Research Article

AI robots as learning companions in PBL: Effects on cognitive and self-regulatory outcomes

Jaitip Nasongkhla 1 * , Chich-Jen Shieh 1
More Detail
1 Excellent Center of Disruptive Innovative Technology for Education, Chulalongkorn University, Bangkok, THAILAND* Corresponding Author
Contemporary Educational Technology, 17(4), October 2025, ep603, https://doi.org/10.30935/cedtech/17417
Published: 12 November 2025
OPEN ACCESS   735 Views   761 Downloads
Download Full Text (PDF)

ABSTRACT

This study examines the effect of robot-assisted problem-based learning (PBL) on learning outcomes in higher education. A quasi-experimental design was adopted, and the application was carried out over 16 weeks (48 hours) with two parallel branches. In the experimental group, a question-and-answer-focused educational robot was integrated into the project-based learning process, while in the control group, the same content was applied without robots. Measurements encompassed self-directed learning (including self-management), problem-solving, and comprehension/discovery dimensions. Bayesian comparisons showed that the level of evidence in favor of the experiment was in the medium-strong range for most variables; a significant increase was observed particularly in self-management. Problem-solving and comprehension/discovery scores were also higher in the experimental condition. Figures and tables report posterior effect sizes and 95% confidence intervals. The results indicate that robot-assisted PBL, which offers timely and elucidative feedback, improves the metacognitive processes associated with PBL and aids students in adhering to their study goals more consistently. In conclusion, robot-assisted PBL can significantly improve self-management, problem-solving, and comprehension outcomes, provided that appropriate feedback design and application fidelity are maintained. Future research should examine its effects across various fields by integrating long-term follow-up periods and behavioral performance assessments. The study enhances the reproducibility of results by evaluating the strength of evidence through open Bayesian reporting. This method makes design choices more transparent and establishes clear guidelines on how educational technologies should be used responsibly.
Keywords: problem-based learning, AI robots, blended learning, learning outcomes, self-directed learning

CITATION (APA)

Nasongkhla, J., & Shieh, C.-J. (2025). AI robots as learning companions in PBL: Effects on cognitive and self-regulatory outcomes. Contemporary Educational Technology, 17(4), ep603. https://doi.org/10.30935/cedtech/17417

REFERENCES

  1. Abildinova, G., Abdykerimova, E., Assainova, A., Mukhtarkyzy, K., & Abykenova, D. (2024). Preparing educators for the digital age: Teacher perceptions of active teaching methods and digital integration. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1473766
  2. Albanese, M. A., & Mitchell, S. (1993). Problem-based learning: A review of literature on its outcomes and implementation issues. Academic Medicine, 68(1), 52–81.
  3. Allen, M., Poggiali, D., Whitaker, K., Marshall, T. R., van Langen, J., & Kievit, R. A. (2019). Raincloud plots: A multi-platform tool for robust data visualization. Wellcome Open Research, 4, Article 63. https://doi.org/10.12688/wellcomeopenres.15191.2
  4. Barry, R.-J., Neumann, M. M., & Neumann, D. L. (2025). Individual differences and young children’s engagement with a social robot. Computers in Human Behavior: Artificial Humans, 4, Article 100139. https://doi.org/10.1016/j.chbah.2025.100139
  5. Bellas, F., Naya-Varela, M., Mallo, A., & Paz-Lopez, A. (2024). Education in the AI era: A long-term classroom technology based on intelligent robotics. Humanities and Social Sciences Communications, 11, Article 1425. https://doi.org/10.1057/s41599-024-03953-y
  6. Belpaeme, T., Kennedy, J., Ramachandran, A., Scassellati, B., & Tanaka, F. (2018). Social robots for education: A review. Science Robotics, 3(21), Article eaat5954. https://doi.org/10.1126/scirobotics.aat5954
  7. Bertacchini, F., Scuro, C., Pantano, P., & Bilotta, E. (2022). A project based learning approach for improving students’ computational thinking skills. Frontiers in Robotics and AI, 9. https://doi.org/10.3389/frobt.2022.720448
  8. Brown, T. A. (2015). Confirmatory factor analysis for applied research. Guilford Publications.
  9. Charisi, V., Gomez, E., Mier, G., Merino, L., & Gomez, R. (2020). Child-robot collaborative problem-solving and the importance of child’s voluntary interaction: A developmental perspective. Frontiers in Robotics and AI, 7. https://doi.org/10.3389/frobt.2020.00015
  10. Chen, H., Kim, Y., Patterson, K., Breazeal, C., & Park, H. W. (2025). Social robots as conversational catalysts: Enhancing long-term human-human interaction at home. Science Robotics, 10(100), Article eadk3307. https://doi.org/10.1126/scirobotics.adk3307
  11. Cook, T. D., Campbell, D. T., & Shadish, W. (2002). Experimental and quasi-experimental designs for generalized causal inference. Houghton Mifflin.
