Comparative Analysis Of Institutional Perceptions Of Artificial Intelligence In University Education
Paper Number
ECIS2026-1167
Paper Type
SP
Abstract
As generative artificial intelligence (GenAI) becomes increasingly embedded in higher education, universities are pressed to formalize rules for its use in teaching and assessment. Despite this urgency, systematic comparisons of how institutions globally govern AI remain scarce. This study addresses that empirical gap by analyzing public GenAI policy documents from 37 of the top 100 universities worldwide. By employing topic modeling, sentiment analysis, and exploratory statistics, we identify the primary thematic focuses and institutional stances toward AI integration. Our analysis reveals that most institutions favor neutral, flexible frameworks that prioritize academic integrity and responsible use over outright prohibition or active encouragement. Furthermore, the topic modeling delineates four core areas of policy focus: academic integrity, responsible AI use, institutional governance, and pedagogical experimentation. Overall, these governance patterns illustrate how universities are currently managing the inherent tension between technological innovation, institutional accountability, and human-AI collaboration.
Recommended Citation
Byun, Jongbok, "Comparative Analysis Of Institutional Perceptions Of Artificial Intelligence In University Education" (2026). ECIS 2026 Proceedings. 2.
https://aisel.aisnet.org/ecis2026/comp_mgmt/comp_mgmt/2
Comparative Analysis Of Institutional Perceptions Of Artificial Intelligence In University Education
As generative artificial intelligence (GenAI) becomes increasingly embedded in higher education, universities are pressed to formalize rules for its use in teaching and assessment. Despite this urgency, systematic comparisons of how institutions globally govern AI remain scarce. This study addresses that empirical gap by analyzing public GenAI policy documents from 37 of the top 100 universities worldwide. By employing topic modeling, sentiment analysis, and exploratory statistics, we identify the primary thematic focuses and institutional stances toward AI integration. Our analysis reveals that most institutions favor neutral, flexible frameworks that prioritize academic integrity and responsible use over outright prohibition or active encouragement. Furthermore, the topic modeling delineates four core areas of policy focus: academic integrity, responsible AI use, institutional governance, and pedagogical experimentation. Overall, these governance patterns illustrate how universities are currently managing the inherent tension between technological innovation, institutional accountability, and human-AI collaboration.
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