Agentic AI as a Digital Mentor for Student Teachers: A Quantitative Study on Internship Management and Teaching Readiness

Abstract

This study examined the role of agentic artificial intelligence as a digital mentoring support system for student teachers in managing academic, pedagogical, and reflective tasks related to their internships. The study was a quantitative survey of 180 B.Ed. student teachers. and the Integrated Teacher Education Programme. The study used the Agentic AI Usage Scale, Internship Management Readiness Scale, and Teaching Readiness Scale. Experts validated the tools; Cronbach’s alpha coefficients were .88, .91, and .89, respectively, indicating satisfactory reliability.
The findings indicated that Agentic AI usage (M = 3.72, SD = 0.61), internship management readiness (M = 3.84, SD = 0.58), and teaching readiness (M = 3.79, SD = 0.60) among student teachers were of moderate to high levels. Pearson's correlation analysis found a significant positive correlation between Agentic AI usage and internship management readiness (r = .64, p < .001). Pearson's correlation analysis also showed a significant positive correlation between Agentic AI usage and teaching readiness (r = .68, p < .001). Internship management was also strongly correlated with teaching readiness (r = .71, p < .001). Agentic AI usage was seen to predict teaching readiness among student teachers to a moderate to high degree with F(1, 178) = 151.42, p < .001, at the rate of approximately 46%, with Agentic AI usage (R^2=.46) leading to higher teaching readiness among students on the basis of a regression analysis with (β =.68, t = 12.30, p < .001) as the coefficient of regression of this study.
The results indicated that student teachers with a high level of AI agent use reported better task planning during the internship, prepared lesson plans in advance, wrote better reflective reports, and were more confident in handling lessons than those with a low level of AI agent use. ANOVA showed that high, moderate, and low use differed, F (2, 177) = 34.86, p < .001. Post hoc comparisons showed that high-scoring students differed from moderate- and low-scoring students. The research concluded that an AI agent would support task management, reflective writing practice, and improved pedagogical practices and lesson plans, leading to better teaching practices, and could serve as a digital mentor. The study concluded that teacher education curricula should incorporate structured support for task management, reflective practice, and improved pedagogies and lesson plans through an AI-based support system to meet future challenges and strengthen practice and professional preparation