Performance of Artificial Intelligence: Does artificial intelligence dream of electric sheep

Abstract

This study investigates the performance of generative artificial intelligence (AI) in evaluating the acceptance of generative AI technologies within higher education guidelines, reflecting on the implications for educational policy and practice. Drawing on a dataset of guidelines from top-ranked universities, we compared generative AI evaluations with human evaluations, focusing on acceptance, performance expectancy, facilitating conditions, and perceived risk. Our study revealed a strong positive correlation between ChatGPT-rated and human-rated acceptance of generative AI, suggesting that generative AI can accurately reflect human judgment in this context. Further, we found positive associations between ChatGPT-rated acceptance and performance expectancy and facilitating conditions, while a negative correlation with perceived risk. These results validate generative AI evaluation, which also extends the application of the Technology Acceptance Model and the Unified Theory of Acceptance and Use of Technology framework from individual to institutional perspectives.

Acceptance of generative AI in higher education: A latent profile analysis of policy guidelines

“…Building on previous research (Ioku et al, 2024), we utilized generative AI, speci cally ChatGPT, to evaluate the acceptance of generative AI in higher education through a two-step process. Initially, we de ned the acceptance of generative AI as the degree to which an institution endorses or limits the use of generative AI technologies in academic environments.…”