Teachers’ perceptions of generative AI in gender-inclusive STEM education: a grounded theory study

Abstract

Abstract The rapid emergence of Generative Artificial Intelligence (GenAI) tools has introduced both new possibilities and risks for supporting inclusive teaching and learning in STEM education. Although GenAI is increasingly adopted in primary and secondary schools, little is known about how teachers conceptualise its potential to support or hinder gender inclusiveness in STEM classrooms. Using qualitative Grounded Theory methodology, this study explored how teachers perceive the opportunities, challenges, and implications of integrating GenAI in ways that might foster girls’ engagement in STEM subjects. Primary and secondary school teachers in Australia ( N = 7 ) were interviewed to understand the factors influencing the adoption of GenAI to support girls’ participation in STEM. The findings revealed three interrelated factors that were shown to influence teachers’ adoption of GenAI tools: (1) teachers’ familiarity and experience with GenAI, (2) the paradoxical nature of GenAI, and (3) first-order and second-order implementation barriers. The findings also show that teachers’ reflections on gender-inclusive applications were speculative and influenced by their general concerns about gender stereotypes and the biases embedded in GenAI content. Together, teachers’ familiarity and experience with GenAI presents as a critical factor affecting educators’ adoption of these tools to bridge gender gaps in STEM, and that teachers experience significant external barriers that impact their ability to effectively integrate GenAI tools into their teaching and learning practice. Based on the insights learned, the study offers practical recommendations for teachers, schools, and policy makers that promote the adoption of GenAI tools to foster girls’ engagement in STEM.

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