Conference Agenda

The rapid adoption of artificial intelligence (AI) in higher education has transformed teaching and learning processes. However, the effective integration of AI into learning activities depends on multiple technological, individual, and institutional factors that have not yet been sufficiently explored in emerging educational contexts. The objective of this study is to analyze the factors that influence the effective integration of artificial intelligence in higher education learning and its impact on student engagement.

A quantitative, cross-sectional research design was employed using a structured questionnaire administered to a sample of 150 higher education students enrolled in engineering programs and business-related fields. The conceptual model integrates constructs derived from the Technology Acceptance Model (TAM), digital competence, institutional support, effective AI integration, and learning engagement. Data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with the WarpPLS 7.0 software.

The results show that perceived ease of use and institutional support have a positive and statistically significant effect on the effective integration of artificial intelligence in learning, while digital competence does not exhibit a significant direct effect. In addition, effective AI integration positively and significantly influences student engagement. The model demonstrates acceptable predictive capability and an adequate overall fit for an exploratory study.

These findings contribute to the understanding of artificial intelligence adoption in higher education and offer practical implications for the design of institutional strategies aimed at promoting the meaningful and responsible use of AI in higher education.