From AI Assistance to Evidence-Based Inquiry: Developing an AI-Supported Problem-Based Learning Model for EFL Academic Reading
DOI:
https://doi.org/10.29408/veles.v10i2.34176Keywords:
Artificial Intelligence, academic reading, problem-based learning, metacognitive regulation, higher educationAbstract
Artificial intelligence (AI) can support academic reading, yet its instructional value depends on whether students use it to interrogate texts or merely obtain ready-made answers. This study developed and preliminarily evaluated an AI-supported Problem-Based Learning (PBL) model designed to promote evidence-based inquiry in EFL academic reading. Following the ADDIE framework, the study produced a six-session instructional module and a structured prompting protocol that guided students to formulate questions, examine textual evidence, evaluate arguments, and reflect on their interpretations. The model was implemented with 22 undergraduate EFL students enrolled in an Academic Reading course at a public university in Indonesia. Data were collected through expert validation, pre- and post-intervention academic reading assessments, a student perception questionnaire, classroom observations, AI-interaction logs, and reflective justification sheets. Quantitative data were analysed using descriptive statistics and a paired-samples t-test, while qualitative data were examined thematically. Students’ mean academic reading score increased from 61.45 to 81.32, with a statistically significant difference between the pretest and posttest scores, t(21) = 9.42, p < .001. The most substantial improvements occurred in coherence evaluation and inferential reasoning. Questionnaire, observational, and interaction-log data also indicated positive student perceptions and progressively greater engagement in inferential questioning, textual justification, argument evaluation, and reflective revision. These findings suggest that structured AI-supported PBL may help shift students’ use of AI from answer seeking toward evidence-based academic inquiry. Nevertheless, the small single-institution sample, absence of a comparison group, and lack of direct cognitive-load measurement require the findings to be interpreted as preliminary.
References
Alifah, N., & Hidayat, A. R. (2025). Effectiveness of artificial intelligence-based learning analytics tools in supporting personalized learning in higher education. Jurnal Pendidikan Progresif, 15(1), 74–84. https://doi.org/10.23960/jpp.v15i1.pp74-84
Arani, S. M. N. (2025). AI-mediated problem-based language learning: Easing the load for teachers, empowering students. Computer Assisted Language Learning, 1–27. https://doi.org/10.1080/09588221.2025.2599156
Belland, B. R., Walker, A. E., Kim, N. J., & Lefler, M. (2017). Synthesizing results from empirical research on computer-based scaffolding in STEM education: A meta-analysis. Review of Educational Research, 87(2), 309–344. https://doi.org/10.3102/0034654316670999
Berenji, S. (2021). Enhancing metacognitive scaffolding and comprehension ability through problem-based learning in an EFL context. Education Research International, 2021, Article 6766793. https://doi.org/10.1155/2021/6766793
Bhimavarapu, U. (2025). Personalized learning in the digital era: Unlocking potential through AI and data analytics. In J. Basha, T. Alade, M. Al Khazimi, R. Vasudevan, & J. Khan (Eds.), Optimizing research techniques and learning strategies with digital technologies (pp. 187–210). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-7863-2.ch007
Branch, R. M. (2009). Instructional design: The ADDIE approach. Springer. https://doi.org/10.1007/978-0-387-09506-6
Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa
Chakraborty, S., Misra, B., & Dey, N. (2024). AI-empowered knowledge management in education. Springer. https://doi.org/10.1007/978-981-97-2574-8
Chang, D. H., Lin, M. P., Hajian, S., & Wang, Q. Q. (2023). Educational design principles of using AI chatbots that support self-regulated learning in education: Goal setting, feedback, and personalization. Sustainability, 15(17), Article 12921. https://doi.org/10.3390/su151712921
Chaudhary, J., & Goyal, J. S. (2025). The future of AI in education: A conceptual exploration. International Journal of Indian Psychology, 13(1). https://doi.org/10.25215/1301.289
Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
Cohen, L., Manion, L., & Morrison, K. (2018). Research methods in education (8th ed.). Routledge.
