A systematic literature review of generative AI support for pre-service teachers in designing computational thinking instruction

Computational Thinking (CT) is increasingly expected across curricula, yet pre-service teachers often report limited preparation for translating CT concepts into instructional practice. This systematic literature review examines how Generative Artificial Intelligence (GenAI) supports pre-service teachers’ instructional design and what the available evidence implies for CT-oriented teaching. Using the PRISMA approach, the review synthesised 26 empirical studies identified through Scopus and ScienceDirect under the original eligibility criteria. The evidence base was heterogeneous: only two studies were coded as explicitly addressing CT integration, fifteen as implicitly CT-aligned, and nine as having no identifiable CT integration. Accordingly, indirect studies are treated as contextual evidence rather than direct evidence of CT instructional design. Across the literature, GenAI was used for ideation, lesson planning, feedback, multimedia generation, and iterative refinement. Several studies reported gains or positive experiences in efficiency, confidence, engagement, creativity, or instructional design competence; however, these outcomes were study-specific and should not be generalised as improvements in CT competence. Several studies have linked more productive use of GenAI to human evaluation, AI literacy, pedagogical or subject-matter knowledge, and structured frameworks or training. Reported risks included inaccurate or inconsistent outputs, hallucinations, overreliance, metacognitive laziness, and ethical concerns. Because the search was limited to Scopus and ScienceDirect and applied an open-access criterion, the review may be affected by database and accessibility bias. Overall, direct evidence that GenAI improves CT-specific instructional design remains preliminary; the broader literature more strongly supports a conditional role for GenAI as a pedagogical scaffold in CT-relevant instructional design.