Research Trends of Artificial Intelligence‐Facilitated Computational Thinking in K‐12 STEM : A Systematic Review Through the Lens of Activity Theory

Background With the rapid advancement of artificial intelligence (AI), research in the cross‐disciplinary field of science, technology, engineering, and mathematics (STEM) education has increasingly focused on how to leverage AI to foster computational thinking (CT) among K‐12 students, given that CT, recognized as a core 21st‐century competency, plays a pivotal role in cultivating students’ literacy and equipping them with the ability to tackle complex problems. Objective Grounded in activity theory, this study seeks to identify the key factors shaping K‐12 students’ CT development in AI‐enhanced STEM education. It systematically analyzes: (a) the subjects involved and the types and roles of AI tools employed, (b) the purposes, periods, and CT dimensions within the object, (c) the methods, strategies, and evaluation performance applied within rules, (d) the learning domains within communities and the interactions within the division of labor, and (e) the developmental trends in outcomes. Method This study employed the PRISMA framework to systematically review 57 SSCI journal publications published between 2017 and 2025. In addition, Epistemic Network Analysis (ENA) was utilized to examine the current state of CT evaluation. Results and Conclusions Findings indicate that: (1) the primary subjects of the studies were elementary school students; expert systems and robotics were the most prevalent types of AI, which were frequently employed as learning tools; (2) the primary objective was to evaluate the effectiveness of AI in CT–K12–STEM, with periods typically ranging from 2 to 6 months, focusing on conceptual knowledge and practical skills; (3) the rules focused on exploratory learning and gamification strategies, employing quantitative research methods; (4) communities spanned cross‐disciplinary domains, with division of labor tending towards mixed interaction types; and (5) outcomes focused on the learning cognitive perspective, encompassing aspects such as learning performance and problem solving. Based on the research results, practical recommendations are provided for educational researchers and policymakers.