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE.
Cronbach, L. J. (1951). Coefficient alpha and the internal structure of tests. Psychometrika, 16(3), 297–334. https://doi.org/10.1007/BF02310555
Dignath, C., & Veenman, M. V. J. (2021). The role of direct strategy instruction and indirect activation of self-regulated learning: Evidence from classroom observation studies. Educational Psychology Review, 33(2), 489–533. https://doi.org/10.1007/s10648-020-09534-0
Elleman, A. M. (2017). Examining the impact of inference instruction on the literal and inferential comprehension of skilled and less skilled readers: A meta-analytic review. Journal of Educational Psychology, 109(6), 761–781. https://doi.org/10.1037/edu0000180
Etikan, I., Musa, S. A., & Alkassim, R. S. (2016). Comparison of convenience sampling and purposive sampling. American Journal of Theoretical and Applied Statistics, 5(1), 1–4. https://doi.org/10.11648/j.ajtas.20160501.11
Farrokhnia, M., Banihashem, S. K., Noroozi, O., & Wals, A. (2024). A SWOT analysis of ChatGPT: Implications for educational practice and research. Innovations in Education and Teaching International, 61(3), 460–474. https://doi.org/10.1080/14703297.2023.2195846
Feng, L. (2025). Investigating the effects of artificial intelligence-assisted language learning strategies on cognitive load and learning outcomes: A comparative study. Journal of Educational Computing Research, 62(8), 1741–1774. https://doi.org/10.1177/07356331241268349
Fitria, W., & Nafiah, U. (2025). Enhancing students’ inferential reading skills through a higher-order-thinking-skills-based module: An experimental study. International Journal of Education and Teaching Zone, 4(2). https://doi.org/10.57092/ijetz.v4i2.434
Fu, Y., & Hiniker, A. (2025). Supporting students’ reading and cognition with AI [Preprint]. arXiv. https://doi.org/10.48550/arXiv.2504.13900
Hadwin, A. F., Järvelä, S., & Miller, M. (2018). Self-regulation, co-regulation, and shared regulation in collaborative learning environments. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 83–106). Routledge.
Hafizah, M., Solin, S., Purba, C. T., Sihotang, M. M., Rahmad, R., & Wirda, M. A. (2024). Meta-analysis: The impact of problem-based learning (PBL) models on students’ critical thinking skills. Journal of Digital Learning and Education, 4(3), 1–13. https://doi.org/10.52562/jdle.v4i3.1393
Handage, S., & Chander, M. (2021). Development of an instrument for measuring student learning outcomes: A content validation process. Indian Journal of Extension Education, 57(3), 1–7. https://doi.org/10.48165/ijee.2021.57302
Hmelo-Silver, C. E. (2004). Problem-based learning: What and how do students learn? Educational Psychology Review, 16(3), 235–266. https://doi.org/10.1023/B:EDPR.0000034022.16470.f3
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Jaiswal, P. (2025). Cognitive, metacognitive, and digital reading strategies in English education: A 20-year review. International Journal of Linguistics, Literature and Translation, 8(9), 10–18. https://doi.org/10.32996/ijllt.2025.8.9.2
Johnson, R. B., & Christensen, L. (2020). Educational research: Quantitative, qualitative, and mixed approaches (7th ed.). SAGE.
