Academic AI Group

To explore and develop comprehensive recommendations for the integration of Artificial Intelligence (AI) into the academic, research, academic administrative, and student success dimensions of our university.

Scope: The group will focus on identifying opportunities and challenges associated with AI technology in higher education contexts, particularly in teaching and learning; research, scholarship, and creative activity; student services, engagement and success; ethics and policy; and connection with external partners. Broadly, the committee will consider the following:

• What are the long-term implications of AI in reshaping higher education?
• How can the university position itself as a leader in AI application and innovation within the academic community?
   

Duration: The group is expected to present its findings and recommendations by the end of spring semester 2024. 
Membership: Faculty from diverse disciplines, staff from key areas, consultation with stakeholders (including alumni/industry).

Expected Outcomes

1. A scan of the current state and potential of AI in higher education.
2. Strategic recommendations for integrating AI in teaching, research, and student success initiatives.
3. A framework for establishing an external industry advisory council to guide and support AI initiatives.
4. An implementation roadmap with actionable short-term and long-term goals, including the infrastructure needed to support the recommendations.
5. An analysis of potential risks, ethical considerations, and policy implications associated with AI deployment.

Potential Guiding Questions

1. Teaching and learning

   • How can AI technologies enhance pedagogy and learning outcomes?
   • How should AI-related learning outcomes be incorporated into the curriculum?
   • What role can AI play in personalizing and diversifying educational experiences?

2. Research, scholarship, and creative activity

   • In what ways can AI contribute to innovative RSCA practices across disciplines?
   • What are the ethical considerations of AI in RSCA, particularly in data-driven fields?

3. Student services, engagement, and success

   • How can AI tools be utilized to improve student advising, student academic support services, and engagement?
   • How can AI be leveraged to support student success, retention, and graduation rates?
   • What AI-driven interventions can be developed to identify and support specific students?

4. Ethics and Policy

   • What measures are necessary to protect student privacy and data security in AI applications?
   • What ethical standards and policies should be established for responsible AI use in higher education?
   • How can we ensure inclusivity and fairness in AI-driven decisions and processes?
   • How can we develop an ongoing process for identifying and addressing ethical concerns and new policy solutions as AI and its use in higher education evolves?

5. Technology and Infrastructure

   • What investments are needed to support AI initiatives?
   • What ongoing support and training will be necessary for faculty, staff, and students?

6. External Industry Advisory Council

   1. How can we effectively establish and utilize an external industry advisory council, including alumni, to guide AI integration?
   2. What role will this council play in connecting the university with industry trends and opportunities?