Detection and remediation of students’ chemical misconceptions through artificial intelligence-based corrective instructional interventions

Abstract

Misconceptions in chemistry represent persistent cognitive barriers that hinder students’ conceptual understanding and negatively affect learning outcomes. Traditional diagnostic methods often fail to identify these misconceptions in a timely and individualized manner. This study explores the potential of artificial intelligence–based approaches for the automatic detection of students’ chemical misconceptions and the implementation of targeted corrective instructional interventions. The proposed model integrates natural language processing and adaptive feedback mechanisms to analyze students’ written responses, problem-solving steps, and explanatory reasoning in key chemistry topics. Based on the identified misconception patterns, the system generates personalized corrective tasks, conceptual explanations, and guided questions aimed at restructuring students’ understanding. A pilot implementation was conducted in secondary school chemistry classes, focusing on topics commonly associated with misconceptions, such as chemical equilibrium, stoichiometry, and reaction mechanisms. The findings indicate that artificial intelligence–supported diagnostic and remedial interventions significantly improve conceptual clarity, reduce the persistence of misconceptions, and enhance students’ metacognitive awareness. The study highlights the pedagogical value of artificial intelligence as a tool for formative assessment and individualized support in chemistry education, offering practical implications for designing more responsive and learner-centered instructional environments.