A meta-analysis of computational thinking and artificial intelligence in education: impacts on students' problem-solving skills - Asmaul Husnah; Widodo Widodo; Merry Lapasau; Hasbullah Hasbullah; Oom Rohmah (Jurnal Konseling dan Pendidikan, 2025)
Despite the rapid expansion of research on computational thinking (CT) and artificial intelligence (AI) in education, evidence on their comparative effects on students' problem-solving skills remains fragmented and inconsistent, underscoring the need for a systematic quantitative synthesis. This study conducted a systematic meta-analysis to examine and compare the effects of CT-based and AI-based instructional interventions on students' problem-solving performance. Following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) 2020 guidelines, twenty-four empirical studies published between 2017 and 2025 were analyzed using a three-level random-effects meta-analytic model to account for within-study dependencies and heterogeneity. The results showed that CT-based instruction produced a statistically significant and consistent positive effect on problem-solving skills (pooled effect size = 0.30, p < 0.001), indicating high stability across educational contexts. AI-based instructional interventions yielded a larger pooled effect size (0.46, p < 0.001), although greater variability was observed across instructional designs and contexts. These findings suggest that CT strengthens analytical reasoning and systematic problem-solving processes, whereas AI enhances adaptive and reflective thinking through personalized learning support. The study contributes theoretically by clarifying the complementary roles of CT and AI in problem-solving development and practically by providing evidence-based guidance for designing effective technology-enhanced learning environments.