Generative AI Scaffolding in Physics Education: A Phenomenological Analysis of Its Role and Implications in STEM Learning

Purpose of the study: This study investigates how generative AI tools—especially video generation—scaffold high school students’ understanding of Newtonian mechanics, focusing on female learners in a STEM Honors Physics class. It explores how these tools impact conceptual mastery, critical thinking, creativity, and students’ perceptions of AI use in education. Methodology: Using a phenomenological qualitative design, the study involved 17 female students. It followed a three-phase structure—preparatory, scaffolding, and post-discourse—with tools like AI-generated videos, simulations, TAM-based surveys, and reflective journals, grounded in Constructivist Learning Theory and the Technology Acceptance Model. Main Findings: AI-enhanced visualizations improved students’ conceptual understanding and learning efficiency. Students gained critical thinking through prompt refinement and creativity. Ethical concerns and AI accuracy issues were noted. Overall, students showed moderate satisfaction, ease of use, and usefulness perceptions, but cautious intentions toward future AI use. Novelty/Originality of this study: This is among the first studies to apply generative AI hypermedia in high school physics education through a structured, theory-driven framework. It uniquely highlights gender-specific engagement, ethical considerations, and practical integration of AI in fostering deeper conceptual and creative STEM learning.