AI-Driven Educational Data Analytics and Intelligent Tutoring in Learning Factory Environments

Abstract

The rapid growth of artificial intelligence in higher education creates new op- portunities to make learning factory environments more adaptive, data-informed, and aligned with industrial practice. This study examines how the integration of educational data analytics and intelligent tutoring systems supports smarter learning factory models that connect theoretical instruction with hands-on indus- trial training. Using a quantitative research design, data were collected from 180 higher education students participating in AI-supported learning factory sessions. Log data on learning interactions, performance metrics, and system- generated feedback were analyzed using statistical modeling to test the effects of AI-driven interventions on learning outcomes. The results show that ed- ucational data analytics significantly increases the adaptability of instructional content, enabling the intelligent tutoring system to personalize learning paths in real time based on individual performance profiles. Students who engaged with AI-based tutoring reported higher learning engagement and achieved better problem-solving scores and stronger retention of practical concepts than those in conventional learning factory settings. These findings indicate that combining educational data analytics with intelligent tutoring systems improves both the efficiency and effectiveness of learning factory models by enabling continuous feedback loops, dynamic adjustment of learning tasks, and learner-centered in- struction. The study concludes that AI-driven, data-informed learning factories can play a strategic role in preparing students with industry-relevant compe- tences and offers practical implications for educational technologists and insti- tutions designing next-generation education technology solutions.