Exploration of An Intelligent Teaching System for the Post-Lithium Battery Course Empowered by AI

Abstract

This study addresses critical challenges in Post-Lithium Battery Courses, including outdated knowledge, disconnection between theory and practice, and simplistic assessment methods. With AI assistance, we propose an intelligent teaching architecture based on a teaching/evaluation/feedback cycle. The system integrates dynamic knowledge graphs and adaptive learning engines to achieve personalized knowledge delivery, utilizes virtual simulation and digital twins to overcome practical training limitations, and employs a data-driven approach to establish a process-centered teaching-evaluation-feedback loop. Practical applications demonstrate its effectiveness in visualizing complex principles, simulating processes, and facilitating project-based innovation. The system significantly enhances students’ knowledge integration, engineering thinking, and problem-solving capabilities, providing new insights for curriculum reform under engineering education accreditation. The implementation results from a semester-long case study show a 27.3% improvement in knowledge mastery and a 42% increase in practical skills success rate, validating the system’s efficacy in bridging the theory-practice gap.