Date
Publisher
arXiv
This paper proposes an approach to the responsible adoption of generative AI in higher education, employing a ''points to consider'' approach that is sensitive to the goals, values, and structural features of higher education. Higher education's ethos of collaborative faculty governance, pedagogical and research goals, and embrace of academic freedom conflict, the paper argues, with centralized top down approaches to governing AI that are common in the private sector. The paper is based on a semester long effort at the University of Pittsburgh which gathered and organized perspectives on generative AI in higher education through a collaborative, iterative, interdisciplinary process that included recurring group discussions, three standalone focus groups, and an informal survey. The paper presents insights drawn from this effort that give rise to the ''points to consider'' approach the paper develops. These insights include the benefits and risks of potential uses of generative AI In higher education, as well as barriers to its adoption, and culminate in the six normative points to consider when adopting and governing generative AI in institutions of higher education.
Study at a glance
- Question: How should institutions of higher education responsibly adopt and govern generative AI?
- Setting and data: University of Pittsburgh, Fall 2023, 144 survey respondents, 19 focus group faculty, and 29 ad hoc committee members.
- Method: Mixed methods (committee discussions, focus groups, and an informal survey)
Why it matters
Higher education institutions face pressures to adopt generative AI, but corporate top-down governance conflicts with academic freedom and shared faculty governance.
Key findings
- Most surveyed faculty (63.9%) reported not using generative AI in their professional activities at all, with only 50.5% of courses having generative AI policies.
- Centralized top-down management clashes with academic freedom and shared governance, though centralized approaches remain appropriate for procurement, academic integrity, and sensitive high-stakes processes.
- Faculty identified major concerns regarding output inaccuracy, bias, displacement of foundational skill-building, increased uncompensated workload, and privacy and intellectual property issues.
Summary drafted with AI from the full paper and may contain errors. See the full paper for details.
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