Exploring AI tool adoption in higher education: evidence from a PLS-SEM model integrating multimodal literacy, self-efficacy, and university support

Abstract

Introduction: Framed within the technology acceptance model, this study examines how multimodal literacy, self-efficacy, and university support affect students' attitudes toward artificial intelligence tools and the students' intentions to adopt them. Methods: Survey data from 498 students were analyzed using PLS-SEM 4.0 and SPSS 29. Results: The findings showed that the perceived usefulness of the AI tools was the strongest predictor of both attitude toward the tools and intention to use them. All three antecedent variables (multimodal literacy, self-efficacy, and university support) significantly impacted perceived usefulness and perceived ease of use. Discussion: By integrating individual dimensions and also institutional dimensions into the technology acceptance model, this study offers fresh insight into how AI tools might take root more effectively in higher education.

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