The effect of AI-based conceptual change instruction, as compared with teacher-led conceptual change instruction and conventional instruction, on remediating misconceptions and enhancing academic performance in biology among eleventh-grade students

Background and Objectives: Misconceptions in biology are one of the persistent challenges in teaching this science. These misconceptions often resist traditional teaching and hinder meaningful learning and academic achievement. Therefore, the present study aimed to compare the effectiveness of traditional teaching, teacher-led conceptual change teaching, and artificial intelligence-based conceptual change teaching in reducing biology misconceptions and improving academic achievement of eleventh grade female students. Methods: This study was conducted using a quasi-experimental design with three intact classroom groups. Participants included 90 eleventh grade female students who were divided into three groups: a control group (30 people) that received conventional biology instruction, a first experimental group (30 people) that received teacher-led conceptual change teaching, and a second experimental group (30 people) that received artificial intelligence-based conceptual change teaching. Data were collected using a Biology Misconceptions Questionnaire and a Biology Academic Achievement Test. One-way analysis of covariance was used to analyze the data. Findings: The results revealed that both conceptual change teaching approaches significantly reduced students’ misconceptions and improved their academic performance compared to the traditional method, and among them, the AI-based conceptual change teaching group showed the greatest improvement. Conclusion: The findings of this study indicate that integrating conceptual change strategies with intelligent educational technologies can significantly improve conceptual understanding and academic achievement in secondary school biology education. These results emphasize the importance of designing educational environments that actively target students’ misconceptions. Future research could investigate the long-term effects of AI-supported conceptual change teaching and its implementation in more diverse educational contexts.