Published May 19, 2026
| Version v1
Journal article
Open
Synergizing Artificial Intelligence and Multiple Intelligences in Project-Based Learning: A Meta-Analysis of Academic Achievement Outcomes
Authors/Creators
- 1. Department of Physics, Dire Dawa University, Dire Dawa, Ethiopia
- 2. Universitas Islam Negeri Sumatera Utara, Indonesia
Description
Project based learning (PBL) promotes deeper learning but often fails to accommodate diverse cognitive profiles. Artificial intelligence (AI) offers adaptive scaffolding, while Multiple Intelligences (MI) theory provides a differentiation framework. However, no quantitative synthesis has examined their combined effect on academic achievement. This meta analysis synthesizes evidence on the synergy of AI and MI within PBL and estimates the overall effect on academic achievement, together with key moderators. Methods: Following PRISMA 2020 guidelines, we systematically searched Scopus, Web of Science, ERIC, PsycINFO, and ProQuest Dissertations (2015–2025). Inclusion criteria were: (a) empirical studies with control/comparison groups; (b) interventions combining AI tools and MI based differentiation in PBL; (c) reported academic achievement data; (d) K 16 learners. Random effects meta analysis, moderator analyses, and publication bias tests were performed. Forty two studies (N = 8,943) were included. The overall effect was moderate and positive (Hedges’ g = 0.48, 95% CI [0.39, 0.57], p < .001). Significant moderators were AI role (scaffolding > content generation > assessment), MI implementation method (student choice > teacher assigned/fixed), and education level (secondary > primary > tertiary). Subject domain did not moderate the effect. Publication bias was minimal (Egger’s p = 0.12), and sensitivity analyses confirmed robustness. AI and MI synergize effectively in PBL, yielding meaningful academic gains that exceed the isolated effects of either component. Educators should embed AI as a scaffolding tool (not an automaton) and allow students to choose MI aligned project roles. Policymakers should invest in AI tools with MI differentiation capabilities for PBL curricula.
Files
70-95-Biolae-Belay Sitotaw Goshu-LAY 2026.pdf
Files
(923.7 kB)
| Name | Size | Download all |
|---|---|---|
|
md5:4fc3639bb4d51bb812ccc229c5adbf1e
|
923.7 kB | Preview Download |
Additional details
References
- Ali, S., Diwan, P., & Srinivasan, R. (2025). The impact of GenAI on learning outcomes: A systematic review and meta analysis of experimental studies. Educational Research Review, 44, 100789. https://doi.org/10.1016/j.edurev.2025.100789 Baş, G. (2016). The effect of multiple intelligences theory based education on academic achievement: A meta analytic review. Educational Sciences: Theory and Practice, 16(6), 1833–1864. https://doi.org/10.12738/estp.2016.6.0156 Botella Nicolás, A. M., & Ramos Ramos, P. (2019). Self-determination theory: A motivational framework for project-based learning. Contextos Educativos: Revista de Educación, (23), 27–40. https://doi.org/10.18172/con.3576 Booth, A., Clarke, M., Ghersi, D., Moher, D., Petticrew, M., & Stewart, L. (2012). The nuts and bolts of PROSPERO: An international prospective register of systematic reviews. Systematic Reviews, 1, 2. https://doi.org/10.1186/2046-4053-1-2 Chen, C. H., & Yang, Y. C. (2019). Revisiting the effects of project based learning on students' academic achievement: A meta analysis investigating moderators. Educational Research Review, 26, 71–81. https://doi.org/10.1016/j.edurev.2018.11.001 Duval, S., & Tweedie, R. (2000). Trim and fill: A simple funnel plot based method of testing and adjusting for publication bias in meta analysis. Biometrics, 56(2), 455–463. https://doi.org/10.1111/j.0006-341X.2000.00455.x Egger, M., Davey Smith, G., Schneider, M., & Minder, C. (1997). Bias in meta analysis detected by a simple, graphical test. BMJ, 315(7109), 629–634. https://doi.org/10.1136/bmj.315.7109.629 Ferrero, M., Vadillo, M. A., & León, S. P. (2021). A valid evaluation of the theory of multiple intelligences is not yet possible: Problems of methodological quality for intervention studies. Intelligence, 88, 101566. https://doi.org/10.1016/j.intell.2021.101566 Foster, E. D., & Deardorff, A. (2017). Open Science Framework (OSF). Journal of the Medical Library Association, 105(2), 203–206. https://doi.org/10.5195/jmla.2017.88 Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books. Gardner, H. (2011). Frames of mind: The theory of multiple intelligences (10th anniversary ed.). Basic Books. (Original work published 1983) Guyatt, G., Busse, J. W., Schünemann, H. J., & Jaeschke, R. (2025). Core GRADE 4: Rating certainty of evidence risk of bias, publication bias, and reasons for rating up certainty. BMJ, 389, e083864. https://doi.org/10.1136/bmj 2024-083864 Hattie, J. (2009). Visible learning: A synthesis of over 800 meta analyses relating to achievement. Routledge. Hedges, L. V. (1981). Distribution theory for Glass's estimator of effect size and related estimators. Journal of Educational Statistics, 6(2), 107–128. https://doi.org/10.3102/10769986006002107 Hedges, L. V., Tipton, E., & Johnson, M. C. (2010). Robust variance estimation in meta regression with dependent effect size estimates. Research Synthesis Methods, 1(1), 39–65. https://doi.org/10.1002/jrsm.5 