Bridging the Gender Gap in STEM Education with AI: Female-Coded Chatbot as Role Model and Learning Assistant

Despite the potential of AI chatbots for personalized learning, empirical evidence for their effectiveness in addressing STEM gender disparities remains limited. This study explores the efficacy of ADA, a female-coded AI chatbot, in mitigating mathematical gender stereotypes while promoting equitable learning outcomes. A quasi-experimental study ( N = 195 ninth-grade German students, mean age = 14.33 years) conducted in mathematics classes compared the AI chatbot with traditional printed help cards on the Heron method. The study measured stereotypical beliefs, technological acceptance, emotions, situational interest, cognitive load, and academic performance. Results demonstrate a significant reduction in gender-stereotypical beliefs, with small but meaningful effect sizes observed across both genders. Technological acceptance was high, exhibiting no gender differences. The intervention promoted learning efficacy for both genders, with a significant gain in situational interest and no statistically significant gender differences in learning performance, emotional responses, or cognitive load. These findings underscore the potential of systematically designed AI chatbots as scalable pedagogical tools that simultaneously challenge gender stereotypes and promote equitable learning efficacy. Subsequent longitudinal research is required to validate the long-term sustainability and generalizability of these effects.