AcademAI: Investigating AI Usage, Attitudes, and Literacy in Higher Education and Research
Richard D. Brown, Elizabeth Sillence, Dawn Branley-Bell
Abstract
We investigate perceptions of AI among university students and staff, focusing on sociodemographic predictors of use, attitudes and literacy. We follow an explanatory mixed-methods approach: an online survey (269 students and staff) capturing self-reported AI use, attitudes, and literacy, and 24 semi-structured online interviews exploring barriers to acceptance in higher education and research. Quantitative data reveal differences in perceptions of AI usage between students and staff. Males report higher use, more positive attitudes, and greater AI literacy than females. Higher socioeconomic status predicts more frequent use, and older age predicts lower AI literacy. Qualitative findings highlight concerns about academic repercussions of AI and threat to jobs. Participants highlight a lack of guidance and need for support to promote responsible use. Universities should increase engagement, and provide unambiguous guidance, to tackle misperceptions of how AI is used by others and address staff and student fears impacting AI acceptance and adoption.