Machine teachers and human-AI collaboration: student differences in perceptions of AI-based education

As artificial intelligence (AI) becomes increasingly integrated into educational settings, it becomes important to understand how AI can serve as a collaborator in the learning process. Although the adoption of AI in education is accelerating, research on how to strategically design AI-based education to resonate with diverse learners remains limited. In particular, the role of learner characteristics in shaping perceptions of AI-based education has received limited attention. Addressing this gap, the present research replicates and extends a prior study on human-AI collaboration by examining student gender. Two experimental studies, Study 1 (N = 182) and Study 2 (N = 218), were conducted with undergraduate students in the United States. Findings from both studies reveal significant differences: female students generally evaluate AI-based education more negatively than male students, with these differences being most pronounced in the human-AI collaboration condition. Results underscore the importance of accounting for student characteristics when designing and implementing AI-based educational experiences. The findings contribute to the ongoing scholarship on human-machine communication and offer practical implications for inclusive, learner-sensitive AI integration in education.