  12. Donnermann, M., Schaper, P., & Lugrin, B. (2022). Social robots in applied settings: A long-term study on adaptive robotic tutors in higher education. Frontiers in Robotics and AI, 9. https://doi.org/10.3389/frobt.2022.831633
  13. Duda, H. J., Wibowo, D. C., Suryani, N. Y., Thoharudin, M., & Budiman, J. (2024). Problem-based learning through authentic assessment: Students’ cognitive and creative thinking abilities. Pedagogika, 156(4), 52–73. https://doi.org/10.15823/p.2024.156.3
  14. Erikson, M. G., & Erikson, M. (2018). Learning outcomes and critical thinking–Good intentions in conflict. Studies in Higher Education, 44(12), 2293–2303. https://doi.org/10.1080/03075079.2018.1486813
  15. Fleckenstein, J., Liebenow, L. W., & Meyer, J. (2023). Automated feedback and writing: A multi-level meta-analysis of effects on students’ performance. Frontiers in Artificial Intelligence, 6. https://doi.org/10.3389/frai.2023.1162454
  16. Fooladvand, M., & Nadi, M. A. (2019). Validation of revisited self-directed learning readiness scale for nursing education among Iranian nursing and midwifery students. Journal of Education and Health Promotion, 8, Article 266. https://doi.org/10.4103/jehp.jehp_82_19
  17. Fung, K. Y., Fung, K. C., Lui, T. L. R., Sin, K. F., Lee, L. H., Qu, H., & Song, S. (2025). Exploring the impact of robot interaction on learning engagement: A comparative study of two multi-modal robots. Smart Learning Environments, 12, Article 12. https://doi.org/10.1186/s40561-024-00362-1
  18. Fung, K. Y., Lee, L. H., Sin, K. F., Song, S., & Qu, H. (2024). Humanoid robot-empowered language learning based on self-determination theory. Education and Information Technologies, 29(14), 18927–18957. https://doi.org/10.1007/s10639-024-12570-w
  19. Ge, W.-L., Zhu, X.-Y., Lin, J. B., Jiang, J.-J., Li, T., Lu, Y.-F., Mi, Y.-F., & Tung, T.-H. (2025). Critical thinking and clinical skills by problem-based learning educational methods: An umbrella systematic review. BMC Medical Education, 25, Article 455. https://doi.org/10.1186/s12909-025-06951-z
  20. Guntur, M., & Purnomo, Y. W. (2024). A meta-analysis of self-regulated learning interventions studies on learning outcomes in online and blended environments. Online Learning, 28(3), 563–584. https://doi.org/10.24059/olj.v28i3.4025
  21. Guo, K., Zhang, E. D., Li, D., & Yu, S. (2024). Using AI-supported peer review to enhance feedback literacy: An investigation of students’ revision of feedback on peers’ essays. British Journal of Educational Technology, 56(4), 1612–1639. https://doi.org/10.1111/bjet.13540
  22. Hesse, F., Care, E., Buder, J., Sassenberg, K., & Griffin, P. (2015). A framework for teachable collaborative problem solving skills. In P. Griffin, & E. Care (Eds.), Assessment and teaching of 21st century skills. Educational assessment in an information age (pp. 37–56). Springer. https://doi.org/10.1007/978-94-017-9395-7_2
  23. Hrastinski, S. (2019). What do we mean by blended learning? TechTrends, 63(5), 564–569. https://doi.org/10.1007/s11528-019-00375-5
  24. Huang, C., Zhong, Y., Li, Y., Wang, X., Han, Z., Zhang, D., & Liu, M. (2025). Enhancing student reading performance through a personalized two-tier problem-based learning approach with generative artificial intelligence. Humanities and Social Sciences Communications, 12, Article 645. https://doi.org/10.1057/s41599-025-04919-4
  25. Huang, G., & Moore, R. K. (2023). Using social robots for language learning: Are we there yet? Journal of China Computer-Assisted Language Learning, 3(1), 208–230. https://doi.org/10.1515/jccall-2023-0013
  26. Jaganathan, S., Bhuminathan, S., & Ramesh, M. (2024). Problem-based learning–An overview. Journal of Pharmacy and Bioallied Sciences, 16(Suppl 2), S1435–S1437. https://doi.org/10.4103/jpbs.jpbs_820_23
  27. JASPTeam. (2025). JASP (version 0.19). JASP. https://jasp-stats.org/download/
