A meta-analysis of computational thinking and artificial intelligence in education: impacts on students' problem-solving skills
-
Published: December 30, 2025
-
Page: 80-95
Abstract
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.
- Computational thinking
- Artificial intelligence
- Problem-solving skills
- Meta-analysis
- Education

This work is licensed under a Creative Commons Attribution 4.0 International License.
- Aminah, N., Sukestiyarno, Y. L., Cahyono, A. N., & Maat, S. M. (2023). Student activities in solving mathematics problems with a computational thinking using Scratch. International Journal of Evaluation and Research in Education (IJERE), 12(2), 613–621. https://doi.org/10.11591/IJERE.V12I2.23308
- Assink, M., & Wibbelink, C. J. M. (2016). Fitting three-level meta-analytic models in R: A step-by-step tutorial. The Quantitative Methods for Psychology, 12(3), 154–174. https://doi.org/10.20982/TQMP.12.3.P154
- Aytekin, A., & Topçu, M. S. (2024). Improving 6th Grade Students’ Creative Problem Solving Skills Through Plugged and Unplugged Computational Thinking Approaches. Journal of Science Education and Technology, 33(6), 867–891. https://doi.org/10.1007/s10956-024-10130-y
- Barus, Y. K., Pangestu, W. T., Muzaki, F. I., & Ahdhianto, E. (2025). Unlocking Potential: A Teacher’s Lens on Artificial Intelligence Towards Students’ Problem-Solving Skills. Journal of Integrated Elementary Education, 5(1), 220–233. https://doi.org/10.21580/JIEED.V5I1.24974
- Bayaga, A. (2024). Enhancing M Enhancing mathematics problem-solving skills in AI-driven environment: Integrated SEM-neural network approach. Computers in Human Behavior Reports, 16. https://doi.org/10.1016/j.chbr.2024.100491
- Chau, D. B., Luong, V. T., Long, T. T., & Thi Thao Linh, N. (2025). Personalized Mathematics Teaching with The Support of AI Chatbots to Improve Mathematical Problem-Solving Competence for High School Students in Vietnam. European Journal of Educational Research, 14(1), 323–333. https://doi.org/10.12973/EU-JER.14.1.323
- Chen, C., & Huang, M. (2024). Enhancing programming learning performance through a Jigsaw collaborative learning method in a metaverse virtual space. International Journal of STEM Education, 11(1). https://doi.org/10.1186/s40594-024-00495-2
- Cheung, M. W. L. (2015). Meta-Analysis: A Structural Equation Modeling Approach. Meta-Analysis: A Structural Equation Modeling Approach, 1–378. https://doi.org/10.1002/9781118957813
- Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Statistical Power Analysis for the Behavioral Sciences. https://doi.org/10.4324/9780203771587
- Egger, M., Smith, G. D., Schneider, M., & Minder, C. (1997). Bias in meta-analysis detected by a simple, graphical test. British Medical Journal, 315(7109), 629–634. https://doi.org/10.1136/BMJ.315.7109.629
- Filiz, O., Kaya, M. H., & Adiguzel, T. (2025). Teachers and AI: Understanding the factors influencing AI integration in K-12 education. Education and Information Technologies 2025 30:13, 30(13), 17931–17967. https://doi.org/10.1007/S10639-025-13463-2
- Fitdyawati, S. D., In’am, A., & Zukhrufurrohmah. (2025). Mathematical Problem-Solving Ability from the Perspective of the Computational Thinking Approach. Kreano, Jurnal Matematika Kreatif-Inovatif, 16(1), 210–227. https://doi.org/10.15294/KREANO.V16I1.12197
- Gadanidis, G. (2017). Artificial intelligence, computational thinking, and mathematics education. International Journal of Information and Learning Technology, 34(2), 133–139. https://doi.org/10.1108/IJILT-09-2016-0048
- Grover, S., & Pea, R. (2021). Computational Thinking: A Competency Whose Time Has Come. Computer Science Education. https://doi.org/10.5040/9781350057142.CH-003
- Hidayatullah, E., Untari, R., & Fifardin, F. (2024). Effectiveness of AI in solving math problems at the secondary school level. Union: Jurnal Ilmiah Pendidikan Matematika, 12(2), 350–360. https://doi.org/10.30738/UNION.V12I2.17548
- Higgins, C., O’Leary, C., McAvinia, C., & Ryan, B. J. (2024). Generating a Template for an Educational Software Development Methodology for Novice Computing Undergraduates: an Integrative Review. Journal of Information Technology Education: Innovations in Practice, 23, 1–23. https://doi.org/10.28945/5374
