One example of gender bias in AI systems in education is an AI-based career guidance tool that recommends science, technology, engineering, and mathematics (STEM) pathways more frequently to male students than to female students. This bias occurs because the AI is trained on historical data that reflects existing gender inequalities in subject choices and workforce participation. If past data shows fewer women in STEM, the system learns and reproduces this pattern. To mitigate this bias, developers should include women and diverse viewpoint in AI development, assess AI outputs for bias, and provide responsible AI training. Ethical AI contributes to gender equality by promoting fairness, transparency, and inclusion in digital learning. When AI systems are designed responsibly, they can challenge stereotypes, expand equal opportunities, and empower all learners regardless of gender. Thank you.