  28. Kaliisa, R., Misiejuk, K., López-Pernas, S., & Saqr, M. (2025). How does artificial intelligence compare to human feedback? A meta-analysis of performance, feedback perception, and learning dispositions. Educational Psychology. https://doi.org/10.1080/01443410.2025.2553639
  29. Kar, S., Akkilagunta, S., Premarajan, K. C., Lakshminarayanan, S., Ramalingam, A., Chacko, T., & Bhandary, S. (2019). Assessment of reliability and adaptation of fisher’s 52-item self-directed learning readiness scale among medical students in Southern India. International Journal of Advanced Medical and Health Research, 6(1), 7–11. https://doi.org/10.4103/ijamr.Ijamr_39_18
  30. Kass, R. E., & Raftery, A. E. (1995). Bayes factors. Journal of the American Statistical Association, 90(430), 773–795. https://doi.org/10.1080/01621459.1995.10476572
  31. Kim, H., Hwang, J., Kim, T., Choi, M., Lee, D., & Ko, J. (2025). Impact of generative artificial intelligence on learning: Scaffolding strategies and self-directed learning perspectives. International Journal of Human-Computer Interaction. https://doi.org/10.1080/10447318.2025.2531267
  32. Kong, S.-C., Cheung, M.-Y. W., & Tsang, O. (2024). Developing an artificial intelligence literacy framework: Evaluation of a literacy course for senior secondary students using a project-based learning approach. Computers and Education: Artificial Intelligence, 6, Article 100214. https://doi.org/10.1016/j.caeai.2024.100214
  33. Kruschke, J. K. (2021). Bayesian analysis reporting guidelines. Nature Human Behaviour, 5, 1282–1291. https://doi.org/10.1038/s41562-021-01177-7
  34. Kumar, A. P., Omprakash, A., Mani, P. K. C., Swaminathan, N., Maheshkumar, K., Maruthy, K. N., Sathiyasekaran, B. W. C., Vijayaraghavan, P. V., & Padmavathi, R. (2021). Validation of internal structure of self-directed learning readiness scale among Indian medical students using factor analysis and the structural equation modelling approach. BMC Medical Education, 21, Article 614. https://doi.org/10.1186/s12909-021-03035-6
  35. Lampropoulos, G. (2025). Social robots in education: Current trends and future perspectives. Information, 16(1), Article 29. https://doi.org/10.3390/info16010029
  36. Langer, A., Marshall, P. J., & Levy-Tzedek, S. (2023). Ethical considerations in child-robot interactions. Neuroscience & Biobehavioral Reviews, 151, Article 105230. https://doi.org/10.1016/j.neubiorev.2023.105230
  37. Leary, H., Walker, A., Lefler, M., & Kuo, Y.-C. (2019). Self-directed learning in problem-based learning. In M. Moallem, W. Hung, & N. Dabbagh (Eds.), The Wiley handbook of problem-based learning (pp. 181–198). Wiley. https://doi.org/10.1002/9781119173243.ch8
  38. Letourneau, A., Deslandes Martineau, M., Charland, P., Karran, J. A., Boasen, J., & Leger, P. M. (2025). A systematic review of AI-driven intelligent tutoring systems (ITS) in K-12 education. npj Science of Learning, 10, Article 29. https://doi.org/10.1038/s41539-025-00320-7
  39. Li, B., Bonk, C. J., Wang, C., & Kou, X. (2024a). Reconceptualizing self-directed learning in the era of generative AI: An exploratory analysis of language learning. IEEE Transactions on Learning Technologies, 17, 1489–1503. https://doi.org/10.1109/tlt.2024.3386098
  40. Li, B., Yu, J., Sun, L., & Yang, H. (2024b). Impact of active learning instruction in blended learning on students’ anxiety levels and performance. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1332778
  41. Li, S., Kwok, S. W. H., Siu, S. C. N., Chung, J. Y. S., Lam, H. C. Y., Tsang, E. Y. M., Li, K. C., Yeung, J. W. Y., & Lam, S. C. (2023). Development of generic student engagement scale in higher education: An application on healthcare students. Nursing Open, 10(3), 1545–1555. https://doi.org/10.1002/nop2.1405
  42. Lin, V., Yeh, H.-C., & Chen, N.-S. (2022). A systematic review on oral interactions in robot-assisted language learning. Electronics, 11(2), Article 290. https://doi.org/10.3390/electronics11020290
