Artificial Intelligence (AI) in Teaching and Learning

In academic year 2025-2026, the project leads are recruiting faculty, staff, and students to join one of the ongoing projects or to propose new projects under the umbrella of the AI in Teaching and Learning project. Just as when we began the project in fall 2023, we will guide individuals and teams to develop project plans, research instruments, coordinate the IRB (Human Subjects Review) process, and facilitate inter-disciplinary and cross-institutional collaborations. Four new teams are in development

• NEW! AI and World Languages
• NEW! AI and Engineering Education
• NEW! AI and Universal Design for Learning (UDL)
• NEW! AI and Teaching Writing
• Onboarding: What Do Students Need to Know Before Being Invited to Use AI? This project is in ongoing data collection, and actively looking for instructors to pilot and gather data on the onboarding activities the team developed.
• Measuring and Grading Learning Done with AI. This project is in ongoing data collection. 
• AI-incorporating learning. This project is in ongoing data collection.
• Mapping AI-Proofness: A Preliminary Cross-Disciplinary Investigation. This project is in ongoing data collection. 

In academic year 2023-2024, faculty, staff, and students came together on several teams to develop research projects exploring faculty and student experiences with AI.

In academic year 2024-2025, project teams worked to develop educational resources to support students and instructors in navigating the ethical integration of AI in postsecondary education, including actionable guidelines, pedagogical strategies, and policy recommendations. The resources focus on the key areas of student onboarding, assignment design, assessment, personalized feedback, accessibility, and open educational resources (OER). The educational resources, developed by a team of faculty and staff from institutions across the state, are evidence-based, informed by cutting-edge pedagogical research on these topics as well as the evolving scholarly literature. Findings from the research projects conducted in AY 2023-2025 are being used to create faculty development materials to support synchronous and asynchronous workshops and learning opportunities for instructors across the state.

• AI as a Support for Students with Disabilities. Exploring how artificial intelligence can support students with disabilities or learning differences in higher education contexts or settings.
• Onboarding: What Do Students Need to Know Before Being Invited to Use AI? The purpose of this project is to evaluate which onboarding activities are beneficial to students to increase comfort in using text generators in college courses.
• OER for Teaching with AI. This study aims to design, develop, and evaluate an adaptable OER resource for faculty/instructors to aid in understanding the principles of AI, methodologies for incorporating AI into teaching, ethical considerations surrounding AI in education, examples of AI-driven assignments, recommended AI class policies, and a review of current AI tools available for teaching. 
• Measuring and Grading Learning Done with AI. How do we measure and grade the learning that’s done with AI? This includes the assessment of AI-assisted projects and (re)defining the work of cognition, what to assess as student-generated content and AI-generated content, (re)defining the unit of intelligence, AI as cheating vs. tool, and the effects on / changes to student learning objectives. 
• AI-incorporating learning. This study seeks to (a) articulate what high-tech vs. low-tech learning looks like across classrooms and departments – including what forms of low-tech learning we consider worth keeping in the curriculum; and (b) outline a both/and approach where instructors are empowered to make use of the potentials of both analogue and AI-assisted modes of thinking and creation. 
• Mapping AI-Proofness: A Preliminary Cross-Disciplinary Investigation. This study develops a typology of possible assignments and “grades” their AI-proofness on a continuum. Doing so will give instructors in various academic disciplines the information they need to weigh pedagogical goals against the cost of various options. 
• The Potential of AI for Personalized and Constructive Feedback. Exploring the potential of AI systems in providing personalized and constructive feedback to students on their written or spoken work. 

Potential roles for collaborators include

• Contribute to development of teaching + learning resources
• Participate in the design of future research studies, including: research questions being investigated, data collection instruments, and/or data analysis plan
• Contribute to data collection in your courses (this may involve disseminating surveys, collecting student artifacts, coordinating interviews or focus groups with students, coordinating with class observations, and other data collection activities)
• Contribute to data collection in colleagues’ courses (this may involve conducting interviews or focus groups with students, conducting class observations, and other data collection activities)
• Contribute to analysis of the data collected by the research team
• Contribute to publication/presentation of findings

MegaSoTL projects are scholarship of teaching and learning (SoTL) projects that generate evidence of learning from multiple institutions.

Read about “MegaSoTL: Supporting pedagogical research across multiple institutions“