Factors influencing student learning performance and continuous use of artificial intelligence in online higher education

Abstract Students in higher education increasingly integrate emerging technologies to enrich their learning experiences. Universal tools such as virtual classrooms, multimedia presentations, and learning management systems are now widely employed in teaching and learning activities. However, Artificial intelligence (AI) techniques are not yet commonly used in higher education institutions (HEIs). Drawing on social constructivism theory, this study aims to determine the relationship between continuous uses of AI technologies, AI self-efficacy and collaborative learning and their impact on learning performance in an online learning environment. The target population for the study was students enrolled in HEIs in India. A simple random sampling was adopted to collect the data which resulted in 918 usable responses. For statistical analysis, Smart PLS v.4 was used to analyze the collected data. The findings show that independent variables - AI in online learning, AI self-efficacy, and collaboration– strongly influence the learning performance of higher education students in online learning environments with β values (0.390, 0.189, and 0.352 respectively). The study highlights that independent variables are key predictors of learning performance. The findings of the study have an important bearing on online learners and HEIs. This study has, however, certain limitations that could challenge generalizability. This includes female-dominated, urban-based, and discipline-specific samples. Future research should address these issues by diversify participants across genders, regions, and different academic fields.