  43. Mavridis, N. (2015). A review of verbal and non-verbal human-robot interactive communication. Robotics and Autonomous Systems, 63(P1), 22–35. https://doi.org/10.1016/j.robot.2014.09.031
  44. McNeish, D. (2018). Thanks coefficient alpha, we’ll take it from here. Psychological Methods, 23(3), 412–433. https://doi.org/10.1037/met0000144
  45. Megahed, N., & Ghoneim, E. (2022). Blended learning: The new normal for post-COVID-19 pedagogy. International Journal of Mobile and Blended Learning, 14(1), 1–16. https://doi.org/10.4018/IJMBL.291980
  46. Mubin, S., Stevens, C. J., Shahid, S., Al Mahmud, A., & Dong, J.-J. (2013). A review of the applicability of robots in education. Technology in Education and Learning, 1. https://doi.org/10.2316/Journal.209.2013.1.209-0015
  47. Nannim, F. A., Ibezim, N. E., Mosia, M., & Oguguo, B. C. E. (2025). Project-based learning with Arduino robots: Impact on undergraduate students’ achievement and task persistence in robotics programming. Frontiers in Robotics and AI, 12. https://doi.org/10.3389/frobt.2025.1615427
  48. Nasir, J., Bruno, B., & Dillenbourg, P. (2024). Social robots as skilled ignorant peers for supporting learning. Frontiers in Robotics and AI, 11. https://doi.org/10.3389/frobt.2024.1385780
  49. Niyomves, B., Kunacheva, N., & Sutadarat, S. (2024). Hybrid learning: A combination of face-to-face and online learning. Journal of Education and Learning Reviews, 1(3), 11–20. https://doi.org/10.60027/jelr.2024.673
  50. OECD. (2017). PISA 2015 results (volume V). OECD Publishing. https://doi.org/10.1787/9789264285521-en
  51. Ouyang, F., & Xu, W. (2024). The effects of educational robotics in STEM education: A multilevel meta-analysis. International Journal of STEM Education, 11, Article 7. https://doi.org/10.1186/s40594-024-00469-4
  52. Pai, R. Y., Shetty, A., Dinesh, T. K., Shetty, A. D., & Pillai, N. (2024). Effectiveness of social robots as a tutoring and learning companion: A bibliometric analysis. Cogent Business & Management, 11(1), Article 2299075. https://doi.org/10.1080/23311975.2023.2299075
  53. Platonova, R. I., Orekhovskaya, N. A., Dautova, S. B., Martynenko, E. V., Kryukova, N. I., & Demir, S. (2022). Blended learning in higher education: Diversifying models and practical recommendations for researchers. Frontiers in Education, 7. https://doi.org/10.3389/feduc.2022.957199
  54. Rong, M., Yao, Y., Li, Q., & Chen, X. (2025). Exploring student engagement with artificial intelligence-guided chatbot feedback in EFL writing: Interactions and revisions. Computer Assisted Language Learning. https://doi.org/10.1080/09588221.2025.2539979
  55. Rouder, J. N., Speckman, P. L., Sun, D., Morey, R. D., & Iverson, G. (2009). Bayesian t tests for accepting and rejecting the null hypothesis. Psychonomic Bulletin & Review, 16, 225–237. https://doi.org/10.3758/PBR.16.2.225
  56. Servant, V. F. C., & Schmidt, H. G. (2016). Revisiting foundations of problem-based learning: Some explanatory notes. Medical Education, 50(7), 698–701. https://doi.org/10.1111/medu.12803
  57. Sharma, D., Sood, A. K., Darius, P. S. H., Gundabattini, E., Darius Gnanaraj, S., & Joseph Jeyapaul, A. (2022). A study on the online-offline and blended learning methods. Journal of the Institution of Engineers (India): Series B, 103(4), 1373–1382. https://doi.org/10.1007/s40031-022-00766-y
  58. Singh, J., Steele, K., & Singh, L. (2021). Combining the best of online and face-to-face learning: Hybrid and blended learning approach for COVID-19, post vaccine, & post-pandemic world. Journal of Educational Technology Systems, 50(2), 140–171. https://doi.org/10.1177/00472395211047865
  59. Su, T., Liu, J., Meng, L., Luo, Y., Ke, Q., & Xie, L. (2025). The effectiveness of problem-based learning (PBL) in enhancing critical thinking skills in medical education: A systematic review and meta-analysis. Frontiers in Education, 10. https://doi.org/10.3389/feduc.2025.1565556
