2026-10-13T09:45:54Z https://www.scidb.cn/oai

10.57760/sciencedb.35834 2026-05-07T09:58:33Z

2026-05-07 A Survey on University Teachers' Trust and Risk Perception Regarding the Use of the AI Agent "Lobster (OpenClaw)" doi:10.57760/sciencedb.35834 en The rapid iteration of artificial intelligence agents (AI agents) is profoundly reshaping the teaching ecology of higher education, but the trust mechanism and risk perception of teachers towards such technologies have not been systematically examined. This study takes OpenClaw intelligent agents as the research context, constructs an analytical framework based on technology trust theory and risk perception theory, and uses questionnaire survey method to conduct empirical research on 163 teachers from 8 universities across the country. Research has found that university teachers exhibit a "limited trust" characteristic towards AI agents, with ability trust and goodwill trust significantly higher than integrity trust; Accuracy risk and privacy risk are equally high in risk perception, while teaching autonomy risk perception is actually the lowest; There is no significant negative correlation between trust and risk perception, but rather a relatively independent dual track structure; Trust and risk perception together explain 44.8% of the variance in usage intention, with ability trust and accuracy risk being the two strongest predictors, and accuracy risk positively predicting usage intention. The study reveals the complex psychological landscape of university teachers between "empowerment" and "crossing boundaries", providing empirical evidence for the formulation of artificial intelligence governance policies in the field of higher education.  AI agents; higher education; technology trust; risk perception Xu Xu PUBLIC https://creativecommons.org/publicdomain/zero/1.0/ dataset Science Data Bank