Exploration and practice of artificial intelligence-assisted genetics experiment teaching

Area of Science:

• Genetics Education
• Artificial Intelligence in Education

Background:

• Traditional genetic experiment courses face challenges in efficiency and interdisciplinary integration.
• Advancements in artificial intelligence (AI) present opportunities for educational reform.

Purpose of the Study:

• To establish a model for integrating AI into genetic experiment teaching.
• To enhance teaching efficiency, quality, and student scientific literacy.

Main Methods:

• Introduction of a student-led module focused on AI-assisted experimental design.
• Development of a multi-dimensional AI-based evaluation system for student learning behavior.

Main Results:

• Significant improvements in teaching efficiency and quality were observed.
• The integrated model fostered cross-disciplinary perspectives, creativity, and practical AI skills.
• AI-based evaluation enabled automated data collection and precise analysis of student learning.

Conclusions:

• The AI-integrated model offers a practical approach to digital reform in university experimental teaching.
• This approach holds significant value for advancing experimental teaching innovation and cultivating digital-era talent.