- Hsu, T., Chang, C., Wong, L. H., & Aw, G. P. (2022). Learning Performance of Different Genders’ Computational Thinking. Sustainability (Switzerland), 14(24). https://doi.org/10.3390/su142416514
- Huang, X., & Qiao, C. (2024). Enhancing Computational Thinking Skills Through Artificial Intelligence Education at a STEAM High School. Science and Education, 33(2), 383–403. https://doi.org/10.1007/s11191-022-00392-6
- Hutchins, N. M., Biswas, G., Maróti, M., Lédeczi, Á., Grover, S., Wolf, R., Blair, K. P., Chin, D., Conlin, L., Basu, S., & McElhaney, K. (2019). C2STEM: a System for Synergistic Learning of Physics and Computational Thinking. Journal of Science Education and Technology 2019 29:1, 29(1), 83–100. https://doi.org/10.1007/S10956-019-09804-9
- Jaya, S. (2025). Enhancing Problem-Solving Abilities of Teachers and Students through Integrated Computational Thinking Training in the Curriculum. Sebatik, 29(1), 96–102. https://doi.org/10.46984/SEBATIK.V29I1.2606
- Kaufmann, E., & Reips, U. D. (2024). Meta-analysis in a digitalized world: A step-by-step primer. Behavior Research Methods, 56(7), 1–21. https://doi.org/10.3758/S13428-024-02374-8/TABLES/7
- Khazanchi, R., Di Mitri, D., & Drachsler, H. (2025). The Effect of AI-Based Systems on Mathematics Achievement in Rural Context: A Quantitative Study. Journal of Computer Assisted Learning, 41(1), e13098. https://doi.org/10.1111/jcal.13098
- Kim, B. S., Go, E. J., Moon, W. J., Kim, B. C., & Kim, J. H. (2024). Development and Application of Elementary School AI Education Program Using the International Baccalaureate (IB) Primary Years Programme (PYP) Approach. Journal of Curriculum and Teaching, 13(2), 83–97. https://doi.org/10.5430/JCT.V13N2P83
- Krstić, L., Aleksić, V., & Krstić, M. (2022). Artificial Intelligence in Education: A Review. 223–228. https://doi.org/10.46793/TIE22.223K
- Liao, J., Zhong, L., Zhe, L., Xu, H., Liu, M., & Xie, T. (2024). Scaffolding Computational Thinking With ChatGPT. IEEE Transactions on Learning Technologies, 17, 1668–1682. https://doi.org/10.1109/TLT.2024.3392896
- Lin, Y., Chen, S., Tsai, C., & Lai, Y. (2021). Exploring Computational Thinking Skills Training Through Augmented Reality and AIoT Learning. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.640115
- Luckin, R. (2018). Machine Learning and Human Intelligence: The Future of Education for the 21st Century. UCL IOE Press, 157. https://eric.ed.gov/?id=ED584840
- Lye, S. Y., & Koh, J. H. L. (2014). Review on teaching and learning of computational thinking through programming: What is next for K-12? Computers in Human Behavior, 41, 51–61. https://doi.org/10.1016/J.CHB.2014.09.012
- Mackinnon, D. P. (2012). Introduction to statistical mediation analysis. Introduction to Statistical Mediation Analysis, 1–477. https://doi.org/10.4324/9780203809556
- Maharani, S., Kholid, M. N., Pradana, L. N., & Nusantara, T. (2019). Problem Solving in the Context of Computational Thinking. Infinity Journal, 8(2), 109–116. https://doi.org/10.22460/INFINITY.V8I2.P109-116
- Menlah, C. K. A., & Boateng, F. O. (2025). Examining the effect of AI-based tutoring systems on students’ mathematical problem-solving skills: The moderating role of mathematics anxiety. Journal of Pedagogical Sociology and Psychology, 7(3), 5–17. https://doi.org/10.33902/JPSP.202536137
- Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., Antes, G., Atkins, D., Barbour, V., Barrowman, N., Berlin, J. A., Clark, J., Clarke, M., Cook, D., D’Amico, R., Deeks, J. J., Devereaux, P. J., Dickersin, K., Egger, M., Ernst, E., Gøtzsche, P. C., … Tugwell, P. (2009). Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement. PLOS Medicine, 6(7), e1000097. https://doi.org/10.1371/JOURNAL.PMED.1000097
- Musaeus, L. H., & Musaeus, P. (2024). Computational Thinking and Modeling: A Quasi-Experimental Study of Learning Transfer. Education Sciences 2024, Vol. 14, 14(9). https://doi.org/10.3390/EDUCSCI14090980
- Nie, X., Tian, Y., Liu, M., Wu, D., & Guo, Y. (2025). The impact of generative artificial intelligence on students’ higher order thinking: Evidence from a three-level meta-analysis. Education and Information Technologies 2025, 1–32. https://doi.org/10.1007/S10639-025-13735-X
- Noviyana, H., Rahmawati, F., Rara Kirana, A., Tanod, J., Pgri, S., & Lampung, B. (2025). Enhancing Elementary Students’ Mathematical Problem-Solving Skills Through AI-Assisted Problem-Based Learning. Journal of Integrated Elementary Education, 5(2), 254–268. https://doi.org/10.21580/JIEED.V5I2.27576
