Individual differences in artificial intelligence literacy among university students Attitudes, self-efficacy, and demographic factors
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This study explored university students’ artificial intelligence (AI) use across multiple European contexts, with a focus on key AI use dimensions, their application in learning, and individual differences. Using a non-representative convenience sample (N = 226), data were collected through a 15-construct questionnaire. Key findings revealed that students exhibited high levels of awareness in dimensions like Ethics and Responsibility, while reporting moderate to low engagement in General attitude and Study-use attitude, highlighting gaps in AI integration within educational routines. Notably, teachers’ influence was rated the lowest, signalling limited institutional support for AI-driven learning. A regression analysis identified four major predictors of Study-use attitude: positive General attitude and Perceived effectiveness boosted AI integration in learning, while Responsibility had a slight negative effect. Self-efficacy emerged as the strongest positive predictor, pointing towards the importance of confidence in tool selection and application for broader adoption. Several significant individual differences were observed in the sample. Male students reported higher scores in Experience, Study-use attitude, and Self-efficacy. Younger students displayed better Conceptual knowledge, while older students demonstrated greater Willingness to learn about them. Students in business disciplines outperformed their counterparts in humanities and education in both Experience and Reliability. Part-time students showed higher Responsibility and Ethics scores, whereas full-time students exhibited stronger Conceptual knowledge. These findings align with previous studies on AI literacy and highlight the need for tailored interventions to address gaps in AI integration and competence in education. Future research should examine longitudinal changes in AI literacy and the role of educators’ AI literacy in shaping students’ perceptions and practices. Enhanced focus on curriculum development for both bachelor’s and master’s programs is recommended to ensure comprehensive AI literacy training.
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