  60. Trapero-González, I., Hinojo-Lucena, F. J., Romero-Rodríguez, J.-M., & Martínez-Menéndez, A. (2024). Didactic impact of educational robotics on the development of STEM competence in primary education: A systematic review and meta-analysis. Frontiers in Education, 9. https://doi.org/10.3389/feduc.2024.1480908
  61. Venter, J., Coetzee, S. A., & Schmulian, A. (2024). Exploring the use of artificial intelligence (AI) in the delivery of effective feedback. Assessment & Evaluation in Higher Education, 50(4), 516–536. https://doi.org/10.1080/02602938.2024.2415649
  62. Verhelst, E., Janssens, R., Demeester, T., & Belpaeme, T. (2024). Adaptive second language tutoring using generative AI and a social robot. In Proceedings of the Companion of the 2024 ACM/IEEE International Conference on Human-Robot Interaction (pp. 1080–1084). ACM. https://doi.org/10.1145/3610978.3640559
  63. Wagenmakers, E. J., Love, J., Marsman, M., Jamil, T., Ly, A., Verhagen, J., Selker, R., Gronau, Q. F., Dropmann, D., Boutin, B., Meerhoff, F., Knight, P., Raj, A., van Kesteren, E. J., van Doorn, J., Smira, M., Epskamp, S., Etz, A., Matzke, D., … Morey, R. D. (2018). Bayesian inference for psychology. Part II: Example applications with JASP. Psychonomic Bulletin & Review, 25, 58–76. https://doi.org/10.3758/s13423-017-1323-7
  64. Wang, J., & Fan, W. (2025). The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: Insights from a meta-analysis. Humanities and Social Sciences Communications, 12, Article 621. https://doi.org/10.1057/s41599-025-04787-y
  65. Wu, D., Zhang, S., Ma, Z., Yue, X.-G., & Dong, R. K. (2024). Unlocking potential: Key factors shaping undergraduate self-directed learning in AI-enhanced educational environments. Systems, 12(9), Article 332. https://doi.org/10.3390/systems12090332
  66. Xu, W., & Ouyang, F. (2025). Robotic roles in education: A systematic review based on a proposed framework of the learner-robot relationships. Educational Research Review, 47, Article 100685. https://doi.org/10.1016/j.edurev.2025.100685
  67. Yang, Q.-F., Lian, L.-W., & Zhao, J.-H. (2023). Developing a gamified artificial intelligence educational robot to promote learning effectiveness and behavior in laboratory safety courses for undergraduate students. International Journal of Educational Technology in Higher Education, 20, Article 18. https://doi.org/10.1186/s41239-023-00391-9
  68. Younas, M., Abdel Salam El-Dakhs, D., & Jiang, Y. (2025). A comprehensive systematic review of AI-driven approaches to self-directed learning. IEEE Access, 13, 38387–38403. https://doi.org/10.1109/access.2025.3546319
  69. Yu, Q., Yu, K., & Wang, J. (2025). Unraveling the impact of blended learning vs. online learning on learners’ performance: Perspective of self-determination theory. Behavioral Science, 15(9), Article 1263. https://doi.org/10.3390/bs15091263
  70. Zakaria, M. I., Abdullah, A. H., Alhassora, N. S. A., Osman, S., & Ismail, N. (2025). The impact of M-learning and problem-based learning teaching method on students motivation and academic performance. International Journal of Instruction, 18(1), 503–518. https://doi.org/10.29333/iji.2025.18127a
  71. Zhang, L., & Ma, Y. (2023). A study of the impact of project-based learning on student learning effects: A meta-analysis study. Frontiers in Psychology, 14. https://doi.org/10.3389/fpsyg.2023.1202728
  72. Zhou, G., Liu, W., Guo, S., & Lin, Y. (2025). Where are we in online project-based learning? Evidence from a visualized bibliometric review (2013–2023). Computer Applications in Engineering Education, 33(3), Article e70044. https://doi.org/10.1002/cae.70044
  73. Zong, Y., & Yang, L. (2025). How AI-enhanced social-emotional learning framework transforms EFL students’ engagement and emotional well-being. European Journal of Education, 60(1), Article e12925. https://doi.org/10.1111/ejed.12925