Hoping for the Best: Artificial Intelligence Policy and Communication in Higher Education

• In Event: Instructional Technology in the Age of AI

Abstract

With the possibilities and challenges in learning and remediation for Artificial Intelligence in Education (AIEd) remaining uncertain, more policy clarity is needed. This study employs a mixed-methods approach to assess policy communications across a sample of U.S. universities. Findings reveal that less than half of institutions have AI policies, with significant disparities in quantity and quality. Descriptive and thematic analysis also illuminates the roles of policy providers, diverse approaches, ethical priorities, target audiences, and specific syllabus recommendations. Informed by Socio-technical Systems Theory (SST), our findings emphasize the complex interplay between social and technical factors in AI adoption and highlight the need for holistic, adaptable policies that balance innovation with ethical consideration in educational settings.

Authors

• Jue Wu, University of Florida

• Daniel Kim, University of Toronto