Machine Learning Applications in Chemistry Education and Experiment Simulation

Advancements in machine learning (ML) have transformed the landscape of chemistry education by enabling predictive experiment simulations, adaptive learning, and data-driven assessment. Integration of ML techniques within virtual laboratories enhances the accuracy, safety, and efficiency of experimental processes, allowing learners to explore complex chemical phenomena without physical constraints. Predictive models facilitate optimization of reaction parameters, identification of procedural errors, and real-time feedback, thereby bridging the gap between theoretical knowledge and practical application. Adaptive educational systems leverage engagement and learning analytics to monitor student progress, personalize learning pathways, and provide actionable insights for instructors, ensuring effective skill acquisition and conceptual understanding. Data-driven evaluation metrics derived from simulation interactions allow comprehensive assessment of both procedural proficiency and cognitive engagement, while decision support systems integrate ML predictions to guide laboratory planning and experimental decision-making. Challenges related to data quality, model interpretability, and pedagogical alignment remain critical for the successful adoption of ML frameworks, highlighting the need for standardized datasets, scalable platforms, and ethically informed practices. The chapter presents a holistic review of ML applications in chemistry education, outlining current methodologies, emerging research opportunities, and future directions for intelligent, simulation-driven, and adaptive learning environments.