Gender Bias in Self-Perception of AI Knowledge, Impact, and Support among Higher Education Students: An Observational Study

Objectives. This study investigates gender biases in AI perceptions among university students. It focuses on assessing self-perceptions regarding knowledge, impact, and support, with a specific emphasis on identifying any significant gender differences. The main hypotheses are focused on the existence of gender disparities in AI awareness, perceptions, and attitudes among higher education students. Participants. The study involves 380 participants, enrolled in undergraduate courses across various academic disciplines. Participants are university students with diverse backgrounds in terms of age, academic majors, and prior exposure to AI technologies. Study Methods. This research employs an observational study design. The sample size includes 380 participants. The study utilizes a structured questionnaire as the primary instrument for data collection. Outcome measures focus on variables such as perceived knowledge of AI, perceived impact of AI, and levels of support or apprehension towards AI technologies. Findings. The findings reveal significant gender differences, with females exhibiting lower levels than their male counterparts in the level of perceived knowledge about AI (\ (p \!\! 0. 005\) ), exposure awareness (p = 0. 001), perceived ability to apply AI (p = 0. 004), sensitivity towards AI use of private data (p = 0. 004), positive impact on society (p = 0. 002), support for AI development (\ (p\!\!0. 005\) ), and positive expectations towards AI (\ (p\!\!0. 005\) ). Statistical analysis, including nonparametric tests, was used to validate these observations. Conclusions. There are notable gender biases in the knowledge and perception of AI among university students. These biases have implications for the future development and adoption of AI technologies, suggesting a need for more gender-inclusive educational strategies in AI. The findings underscore the importance of addressing gender disparities in AI education to ensure equitable access and understanding of these technologies. It is important to integrate gender perspectives in AI curriculum and policy-making to mitigate potential biases and enhance inclusivity in the field of AI.