Artificial intelligence for enhancing learning and motivation among education faculty students

The rapid development of Artificial Intelligent (AI) has triggered change of thoughts about conventional pedagogical patterns in educational institutions especially in relation to student involvement and educational motivation. Even though there has been significant investment in educational technology infrastructure, the modern-day education faculty programs are still grappling with a deteriorating student motivation, poor personal learning activities and a lack of readiness to work in a technology-based teaching capacity. The research filling the critical gap that is presented in this study is the empirical effectiveness of AI-based intervention targeted at education faculty students in particular, as they are in a unique position to impact the future of the educational practices. Results were obtained through a mixed-methods research design that studied undergraduate and graduate education students and three semesters using an integrated AI learning ecosystem based on adaptive learning platforms, intelligent tutoring systems, feedback mechanisms based on natural language processing, and elements of gamification. The study used structural equation modeling, hierarchical linear regression, and thematic analysis as part of the evaluation of cognitive, affective, and behavioral aspects of learning engagement. Findings showed statistically significant differences with many outcome measures of biochemical results, such as a gain in intrinsic motivation scores of 34.7, a gain in learning persistence measures of 41.2, and an increase in academic performance indicators of 28.9. In addition, qualitative results also showed paradigm changes in self-efficacy perceptions and attitude to the use of technology amongst students.