any priority they want to bring to specific disciplines or departments, any particular instructor category (tenured, tenure-track, contingent, new hires, etc.), or level of AI engagement (enthusiastic, skeptical, etc.).
Campus Output: Instructor outreach and engagement plan.
Data Collection Phase (months 3-6)
In the second phase, participants will investigate how instructors across their institutions are experiencing the challenges and opportunities for student learning assessment created by generative AI.
Using research instruments and facilitation guidance co-developed by Ithaka S+R, campus teams will conduct semi-structured focus groups with faculty members from diverse disciplinary and pedagogical backgrounds. The goals of this research phase are to:
- better understand faculty concerns, aspirations, and barriers related to AI and assessment;
- identify unmet faculty support needs;
- and generate institution-specific data to inform redesign and implementation efforts.
Campus output: Data from semi-structured focus groups.
Design Phase (months 7-10)
Workshop 3: What Resources do Faculty Want?
Participants will draw on findings from their focus groups to consider specific support needs articulated by instructors at their institution and others to either identify existing resources that can provide this support or gaps that need to be filled through development of new resources.
At the conclusion of the workshop, teams will commit to repurposing or developing resources to support AI-ready assessment, a timeline for implementation, and an outreach plan to reach their identified instructor population.
Campus output: Menu of options of new or repurposed AI-ready assessment resources or programming.
Workshop 4: Making Learning Visible
Leveraging what they have learned from the instructor focus groups and each other, participants will begin to devise a campus-specific diagnostic protocol that incorporates elements of the previously developed menu of options. This diagnostic protocol is a tool that can be used within individual campuses to help instructors identify ways to make student learning visible, and therefore assessable, in ways that are robust to generative AI.
Campus output: Diagnostic protocol that can be used in individual consultations, group workshops, or in self-directed reflection.
Sharing Phase (Month 11-12)
The cohort will convene for a final time for an internal symposium to share outcomes from their AI-ready assessment resources or programming and reflect on their experience in the cohort.
This convening will conclude with a structured conversation about next steps for the assessment diagnostic tools participants have created, including ways that future collaborations might contribute to institutional capacity building and scalability of services.
Campus Output: Idea bank for programming, resources, and outreach strategies, expanded professional networks.
What costs and resources can be anticipated?
We anticipate the project will require approximately two weeks of FTE to be distributed across local campus team members at their institution’s discretion. Local team activities include: periodically attending virtual cohort-wide meetings, conducting focus groups, and working on implementation of AI-ready assessment resources.
To defray the project costs, including planning cohort workshops, instrument development, project coordination, individual consultation, and training support, each institution will make a cost-sharing contribution of $15,000.
What steps do I need to take?
If your institution is interested in participating, please send an expression of interest to Michael Fried (michael.fried@ithaka.org).