Abstract Artificial intelligence (AI) is increasingly shaping how young learners interact with digital technologies, yet many upper elementary students engage with AI systems passively and develop intuitive and sometimes inaccurate conceptions of how these systems work. This study examines the Foundational AI construct within a refined learning progression (LP), exploring how scaffolded instruction and dynamic assessment support conceptual shifts in students' understanding of how AI collects, learns from and uses data to make decisions. Drawing on Vygotsky's zone of proximal development and synergistic scaffolding theory, we refined the foundational AI construct of a five‐level LP and designed a two‐phase activity grounded in this LP to elicit and support student reasoning through structured tasks, informational scaffolds and facilitator prompts. Through mixed methods analysis of clinical interviews with 13 fourth and fifth graders (9–11 years), we identified recurring misconceptions and tracked shifts in student reasoning and movement along the Foundational AI construct of the LP. Furthermore, we examined one student's trajectory in depth to illustrate how dynamic assessment can function as a responsive instructional tool. Findings provide initial empirical insight into how scaffolded LP‐aligned instruction, paired with dynamic assessment, can support young learners' movement from surface‐level ideas to more structured understandings of how AI systems function. These insights contribute to the design of developmentally appropriate and contextually responsive AI learning experiences for primary education. Practitioner notes What was already known about this topic? Many young learners interact with AI technologies (eg, voice assistants, recommendation systems) but often hold surface‐level or inaccurate conceptions of how AI works. AI literacy frameworks exist, but none currently provide scaffolded pathways that align with young students' developmental readiness or explicitly address their initial misconceptions What this paper adds? Provides an initial empirical examination of a refined five‐level Foundational AI construct within a broader Learning Progression (LP) for upper elementary students. Demonstrates how LP‐aligned scaffolded instruction, using tasks, just‐in‐time informational supports and decision trees, can guide students from intuitive ideas to more data‐centered reasoning. Uses dynamic assessment to track and support conceptual growth, providing insight into students' readiness to reason about AI systems. Implications for practice and/or policy Scaffolded LPs that integrate structured tasks, informational prompts and dialogic facilitation can help support developmentally grounded AI instruction that is responsive to learner needs. Dynamic assessment frameworks can help researchers and educators capture students' shifts in reasoning, differentiating between ideas students can articulate independently and those requiring additional support. Designing layered, responsive scaffolds that actively elicit student reasoning and provide opportunities for reflection can support educators in guiding students' conceptual growth in AI literacy.
Chakraburty et al. (Mon,) studied this question.