Investigating the Impact of Chatgpt on Reading Strategies

This study examines the influence of ChatGPT on reading strategies in undergraduate students, concentrating on global reading strategies, problem-solving reading strategies, and supportive reading strategies. The study utilised a quantitative survey design informed by Cognitive Load Theory and the Metacognitive Regulation System, involving 197 participants. Data were gathered utilising a 44-item five-point Likert-scale questionnaire modified from Amer et al. (2010) and Youssef et al. (2024). The results indicated notable disparities in reading methods based on gender and reading proficiency; however, there were no significant differences in perceptions of ChatGPT across these characteristics. Students indicated extensive utilisation of global and problem-solving reading strategies, including the employment of contextual clues and the re-examination of challenging texts. Moreover, participants viewed ChatGPT favourably in fostering critical thinking, intellectual inquiry, and participation. Correlation studies revealed substantial moderate positive correlations between ChatGPT and global reading techniques (r = .337), problem-solving strategies (r = .419), and support methods (r = .440). The study suggests that ChatGPT serves as an effective cognitive scaffold, enhancing strategic reading, critical assessment, and student engagement when utilised with autonomous reading and critical thinking

ChatGPT; Reading Strategies; Academic Reading; Cognitive Load Theory; Metacognitive Regulation System

1. Ahmad, N., Alias, F.A., Hamat, M., & Mohamed, S.A. (2024) Reliability Analysis: Application of Cronbach’s Alpha in Research Instruments. SIG: e-L earning@CS, 114-119. https://appspenang.uitm.edu.my/sigcs/ [Google Scholar] [Crossref]

2. Amer,A., AL Barwani,T., Ibrahim, M. (2010) Student Teachers’ Perceived Use of Online Reading Strategies. International Journal of Education and Development using Information and Communication Technology (IJEDICT), Vol 6(4), pp 102-113. Retrieved from httpe://ijedict.dec.uwi.edu [Google Scholar] [Crossref]

3. He, L. (2024) The Application of SPSS Correlation Analysis in the Study of Precision Teaching of English in Universities. Applied Mathematics and Nonlinear Science, 9(1), 1-13. http://dx.doi.org/10.2478/amns-2024-1371 [Google Scholar] [Crossref]

4. Rahmat, N. H., Arepin, M., & Sulaiman, S. (2020). The cycle of reading fear among undergraduates. Asian Journal of University Education, 16(3), 265–274. https://doi.org/10.24191/ajue.v16i3.9730 [Google Scholar] [Crossref]

5. Shah, M.A.(2024). Meaning and Statistical Analysis of T-Tests in SPSS. Journal of Saidu Medical College, 14(1), 64–65. https://doi.org/10.52206 [Google Scholar] [Crossref]

6. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12, 257–285. https://doi.org/10.1207/s15516709cog1202_4 [Google Scholar] [Crossref]

7. Vetter, T. R. (2017). Descriptive statistics: Reporting the answers to the 5 basic questions of who, what, why, when, where, and a sixth, so what? Anesthesia & Analgesia, 125(5), 1797–1802. https://doi.org/10.1213/ane.0000000000002471 [Google Scholar] [Crossref]

8. Youssef, E., Medhat, M., Abdellatif, S., & Al Malek, M. (2024). Examining the effect of ChatGPT usage on students’ academic learning and achievement: A survey-based study in Ajman, UAE. Computers and Education: Artificial Intelligence, 7, Article 100316. https://doi.org/10.1016/j.caeai.2024.100316 [Google Scholar] [Crossref]

9. Ziegenfuss, J. Y., Casey, A. E., Jennifer, M. D., Meghan, M. J., Thomas, E. K., & Marna, C. (2021). Impact of demographic survey questions on response rate and measurement: A randomized experiment. Survey Practice, 14(1). https://doi.org/10.29115/SP-2021-0010 [Google Scholar] [Crossref]

10. Abdullah, A. D., Qiu, X., Li, H., & Kabilan, M. K. (2025). “It becomes very intelligent”: ChatGPT as an academic reading tool for postgraduates. Reading Research Quarterly, 60(4), e70058. doi:10.1002/rrq.70058 [Google Scholar] [Crossref]

11. Butterfuss, R., Kim, J., & Kendeou, P. (2020). Reading comprehension. In Oxford Research Encyclopedia of Education. doi:10.1093/acrefore/9780190264093.013.865 [Google Scholar] [Crossref]

12. Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025). Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies. Computers & Education, 227, 105224. doi:10.1016/j.compedu.2024.105224 [Google Scholar] [Crossref]

13. Digital Education Council. (2024, August 7). What students want: Key results from DEC Global AI Student Survey 2024. [Google Scholar] [Crossref]

14. Digital Education Council. (2025, January 28). What faculty want: Key results from the Global AI Faculty Survey 2025. [Google Scholar] [Crossref]

15. Huang, B., Dong, Z., & Dou, J. (2025). Exploring the acceptance of large language models as an integrated reading tool: A UTAUT-based analysis. SAGE Open, 15(4). doi:10.1177/21582440251392110 [Google Scholar] [Crossref]

16. 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, 50. doi:10.1186/s40561-024-00342-5 [Google Scholar] [Crossref]

17. Memarian, B., & Doleck, T. (2023). ChatGPT in education: Methods, potentials, and limitations. Computers in Human Behavior: Artificial Humans, 1(2), 100022. doi:10.1016/j.chbah.2023.100022 [Google Scholar] [Crossref]

