Use of Large Language Models in the Teaching of Computational Thinking: A Systematic Mapping Study - Milhorim, João Antônio Misson; Santana, Diego Gomes de; Lemos, Diego Fernandes; Garcés, Lina (Anais do Simpósio Brasileiro de Educação em Computação (EDUCOMP), 2026)
This study presents the results of a systematic mapping of the use of Large Language Models (LLMs) in Computational Thinking (CT) education. The study was designed using the Goal–Question–Metric approach and well-known secondary study guidelines. A final set of 13 primary studies was selected from 10,022 records retrieved from Scopus, IEEE Xplore, and the ACM Digital Library. This study provides an overview of technological configurations, pedagogical practices, learning outcomes, evaluation strategies, and educational contexts in which LLMs have been used in CT education. Results show that LLMs are most commonly utilized as evaluators, feedback generators, and intelligent tutors, with emerging applications in pair programming and scaffolding. Pedagogical approaches favor structured support and projector design-based learning, with teachers acting primarily as facilitators and intervention designers and students as the main protagonists of their education. LLMs’ adoption is growing in higher education, while adoption at primary and secondary levels remains underexplored. Most interventions take place in synchronous, face-to-face formats and display substantial variation in evaluation rigor. Key gaps include limited use of validated knowledge assessment tools and insufficient attention to teacher preparation for hybrid human–AI instruction. These findings inform future research, design, and policy for AI-enhanced computing education.