- Nuangchalerm, P., Prachagool, V., Saregar, A., & Yunus, Y. M. (2024). Fostering Pre-Service Teachers’ AI Literacy through School Implications. Journal of Philology and Educational Sciences, 3(2), 77–86. https://doi.org/10.53898/JPES2024327
- Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., Shamseer, L., Tetzlaff, J. M., Akl, E. A., Brennan, S. E., Chou, R., Glanville, J., Grimshaw, J. M., Hróbjartsson, A., Lalu, M. M., Li, T., Loder, E. W., Mayo-Wilson, E., McDonald, S., … Moher, D. (2021). The PRISMA 2020 statement: an updated guideline for reporting systematic reviews. BMJ (Clinical Research Ed.), 372. https://doi.org/10.1136/BMJ.N71
- Qurohman, M. T. (2024). Enhancing High School Students Problem Solving Ability in Algebra through Artificial Intelligence Based Learning. International Journal of Trends in Mathematics Education Research, 7(4), 9–17. https://doi.org/10.33122/IJTMER.V7I4.358
- Rajapakse, C., Ariyarathna, W., & Selvakan, S. (2024). A Self-Efficacy Theory-Based Study on the Teachers’ Readiness to Teach Artificial Intelligence in Public Schools in Sri Lanka. ACM Transactions on Computing Education, 24(4). https://doi.org/10.1145/3691354
- Ruangtip, P., Ruckbumrung, T., & Rueboon, W. (2025). Enhancing computational thinking in elementary students through STEM and Mojobot. International Journal of Evaluation and Research in Education (IJERE), 14(5), 3917–3927. https://doi.org/10.11591/IJERE.V14I5.33091
- Saghai, M. A., & Mohtat, N. (2024). The Impact of Implementing Artificial Intelligence in Educational Management on Creativity and Problem-Solving Skills of Secondary School Students. No DOI, Retrieved from https://www.juac.ir/article_209034_en.html
- Salehudin, M., Azizah, Y. N., Hamidy, A., Iswanto, B., & Rahman, F. (2024). Learning ICT with computational thinking approach to improve problem solving ability in junior high school students. AIP Conference Proceedings, 2927(1). https://doi.org/10.1063/5.0193638
- Sapulete, H., santosa, T. A., Wantu, H. M., Sarnoto, A. Z., Nungraha, A. R., Nursalim, E., Panggabean, T. E., & Triantho, A. I. (2024). The Effect of Machine Learning-Assisted Inquiry Based Learning Size Model on Students’ Problem-Solving Thinking Skills. EScience Humanity Journal, 5(1), 289–296. https://doi.org/10.37296/esci.v5i1.226
- Scherer, R., Siddiq, F., & Viveros, B. S. (2019). The cognitive benefits of learning computer programming: A meta-analysis of transfer effects. Journal of Educational Psychology, 111(5), 764–792. https://doi.org/10.1037/EDU0000314
- Shute, V. J., Sun, C., & Asbell-Clarke, J. (2017). Demystifying computational thinking. Educational Research Review, 22, 142–158. https://doi.org/10.1016/J.EDUREV.2017.09.003
- Touretzky, D. S., & Gardner-McCune, C. (2022). Artificial Intelligence Thinking in K–12. Computational Thinking Education in K–12, 153–180. https://doi.org/10.7551/MITPRESS/13375.003.0013
- Voogt, J., Fluck, A., Webb, M., Cox, M., Malyn-Smith, J., & Zagami, J. (2016). A K-6 Computational Thinking Curriculum Framework: Implications for Teacher Knowledge. In Journal of Educational Technology and Society (Vol. 19, Issue 3). No DOI, Retrieved from https://dare.uva.nl
- Weese, J. L., & Feldhausen, R. (2017). STEM Outreach: Assessing Computational Thinking and Problem Solving. ASEE Annual Conference and Exposition, Conference Proceedings, 2017-June. https://doi.org/10.18260/1-2--28845
- Wing, J. M. (2008). Computational thinking and thinking about computing. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 366(1881), 3717–3725. https://doi.org/10.1098/RSTA.2008.0118
- Wu, D., Xiang, Y., Wu, X., Yu, T., Huang, X., Zou, Y., Liu, Z., & Lin, H. (2020). Artificial intelligence-tutoring problem-based learning in ophthalmology clerkship. Annals of Translational Medicine, 8(11), 700–700. https://doi.org/10.21037/ATM.2019.12.15
- Xing, G. Y., Cady, A., & Wang, X. C. (2025). Playful Computational Thinking Learning in and Beyond Early Childhood Classrooms: Insights from Collaborative Action Research of Two Teacher-Researchers. Education Sciences, 15(7). https://doi.org/10.3390/educsci15070840
- Yatani, K., Sramek, Z., & Yang, C.-L. (2024). AI as Extraherics: Fostering Higher-order Thinking Skills in Human-AI Interaction. https://doi.org/10.48550/arXiv.2409.09218