GenAI-Supported Flipped Learning in Preservice Chemistry Teacher Education: Lesson-Design Performance, Learning...

Area of Science:

• Educational Technology
• Chemistry Education
• Artificial Intelligence in Education

Background:

• Flipped learning models are increasingly adopted in science education.
• The integration of Generative Artificial Intelligence (GenAI) into educational settings is rapidly evolving.
• Understanding the impact of GenAI on preservice teacher preparation is crucial.

Purpose of the Study:

• To compare the effectiveness of GenAI-supported flipped learning (AI-FL) versus reading-based flipped learning (R-FL).
• To examine differences in lesson-design performance, learning attitude, self-regulated learning, and critical thinking awareness.
• To evaluate GenAI's role in pre-class preparation for preservice chemistry teachers.

Main Methods:

• A quasi-experimental design was employed with two intact classes over an 11-week preservice chemistry course.
• One group engaged in AI-FL, while the other used R-FL for pre-class preparation.
• Lesson-design performance, learning attitude, self-regulated learning, and critical thinking were assessed, controlling for pretest scores.

Main Results:

• The reading-based flipped learning group demonstrated significantly stronger lesson-design performance.
• The GenAI-supported flipped learning group reported more positive learning attitudes.
• No significant differences were found between groups in self-regulated learning or critical thinking awareness.

Conclusions:

• GenAI-supported pre-class learning may positively influence student attitudes in this context.
• Traditional reading-based preparation was more effective for improving lesson-design performance.
• Further research is needed to optimize GenAI integration for skill development in teacher education.