The impact of artificial intelligence-assisted teaching on medical students' learning outcomes

This research explores the impact of AI-assisted teaching on medical students' learning motivation and educational outcomes by researchers investigating the "teacher-student-external environment" framework in intelligent medical education.

The study reveals that teaching quality and external environment serve as primary drivers for enhancing medical students' learning motivation in AI-assisted educational settings. Higher motivation subsequently leads to more positive learning attitudes and increased satisfaction levels. The research demonstrates that all these factors—teaching quality, environment, motivation, attitude, and satisfaction—positively influence educational outcomes, with learning motivation playing a crucial mediating role between teaching quality, external environment, and final results.

• Teaching quality directly impacts both learning motivation and outcomes, with AI enabling more targeted instructional design and personalized learning experiences
• Learning motivation mediates the relationship between environmental factors and academic achievement, creating a positive cycle of engagement
• AI integration requires systematic faculty training, curriculum adaptation, and institutional collaboration to maximize educational effectiveness
• The findings support combining constructivist learning theory with the ARCS motivational model for optimal AI-enhanced medical education implementation