Generative artificial intelligence attitude analysis of undergraduate students and their precise improvement strategies: A differential analysis of multifactorial influences

Generative Artificial Intelligence (GenAI) has fundamentally transformed the education landscape, offering unprecedented potential for personalized learning and enhanced teaching methods. This research conducted two sub-studies aimed at exploring the influences and differences in college students’ attitudes towards generative artificial intelligence across the dimensions of gender, major, and experience. The first sub-study developed a scale based on the Expectancy-Value Theory to measure college students’ attitudes towards generative artificial intelligence, confirming the scale’s validity and reliability. The second sub-study analyzed attitudes towards generative artificial intelligence among 713 undergraduate students. The results revealed significant differences in undergraduate students’ attitudes towards generative artificial intelligence based on gender, major, and experience. Female students and those with experience in generative artificial intelligence showed more positive attitudes compared to male students and those without generative artificial intelligence experience. Notably, students majoring in Education scored significantly higher on generative artificial intelligence attitudes than students from other majors. Regression analysis further indicated that gender, discipline, and experience significantly predicted attitudes towards generative artificial intelligence. Students from different academic backgrounds exhibited significant gender and experience differences in their attitudes towards generative artificial intelligence within their respective majors. These findings underscore the necessity for educational strategies that consider gender, experience, and disciplinary backgrounds. It is crucial to provide all students with ample opportunities to engage with generative artificial intelligence to ensure gender equity in artificial intelligence education. Educators must design differentiated and inclusive generative artificial intelligence education activities and strategies tailored to specific disciplines to meet diverse student needs and accommodate disciplinary variations. This study aims to provide insightful perspectives for assessing college students’ attitudes towards generative artificial intelligence and to offer information for future artificial intelligence education research.