How students engage with LLM-based GenAI to develop computational thinking: A systematic review
Mustafa Mustafa 1 , Tatang Herman 1 * , Yaya S. Kusumah 1 , Nurjanah Nurjanah 1 , Trysa Gustya Manda 1
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1 Department of Mathematics Education, Indonesia University of Education, Bandung, INDONESIA* Corresponding Author

Abstract

This systematic literature review examines how students engage with large language model-based generative artificial intelligence (GenAI) in developing computational thinking (CT). Following PRISMA 2020 procedures, 14 of 212 peer-reviewed studies published between 2021 and 2026 were synthesized to investigate how GenAI functions along a continuum from problem-solving tool to thinking partner. The review identifies four student-AI interaction patterns: surface, scaffolded, achiever-linked, and dialogic. Students with stronger CT proficiency and AI literacy engaged GenAI dialogically through critique, verification, and reflective prompting, whereas lower-proficiency learners relied on answer-oriented interactions linked to substitutive offloading and weaker problem-solving. GenAI supported algorithmic thinking, creativity, and reflection when students retained task agency, but risked over-reliance otherwise. Most studies positioned GenAI at a cognitive tool mid-point, yet sustained CT development depended on designs promoting epistemic agency and reflective dialogue. The review contributes a continuum framework and seven pedagogical safeguards that shift AI use toward thinking partnership.

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This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Article Type: Literature Review

EURASIA J Math Sci Tech Ed, Volume 22, Issue 12, December 2026, Article No: em2961

https://doi.org/10.29333/ejmste/19893

Publication date: 08 Nov 2026

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