Kanthimathi, S., Nanda, P., Venkatraman, S., Renganayagan, V., & Eswaran, S. (2025). Transforming education through AI-powered personalized assessment models. In Adopting artificial intelligence tools in higher education (pp. 136–154). CRC Press.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., Krusche, S., Kutyniok, G., Michaeli, T., Nerdel, C., Pfeffer, J., Poquet, O., Sailer, M., Schmidt, A., Seidel, T., . . . Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, Article 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kendeou, P., McMaster, K. L., & Christ, T. J. (2016). Reading comprehension: Core components and processes. Policy Insights from the Behavioral and Brain Sciences, 3(1), 62–69. https://doi.org/10.1177/2372732215624707
Kim, N. J., Belland, B. R., & Walker, A. E. (2018). Effectiveness of computer-based scaffolding in the context of problem-based learning for STEM education: Bayesian meta-analysis. Educational Psychology Review, 30(2), 397–429. https://doi.org/10.1007/s10648-017-9419-1
Kooli, C., & Yusuf, N. (2024). Transforming educational assessment: Insights into the use of ChatGPT and large language models in grading. International Journal of Human–Computer Interaction, 1–12. https://doi.org/10.1080/10447318.2024.2338330
Lan, M., & Zhou, X. (2025). A qualitative systematic review on AI-empowered self-regulated learning in higher education. npj Science of Learning, 10(1), Article 21. https://doi.org/10.1038/s41539-025-00319-0
Law, L. (2024). Application of generative artificial intelligence (GenAI) in language teaching and learning: A scoping literature review. Computers and Education Open, 6, Article 100174. https://doi.org/10.1016/j.caeo.2024.100174
Lo, C. K., Yu, P. L. H., Xu, S., Ng, D. T. K., & Jong, M. S.-Y. (2024). Exploring the application of ChatGPT in ESL/EFL education and related research issues: A systematic review of empirical studies. Smart Learning Environments, 11, Article 50. https://doi.org/10.1186/s40561-024-00342-5
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Matcha, W., Gašević, D., Jovanović, J., Uzir, N. A. A., Oliver, C. W., & Murray, A. (2020). Analytics of learning strategies: The association with personality traits. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (pp. 151–160). Association for Computing Machinery.
Molenaar, I. (2022). The concept of hybrid human–AI regulation: Exemplifying how to support young learners’ self-regulated learning. Computers and Education: Artificial Intelligence, 3, Article 100070. https://doi.org/10.1016/j.caeai.2022.100070
Nguyễn, T. M. T. (2021). Using problem-based tasks to promote higher-order thinking skills for TESOL MA students in Vietnam. Interdisciplinary Journal of Problem-Based Learning, 15(1). https://doi.org/10.14434/ijpbl.v15i1.28772
O’Reilly, T., Wang, Z., & Sabatini, J. (2019). How much knowledge is too little? When a lack of knowledge becomes a barrier to comprehension. Psychological Science, 30(9), 1344–1351. https://doi.org/10.1177/0956797619862276
Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, Article 100020. https://doi.org/10.1016/j.caeai.2021.100020
Pan, Z., & Liu, M. (2022). The role of an adaptive scaffolding system in supporting middle school problem-based learning activities. Journal of Educational Technology Systems, 51(2), 117–145. https://doi.org/10.1177/00472395221133855
Rokeman, N. R. M. (2024). Likert measurement scale in education and social sciences: Explored and explained. EDUCATUM Journal of Social Sciences, 10, 77–88. https://doi.org/10.37134/ejoss.vol10.1.7.2024
Safarli, N. T., Jafarova, I. M., Nazirzada, L. N., Jumayeva, L., Deepa, B., Antar, D., & Mehdizade, N. S. (2025). Foreign language proficiency among EFL students in higher education and usage of artificial intelligence. Forum for Linguistic Studies, 7(8), 646–665. https://doi.org/10.30564/fls.v7i8.11067
Saint, J., Fan, Y., Gašević, D., & Pardo, A. (2022). Temporally focused analytics of self-regulated learning: A systematic review of literature. Computers and Education: Artificial Intelligence, 3, Article 100060. https://doi.org/10.1016/j.caeai.2022.100060
Savery, J. R. (2015). Overview of problem-based learning: Definitions and distinctions. In A. Walker, H. Leary, C. E. Hmelo-Silver, & P. A. Ertmer (Eds.), Essential readings in problem-based learning: Exploring and extending the legacy of Howard S. Barrows (pp. 5–15). Purdue University Press.
