Equipping Elementary Teacher Candidates for AI-Enhanced Classrooms: Lessons from a STEMLEAD Self-Assessment Webtool Interaction

As artificial intelligence (AI) reshapes formative assessment practices, elementary teacher candidates (ETCs) must be prepared to engage with AI-supported tools pedagogically and ethically. This qualitative study explored ETCs’ experiences with STEMLEAD, an AI-integrated self-assessment platform designed to support science conceptual learning through adaptive quizzes and immediate feedback. Grounded in Zimmerman’s self-regulated learning theory and Vygotsky’s sociocultural perspective, the study examined the evolving perceptions of ETCs through pre- (N = 22) and post-surveys (N = 19) during a single session of a science methods course. Initial findings revealed that ETCs regarded AI as efficient but ethically complex using it personally, but not yet pedagogically. After using STEMLEAD, participants reported increased awareness of AI’s potential to support learner autonomy, motivation, and personalized feedback. They also voiced the need for professional learning around ethical AI use, transparency, and alignment with instructional goals. This study contributes to science teacher education by demonstrating how structured AI engagement has the potential to develop ETCs’ assessment literacy and pedagogical agency. It calls for teacher preparation programs to integrate experiential AI learning with reflective inquiry to foster equitable, student-centered instruction in digital classrooms.