A Bibliometric Review of Studies about the Acceptance of Artificial Intelligence Technologies in Teaching and Learning in Higher Education

Flores-Velásquez C.H.; Olivares-Zegarra S.; Dávila-Ignacio C.; Arévalo-Tuesta J.A.; Morales-Romero G.; Trinidad-Loli N.; Caycho-Salas B.; Aybar-Bellido I.; Arones M.; Aldana-Trejo F. · International Journal of Learning, Teaching and Educational Research (2024) · DOI: 10.26803/ijlter.23.3.14

Resumen

The growing incorporation of artificial intelligence (AI) tools in higher education (HE) has led to the use of indicators that allow the real impact of these tools to be identified in the teaching and learning process. In this sense, this study developed a bibliometric review on the acceptance of AI technologies in HE, providing an analysis of indicators on scientific production, with the aim of identifying prevalent thematic areas and knowledge gaps. From a methodological point of view, this study was carried out using a quantitative approach with a descriptive level, utilising 56 publications drawn from the Scopus database. The results show a sustained evolution with a growing trend in scientific production since 2021. The most predominant thematic area is evaluation of the acceptance of AI technologies in HE, making greater use of the Technology Acceptance Model (TAM) and the Unified Acceptance and Use of Technology theory (UTAUT). Therefore, it was concluded that the existing literature shows a sustained interest in investigating the acceptance of AI technologies due to the importance of determining the impact generated by their applications in different contexts or scenarios of the reality of HE in regard to the extent that AI technology is developed. This is because, on some occasions, its application does not necessarily lead to meeting the expectations raised in the teaching and learning processes. Finally, the gaps that need to be addressed in future research are "cultural and contextual diversity in AI acceptance", "emerging models of AI acceptance", and "critical elements influencing the acceptance of AI technologies", in HE. © Authors.

Instituciones: Universidad Nacional San Luis Gonzaga; Universidad Nacional Federico Villarreal; Universidad Nacional Tecnológica de Lima Sur; Universidad Nacional del Callao; Universidad Nacional de Educación Enrique Guzmán y Valle