THE IMPACT OF GENERATIVE ARTIFICIAL INTELLIGENCE TOOLS ON SELF-LEARNING SKILLS IN ENGINEERING EDUCATION: THE ROLE OF SELF-EFFICACY AND INTRINSIC MOTIVATION AS MEDIATING VARIABLES IN THE TECHNOLOGY ACCEPTANCE MODEL

Abstract

Abstract The introduction of Generative Artificial Intelligence (GenAI) into engineering education is an important shift that marks the change of pedagogical methods. It is based on the Technology Acceptance Model (TAM), which will help to analyze the role of self-efficacy and self-motivation in mediating the relationship between the use of GenAI and major educational outcomes of engineering students. In particular, it explores the role played by the confidence of the students in using technology and their intrinsic motivation in the effected perceptions of usefulness, perceived ease of use, behavioral intention, satisfaction, and academic performance. Students of engineering enrolled at several universities were used as the source of data by using a structured questionnaire, and the hypothesized relationships were tested using structural equation modeling. The results show that GenAI has a positive effect on the perceptions of usefulness and ease of use in students, which subsequently lead to their intention to use AI tools in learning. The findings further indicate that self-efficacy and self-motivation are important mediating variables hence enhancing the connection between GenAI use and the independent learning abilities of the students. All in all, the research shows that academic performance, satisfaction, and self-directed learning can be positively promoted among engineering students as a result of the successful implementation of GenAI. These findings indicate that artificial intelligence teaching tools should be incorporated in the learning process, and digital literacy programs should be encouraged in universities to assist learners in their independent and technology-assisted learning processes.