Adoption of Artificial Intelligence and Sustainable Learning Outcomes in Engineering Education: Evidence from the Technology Acceptance Model

Artificial Intelligence (AI) is transforming higher education by introducing new learning dynamics based on automation, personalization, and data analytics. Within the framework of sustainable development, its integration into university education represents an opportunity to enhance the quality, accessibility, and resilience of educational systems, in alignment with Sustainable Development Goal 4 (SDG 4). Nevertheless, there remains limited empirical evidence regarding how the adoption of these technologies influences learning outcomes, particularly in engineering education contexts within developing countries. This study analyzes the adoption of artificial intelligence tools among engineering students and evaluates their impact on academic performance from the perspective of the Technology Acceptance Model (TAM). A structural model is proposed that examines the relationships among perceived ease of use, perceived usefulness, attitude toward AI, intention to use, and perceived academic learning. Based on a sample of 389 university students, the data were analyzed using Structural Equation Modeling (SEM). The results confirm that perceived ease of use significantly influences perceived usefulness, and that both variables positively impact attitudes toward artificial intelligence. In turn, attitude significantly influences usage intention, which is positively correlated with academic performance. Notably, attitude emerges as the primary predictor of learning, underscoring the central role of attitudinal factors in technology adoption. Furthermore, a slight negative effect of perceived ease of use on learning is identified, suggesting potential risks of superficial engagement or cognitive dependency in highly automated environments. These findings contribute to the literature by extending the TAM model to the analysis of sustainable learning, demonstrating that the adoption of artificial intelligence depends not solely on functional factors, but on interrelated cognitive, attitudinal, and behavioral processes. From a practical perspective, the study offers implications for higher education institutions, highlighting the need to promote a critical, reflective, and pedagogically guided use of artificial intelligence. This research provides empirical evidence from the context of engineering education in a developing country, contributing to an understanding of the role of artificial intelligence in building sustainable learning environments.