AI-Mediated STEM Problem Solving: Activity Distribution and Functional Roles in a Systematic Review

Abstract

Artificial intelligence (AI) is increasingly embedded in STEM problem-solving activities, yet existing reviews have generally organized the literature by technology type or learning outcome. This review shifts the analytic focus from what AI can do to how it is integrated into students' STEM problem solving. It examined where AI was positioned within problem-solving activity, what functional role it performed, and how its functions or outputs were connected to students' subsequent judgments and actions. Following PRISMA 2020, searches of Scopus, Web of Science, ERIC, and backward references identified 29 English-language journal articles published from 2017 through June 4, 2026. Study-level coding mapped AI use across seven non-linear activity categories, while case-level analysis examined 37 reporting-eligible AI–student interactions in which the AI function or output, activity context, and subsequent student action were identifiable. AI was reported most often in two activity categories: Problem Understanding and Exploration, and Revision and Improvement. Four recurring functional roles were identified: Conceptual Resource, Generative Resource, Operational Component, and Adaptive Scaffold. The same technology assumed different roles depending on the activity category and how students used its output. The principal contribution of this review is to shift attention from AI's technical capabilities and associated learning outcomes to the interaction between AI outputs and students' subsequent activity. By linking each AI function or output to a problem-solving activity category and a subsequent student action, the review shows that AI's role is defined by how students use its output in context, not by the technology itself. The findings are descriptive of the selected corpus and its reporting practices; they should not be interpreted as prevalence estimates or evidence of causal learning effects.