A meta-analysis of computational thinking and artificial intelligence in education: impacts on students' problem-solving skills

Jurnal Konseling dan Pendidikan December 30, 2025 Volume 13, Number 4 5 authors

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.

Authors

  1. Asmaul Husnah

    Universitas Indraprasta PGRI

  2. Widodo Widodo

    Universitas Indraprasta PGRI

  3. Merry Lapasau

    Universitas Indraprasta PGRI

  4. Hasbullah Hasbullah

    Universitas Indraprasta PGRI

  5. Oom Rohmah

    Universitas Indraprasta PGRI

Keywords

Computational thinkingArtificial intelligenceProblem-solving skillsMeta-analysisEducation

References

  1. 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
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. Cohen, J. (2013). Statistical Power Analysis for the Behavioral Sciences. Statistical Power Analysis for the Behavioral Sciences. https://doi.org/10.4324/9780203771587
  10. 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
  11. 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
  12. 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
  13. 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
  14. Grover, S., & Pea, R. (2021). Computational Thinking: A Competency Whose Time Has Come. Computer Science Education. https://doi.org/10.5040/9781350057142.CH-003
  15. 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
  16. 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
  17. 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
  18. 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
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. Krstić, L., Aleksić, V., & Krstić, M. (2022). Artificial Intelligence in Education: A Review. 223–228. https://doi.org/10.46793/TIE22.223K
  25. 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
  26. 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
  27. 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
  28. 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
  29. Mackinnon, D. P. (2012). Introduction to statistical mediation analysis. Introduction to Statistical Mediation Analysis, 1–477. https://doi.org/10.4324/9780203809556
  30. 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
  31. 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
  32. 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
  33. 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
  34. 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
  35. 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
  36. 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
  37. 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
  38. 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
  39. 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
  40. 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
  41. 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
  42. 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
  43. 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
  44. 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
  45. 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
  46. 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
  47. 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
  48. 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
  49. 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
  50. 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
  51. 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
  52. 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