A Comparative Study of Gender Differences in the Utilization and Effectiveness of AI-Assisted Learning Tools in Programming Among University Students

This comparative study examines gender differences in the use and efficacy of AI-assisted learning tools for programming among university students. A survey-based methodology was used to collect data from a varied sample of students across different academic years, ensuring representation from diverse demographic groups. The study sought to investigate the frequency of AI tool use and students' assessments of their efficacy in learning programming fundamentals. Notably, the findings reveal that students overwhelmingly prefer ChatGPT as the primary AI tool for learning programming concepts, with 312 mentions, making it the most popular and presumably the most useful tool among respondents. Following ChatGPT, Blackbox AI was the second most mentioned, with 74 students highlighting its utility. Gemini, Co-pilot, Assistguru, and Amazon Code Whisperer followed in popularity, with varying levels of student engagement and perceived usefulness. The study also found a substantial positive association (r = 0.296, p < 0.05) between AI tool usage frequency and perceived effectiveness in programming education, indicating the potential benefits of increased interaction with AI tools. Gender disparities in tool preferences were identified, with male students showing a preference for specific instruments over their female counterparts. Despite these differences, there was little variation in the overall perceived efficacy of AI technology between genders. Furthermore, first-year students exhibited the highest frequency of AI tool usage, particularly on a weekly basis, emphasizing the importance of early exposure on usage patterns throughout students' academic careers. These findings underscore the necessity of considering gender preferences and demographics when developing and implementing AI-powered educational systems. The study recommends individualized approaches to enhance inclusivity and effectiveness in programming education, aiming to inform future advancements in educational technology and pedagogy.