Coded Narratives: Leveraging Women AI Scientists' Memoirs for Inspiration and Knowledge in K-12 AI Education

Abstract

This conceptual paper explores how autobiographical memoirs by prominent AI women scientists can be integrated into K-12 AI literacy curricula to inspire and educate students. Drawing on research on the motivating influences of role models and narrative stories in STEM learning, the paper analyzes The Worlds I See by Fei-Fei Li, a Chinese immigrant; Unmasking AI by Joy Buolamwini, a Black woman of Ghanaian descent; and Girl Decoded by Rana el Kaliouby, an Egyptian Muslim immigrant. These works represent diverse identities, resonating with girls and students from underrepresented groups and offering relatable narratives that can enhance motivation and engagement. The analysis explores how such memoirs can be leveraged for the motivational power of role models—demonstrating resilience in the face of challenges, identity affirmation, and career pathways while providing accessible insights into core AI literacy topics such as the critical role of data in AI, ethics & bias in AI, societal impacts, computer vision, and affective human-centered AI. Together, these narratives weave technical concepts with personal stories of discovery, addressing K-12 AI literacy goals like understanding AI’s evolution, technical foundations, and the nature of scientific inquiry. The paper argues that these books serve dual purposes: as motivational tools that align with STEM role model research, inspiring students to envision themselves as future innovators and as pedagogical resources that deepen AI understanding, making them powerful catalysts for inclusive education.