18. Paas, F., Renkl, A., & Sweller, J. (2003). Cognitive load theory and instructional design: Recent developments. Educational Psychologist, 38(1), 1–4. https://doi.org/10.1207/S15326985EP3801_1 [Google Scholar] [Crossref]

19. Rees, G. P., & Lew, R. (2024). The effectiveness of OpenAI GPT-generated definitions versus definitions from an English learners’ dictionary in a lexically orientated reading task. International Journal of Lexicography, 37(1), 50–74. doi:10.1093/ijl/ecad030 [Google Scholar] [Crossref]

20. Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285. https://doi.org/10.1207/s15516709cog1202_4 [Google Scholar] [Crossref]

21. Sweller, J., Ayres, P., & Kalyuga, S. (2011). Cognitive Load Theory. New York, NY: Springer. [Google Scholar] [Crossref]

22. UNESCO. (2023). Guidance for generative AI in education and research. [Google Scholar] [Crossref]

23. Wang, H., Dang, A., Wu, Z., & Mac, S. (2024). Generative AI in higher education: Seeing ChatGPT through universities’ policies, resources, and guidelines. Computers and Education: Artificial Intelligence, 7, 100326. doi:10.1016/j.caeai.2024.100326 [Google Scholar] [Crossref]

24. Al-Otaibi, S., & Al-Homidhi, A. (2025). Teaching program based on ChatGPT and its effectiveness in developing reading comprehension skills for English language students at university. International Journal of Language and Literary Studies. [Google Scholar] [Crossref]

25. Behforouz, B., & Al Ghaithi, A. (2025). AI meets education: How ChatGPT transforms reading skills in Omani EFL learners. Training, Language and Culture. [Google Scholar] [Crossref]

26. Gharaibeh, M., & Basulayyim, H. (2025). ChatGPT enhances reading comprehension for children with dyslexia in Arabic language. Disability and Rehabilitation: Assistive Technology. [Google Scholar] [Crossref]

27. Ma, Q., et al. (2024). Exploring ChatGPT literacy in language education: A global perspective and comprehensive approach. Computers and Education: Artificial Intelligence. [Google Scholar] [Crossref]

28. Rahmat, N. H. (2026). Exploring the relationship between online reading strategies and ChatGPT usage among undergraduates. Quantum Journal of Social Sciences and Humanities. [Google Scholar] [Crossref]

29. Thongsan, S., & Anderson, J. (2025). From passive answers to active inquiry: How AI supports critical reading in EFL classrooms. LEARN Journal: Language Education and Acquisition Research Network. [Google Scholar] [Crossref]

30. Zhang, Y., et al. (2025). Reading differently with ChatGPT: EFL learners’ use of Question-Answer Relationship strategy. Reading in a Foreign Language. [Google Scholar] [Crossref]

31. Martínez, J. R., & Rodríguez, L. M. (2026). ChatGPT at university: The definitive transition from adoption to quality of student interaction. Education Sciences, 16(4), 515. https://doi.org/10.3390/educsci16040515 [Google Scholar] [Crossref]

32. Dai, Y. (2026) Metacognitive strategy use in GenAI-supported academic reading: a qualitative study of postgraduate students in UK higher education. Front. Psychol. 17:1787647. doi: 10.3389/fpsyg.2026.1787647 [Google Scholar] [Crossref]

33. Iqbal, J., Hashmi, Z. F., Asghar, M. Z., & Abid, M. N. (2025). Generative AI tool use enhances academic achievement in sustainable education through shared metacognition and cognitive offloading among preservice teachers. Scientific Reports, 15(1), 16610. https://doi.org/10.1038/s41598-025-01676-x [Google Scholar] [Crossref]

34. Alyami N (2026) Investigating ChatGPT-mediated mind mapping to facilitate EFL learners’ reading comprehension. PLoS One 21(5): e0336185. https://doi.org/10.1371/journal.pone.0336185 [Google Scholar] [Crossref]

35. Rahmat, N. H. (2026). ChatGPT as a scaffold for strategic academic reading in tertiary education: A bibliometric study. International Journal of Research and Innovation in Social Science, 10(2), 2570–2582. https://doi.org/10.47772/IJRISS.2026.10200193 [Google Scholar] [Crossref]

36. Skulmowski, A., & Xu, K. (2026). Cognitive offloading through digital tools and its relationship with critical thinking, task persistence, and learning depth. Frontiers in Psychology, 17, Article 1781101. https://doi.org/10.3389/fpsyg.2026.1781101 [Google Scholar] [Crossref]

37. Du, J., & Alm, A. (2024). The impact of ChatGPT on English for academic purposes (EAP) students' language learning experience: A self-determination theory perspective. Education Sciences, 14(7), Article 726. https://doi.org/10.3390/educsci14070726 [Google Scholar] [Crossref]

38. Kitichaidateanan, P., Yothachai, N., & Sukying, A. (2026). Modelling cognitive and metacognitive strategies and their relationships to reading comprehension test performance of Thai high school EFL learners. Journal of Language Teaching and Research, 17(3), 801–811. https://jltr.academypublication.com/index.php/jltr/article/view/12631 [Google Scholar] [Crossref]

39. Dahia, I. (2026). Reading reinvented: The role of LLMs in developing EFL students' reading skills. International Journal of Technology in Education and Science, 10, 71-83. https://files.eric.ed.gov/fulltext/EJ1494081.pdf [Google Scholar] [Crossref]

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