Measuring Perceptions of Artificial Intelligence use in Higher Education: A Critical Review and Adaptation Framework for the Latin American Context (Papeles, 2026)
Introduction: The adoption of artificial intelligence tools in higher education has generated growing interest in understanding the factors that influence their acceptance, as well as in assessing the quality of the instruments used for their measurement. In this context, it is necessary to critically examine the theoretical models and the psychometric evidence supporting these instruments. Methodology: A systematic review was conducted following the Prisma guidelines, through which 26 empirical studies published between 2022 and 2025 and indexed in Scopus, Web of Science, SciELO and Redalyc were analyzed. The selected studies predominantly employ questionnaires with Likert-type scales to measure the constructs proposed by the TAM and UTAUT2 models and report information on instrument validation and reliability procedures. Results and Discussion: The findings reveal a consistent use of construct validity and internal reliability as minimum criteria for instrumental evaluation, although relevant variations are observed in methodological procedures and in the contextual adaptation of the models. In addition, challenges related to digital equity and institutional trust are identified as factors influencing the acceptance of artificial intelligence, particularly in Latin American contexts. Based on these findings, guidelines are proposed to improve the assessment of artificial intelligence acceptance in Latin American higher education. Conclusions: The review suggests that Likert-scale–based questionnaires are widely used and potentially replicable instruments; however, their application in Latin American higher education requires contextual and regulatory adaptations to ensure the validity of the measured constructs. In this regard, there is a clear need to advance toward instrumental designs that are more sensitive to the structural and institutional conditions of the region.
Keywords: Artificial intelligence; perception; higher education; questionnaires; measurement