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity Press.
Shin, M. (2021). Development of an English teaching model applying artificial intelligence through maker education. Journal of the Korea Convergence Society, 12(3), 61–67. https://doi.org/10.15207/JKCS.2021.12.3.061
Song, X., Razali, A. B., & Jeyaraj, J. J. (2025). How project-based learning improves college EFL learners’ critical thinking skills and reading comprehension ability: A case study. Language Teaching Research. Advance online publication. https://doi.org/10.1177/13621688251352275
Sweller, J., van Merriënboer, J. J. G., & Paas, F. (2019). Cognitive architecture and instructional design: 20 years later. Educational Psychology Review, 31(2), 261–292. https://doi.org/10.1007/s10648-019-09465-5
Taky-Eddine, O., & Madaoui, R. (2024). Cognitive overload in the hypertext reading environment. International Journal of English Language Studies, 6(2), 94–100. https://doi.org/10.32996/ijels.2024.6.2.13
Thongsan, N. C., & Anderson, N. J. (2025). From passive answers to active inquiry: How AI supports critical reading in EFL classrooms. LEARN Journal: Language Education and Acquisition Research Network, 18(2), 795–820. https://doi.org/10.70730/kmkl8505
Torres, V. (2025). Unlocking potential: Teaching bilingual students how to think using metacognitive strategies [Unpublished manuscript or graduate project]. California State University, Northridge.
Tuononen, T., Hyytinen, H., Räisänen, M., Hailikari, T., & Parpala, A. (2022). Metacognitive awareness in relation to university students’ learning profiles. Metacognition and Learning, 18(1), 37–54. https://doi.org/10.1007/s11409-022-09314-x
van Merriënboer, J. J. G., & Sweller, J. (2005). Cognitive load theory and complex learning: Recent developments and future directions. Educational Psychology Review, 17(2), 147–177. https://doi.org/10.1007/s10648-005-3951-0
Viberg, O., Khalil, M., & Baars, M. (2020). Self-regulated learning and learning analytics in online learning environments: A review of empirical research. In Proceedings of the 10th International Conference on Learning Analytics & Knowledge (pp. 524–533). Association for Computing Machinery. https://doi.org/10.1145/3375462.3375483
Wijaya, K. F. (2021). Collaborative and problem-based learning in promoting Indonesian EFL learners’ learning autonomy. Polyglot: Jurnal Ilmiah, 17(2), 191–211. https://doi.org/10.19166/pji.v17i2.3590
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning, Media and Technology, 45(3), 223–235. https://doi.org/10.1080/17439884.2020.1798995
Winne, P. H. (2018). Cognition and metacognition within self-regulated learning. In D. H. Schunk & J. A. Greene (Eds.), Handbook of self-regulation of learning and performance (2nd ed., pp. 36–48). Routledge.
Yan, L., Sha, L., Zhao, L., Li, Y., Martinez-Maldonado, R., Chen, G., Li, X., Jin, Y., & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90–112. https://doi.org/10.1111/bjet.13370
Yew, E. H. J., & Goh, K. (2016). Problem-based learning: An overview of its process and impact on learning. Health Professions Education, 2(2), 75–79. https://doi.org/10.1016/j.hpe.2016.01.004
Yin, J., & Gulnaz, A. (2025). Enhancing teaching effectiveness in ancient architecture colour painting through blended design. [The supplied journal title, SHILAP Revista de Lepidopterología, does not match DOI https://doi.org/10.48619/ais.v6i1.1093; verify the actual journal title before inclusion.]
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, Article 39. https://doi.org/10.1186/s41239-019-0171-0
Zimmerman, B. J. (1994). Dimensions of academic self-regulation: A conceptual framework for education. In D. H. Schunk & B. J. Zimmerman (Eds.), Self-regulation of learning and performance: Issues and educational applications (pp. 3–21). Routledge. https://doi.org/10.4324/9780203763353-1
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