Generative AI (GAI) is already impacting education at Brown, as well as higher education more broadly, and the rapid pace of change poses a challenge to institutional responses. Brown needs to continually adapt to emerging and pervasive AI capabilities–many of which are widely available and now embedded in educational technology systems used by Brown faculty, staff, and students. In light of the technology’s pervasiveness,the key question this committee will explore is not, “Should our teaching and learning communities use generative AI?” but rather, “How can we best utilize generative AI to support innovative and equitable teaching and learning?” In answering this question, the committee should make broad recommendations for how Brown can develop the organizational capacity to not only adapt to current developments in generative AI but also to rapidly respond to future developments. With this focus in mind, the Generative AI and Teaching & Learning (GAITL) committee will prepare a report and recommendations in the following areas:

1. How are Brown’s peer institutions responding to GAI as an innovation? What is happening now in Brown’s practice and policy, regarding Generative AI as an innovation?

Key Deliverables:

• Literature review on GAI-related policies and practices for teaching and learning
• Benchmarking policies and recommendations from peer institutions
• Analysis of a sample of syllabi at Brown for GAI-related policies, content, and assignments
• Review of current GAI approaches in support of teaching and learning for faculty and students
• Survey of faculty (including postdoctoral scholars) inquiring into a) current use in teaching practices and course policies and b) potential and concerns
• Review existing surveys of graduate and undergraduate students for insights on their perspectives and current use. Collect focus group insights from graduate and undergraduate students to supplement (see “Defining and Implementing AI Literacy” as an example).
• Survey of staff responsible for curriculum and policy processes at college, school, or department level around how GAI in teaching and learning is and could be addressed
• Focus groups/interviews with faculty and departments trying to innovate around GAI at the course and curriculum level
• A statement about the value proposition of a liberal education in the context of rapidly-expanding AI capabilities
   

2. What are the well-established principles in Brown’s approach to its educational mission that should guide Brown’s decision-making about the role of GAI in teaching and learning? How should these principles shape the review of policies and practices related to teaching and learning that are impacted by the existence of GAI?

Key Deliverables:

• Review of mission, Open Curriculum as described in Faculty Rules and Regulations, the Academic Code, and other relevant governance documents (e.g. Data Governance or FERPA requirements)
• Identify uniquely human capabilities that will remain critical even as AI capabilities advance (perhaps in reference to Brown’s Liberal Learning Goals)
• Specific recommendations for possible modifications to:
  • The Academic Code
  • Faculty Rules and Regulations guidance for faculty on their responsibilities around teaching
  • Use of Plagiarism Detection tools
  • Assurance of Learning for NECHE accreditation
• Recommend guidelines for educators and learners for the appropriate and ethical use of GAI for teaching and learning:
  • in teaching activities (e.g., content creation, answering student questions, feedback on student work, and grading)?
  • for learning (e.g. recording and summarizing lectures, generating practice exams and quizzes, generating ideas for assignments)
     

3. How should Brown help faculty, students, and staff develop a general level of AI Literacy for use in academic contexts and beyond? How can Brown develop the organizational capacity to support these efforts?

Key Deliverables:

• Literature review of best practices for developing AI Literacy (being an informed user) and organizational capacity around GAI in teaching and learning
• Recommend an AI Literacy model that is appropriate for university-wide conversations and planning
• Identify strategies to encourage departments and programs to consider how GAI will impact their curricula and make curricular revisions:
  • How can the University support departments and programs to reduce barriers to curricular revision?
• Recommend a process to identify and support pilots by faculty and staff around the use of GAI to enhance student learning.
  • Benchmark supports around GAI for instructors and students offered by peer institutions.
• If needed, identify policy changes to better support faculty in developing and delivering cross-unit courses that involve AI.