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

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 anteced…

By the Truvace deskOctober 11, 20262 min read1 sourcePeer-reviewedTRV-2026-1324· cite & verify

Exploring AI tool adoption in higher education: evidence from a PLS-SEM model integrating multimodal literacy, self-efficacy, and university support — "student_ipad_school - 078" by flickingerbrad is licensed under CC BY 2.0. To view a copy of this license, visit https://creativecommons.org/licenses/by/2.0/.

In brief

Published September 8 2025, this peer-reviewed study examined AI tool adoption among 498 higher-education students using a PLS-SEM model that integrated multimodal literacy, self-efficacy, and university support into the technology acceptance model. It found perceived usefulness was the strongest predictor of attitude and intention to use AI tools, with all three antecedents significantly affecting perceived usefulness and ease of use.

The findings matter because they point to actionable levers for universities seeking to embed AI tools effectively, linking student skills and institutional support to adoption intentions rather than tool features alone. What remains uncertain from the supplied text is whether intentions translated into sustained use, academic outcomes, or differences across disciplines, institutions, or student populations.

Main points

Survey of 498 students analyzed with PLS-SEM 4.0 and SPSS 29 within technology acceptance model framework.
Perceived usefulness was the strongest predictor of both attitude toward AI tools and intention to use them.
Multimodal literacy, self-efficacy, and university support each significantly influenced perceived usefulness and perceived ease of use.

The gain

In higher education, higher multimodal literacy, self-efficacy, and university support increased students' perceived usefulness and ease of use of AI tools, with perceived usefulness driving more positive attitudes and stronger intentions to adopt them.

The rundown

The study framed AI adoption within the technology acceptance model and tested how individual and institutional factors relate to student perceptions. It collected survey data from 498 students and applied PLS-SEM 4.0 and SPSS 29 to model relationships.

Results identified perceived usefulness as the dominant driver of attitude and intention, while multimodal literacy, self-efficacy, and university support were significant antecedents of both perceived usefulness and perceived ease of use, suggesting institutional support and student capabilities jointly shape uptake.