Making Thinking Visible: Human–AI Collaborative Filmmaking as Pedagogy in Secondary Classrooms

Abstract

Generative AI (text-to-image, text-to-video) enables K-12 students to produce sophisticated films with minimal effort. Yet this ease risks hiding the very processes educators value: drafting, hesitation, revision, and justification – the traditional traces of student thinking (Ritchhart, Church, & Morrison, 2011). When a compelling scene is generated from a single prompt, what evidence remains of the learner’s cognitive journey? This study addresses: How can AI-assisted filmmaking be designed to make student thinking visible rather than hidden? We answer through a three-year DBR project. Unlike prior work focusing on AI as automation (Lubart, 2023) or on visible thinking in purely verbal contexts, we position AI as a creative partner whose outputs students must interpret, evaluate, and negotiate. Our contribution is twofold: (a) an empirically grounded framework for visible thinking in AI-mediated creative production, and (b) methodological exemplars for capturing learning analytics from process data.