Region - Educational Research and Reviews

Against the backdrop of digital transformation in education, smart teaching has become an important trend in the reform of higher engineering education. College chemistry features systematic content, abstract concepts, and tight logical connections. Traditional blended teaching, however, still falls short in process supervision, learning status diagnosis, and personalized support. This study combined knowledge graphs, learning profiles, and intelligent teaching assistants to build an integrated smart teaching model, and conducted a quasi-experiment with 277 engineering students. Results show that the experimental group performed notably better than the control group in learning task completion rate and mastery of difficult and key knowledge points. Both groups achieved a pass rate of over 96%, but the experimental group demonstrated distinct advantages in learning equity, process management, and precise tutoring. This model can effectively optimize learning paths, enhance process guidance, and promote knowledge internalization, providing empirical references for the smart teaching reform of basic science and engineering courses in universities.