Higgins, J. P. T., Savović, J., Page, M. J., Elbers, R. G., & Sterne, J. A. C. (2019). Assessing risk of bias in a randomized trial. In J. P. T. Higgins, J. Thomas, J. Chandler, M. Cumpston, T. Li, M. J. Page, & V. A. Welch (Eds.), Cochrane handbook for systematic reviews of interventions (2nd ed., pp. 205–228). Wiley. https://doi.org/10.1002/9781119536604.ch8 Higgins, J. P. T., & Thompson, S. G. (2002). Quantifying heterogeneity in a meta analysis. Statistics in Medicine, 21(11), 1539–1558. https://doi.org/10.1002/sim.1186 Howard, J. L., Bureau, J., Guay, F., Chong, J. X. Y., & Ryan, R. M. (2021). Student motivation and associated outcomes: A meta analysis from self determination theory. Perspectives on Psychological Science, 16(6), 1300–1323. https://doi.org/10.1177/1745691620966789 Kokotsaki, D., Menzies, V., & Wiggins, A. (2016). Project based learning: A review of the literature. Improving Schools, 19(3), 267–277. https://doi.org/10.1177/1365480216659733 Langan, D., Higgins, J. P. T., Jackson, D., Bowden, J., Veroniki, A. A., Kontopantelis, E., Viechtbauer, W., & Simmonds, M. (2019). A comparison of heterogeneity variance estimators in simulated random effects meta analyses. Research Synthesis Methods, 10(1), 83–98. https://doi.org/10.1002/jrsm.1316 Lee, S., & Kim, J. (2025). A systematic review of generative AI in K–12: Mapping goals, activities, roles, and outcomes via the 3P model. Computers & Education: Artificial Intelligence, 8, 100245. https://doi.org/10.1016/j.caeai.2025.100245 Lipsey, M. W., & Wilson, D. B. (2001). Practical meta analysis. Sage Publications. Liu, W., & Wang, Y. (2026). The effect of AI and multiple intelligences in project based learning on English achievement. Computers and Education: Artificial Intelligence, 10, 100312. https://doi.org/10.1016/j.caeai.2026.100312 Meng, Z., Chen, Y., & Li, R. (2024). Sensitivity analysis with iterative outlier detection for systematic reviews and meta analyses. Statistics in Medicine, *43*(8), 1549–1563. https://doi.org/10.1002/sim.10008 Page, M. J., McKenzie, J. E., Bossuyt, P. M., Boutron, I., Hoffmann, T. C., Mulrow, C. D., & Moher, D. (2021). The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. BMJ, 372, n71. https://doi.org/10.1136/bmj.n71 Partlett, C., White, I. R., & Riley, R. D. (2017). Random effects meta analysis: Coverage performance of 95% confidence and prediction intervals following REML estimation. Statistics in Medicine, 36(2), 301–317. https://doi.org/10.1002/sim.7140 R Core Team. (2023). R: A language and environment for statistical computing. R Foundation for Statistical Computing. https://www.R-project.org/ Riley, R. D., Higgins, J. P. T., & Deeks, J. J. (2011). Interpretation of random effects meta analyses. BMJ, 342, d549. https://doi.org/10.1136/bmj.d549 Rosenthal, R. (1979). The file drawer problem and tolerance for null results. Psychological Bulletin, 86(3), 638–641. https://doi.org/10.1037/0033-2909.86.3.638 Ryan, R. M., & Deci, E. L. (2017). Self determination theory: Basic psychological needs in motivation, development, and wellness. Guilford Press. https://doi.org/10.1521/978.14625/28806 Sterne, J. A. C., Sutton, A. J., Ioannidis, J. P. A., Terrin, N., Jones, D. R., Lau, J., & Higgins, J. P. T. (2011). Recommendations for examining and interpreting funnel plot asymmetry in meta analyses of randomised controlled trials. BMJ, 343, d4002. https://doi.org/10.1136/bmj.d4002 Sterne, J. A. C., Hernán, M. A., Reeves, B. C., Savović, J., Berkman, N. D., Viswanathan, M., & Higgins, J. P. T. (2016). ROBINS I: A tool for assessing risk of bias in non randomised studies of interventions. BMJ, 355, i4919. https://doi.org/10.1136/bmj.i4919 Steinert, S., Hellmers, N., & Tschirner, N. (2025). Scaffolding through prompts in digital learning: A systematic review and meta-analysis of effectiveness on learning achievement. Educational Research Review, 46, 100670. Advance online publication. https://doi.org/10.1016/j.edurev.2025.100670 Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4 Sweller, J. (2010). Element interactivity and intrinsic, extraneous, and germane cognitive load. Educational Psychology Review, 22(2), 123–138. https://doi.org/10.1007/s10648-010-9128-5 Tlili, A., Saqer, K., Salha, S., & Huang, R. (2025). Investigating the effect of artificial intelligence in education (AIEd) on learning achievement: A meta analysis and research synthesis. Information Development, 41(3), 825–842. https://doi.org/10.1177/02666669241304407 Veritas Health Innovation. (2024). Covidence systematic review software. https://www.covidence.org Viechtbauer, W. (2010). Conducting meta analyses in R with the metafor package. Journal of Statistical Software, 36(3), 1–48. https://doi.org/10.18637/jss.v036.i03 Warner, A., & Myers, B. (2009). Unpacking effective project based learning. Corwin Press. Xu, W., & Ouyang, F. (2022). Exploring the effectiveness and moderators of artificial intelligence in the classroom: A meta analysis. In Proceedings of the 2022 International Conference on Artificial Intelligence in Education (pp. 78–92). Springer. https://doi.org/10.1007/978-981-19-5967-7_7 Wilson, D. B. (2025). Effect size conversion and computation for meta analysis. https://www.campbellcollaboration.org/ Zawacki Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – Where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 39. https://doi.org/10.1186/s41239-019-0171-0