Enhancing University Students' Reading Comprehension through AI-Supported Scaffolding with ChatGPT
Abstract
This quasi-experimental study investigated the effects of ChatGPT-supported pre-reading scaffolding on the English reading comprehension of university students. Forty-eight students from Universitas Nusantara PGRI Kediri participated in the study and were assigned to an experimental group receiving ChatGPT-supported pre-reading scaffolding and a control group receiving traditional teacher-led instruction over one academic semester. The intervention consisted of structured pre-reading activities involving vocabulary pre-teaching, background knowledge activation, predictive questioning, and interactive clarification through ChatGPT. These activities were designed to prepare students for reading by activating prior knowledge, developing relevant vocabulary, and establishing content schemata before engagement with target texts. Reading comprehension outcomes were analyzed using ANCOVA. The results showed that the experimental group significantly outperformed the control group, F(1, 63) = 5.06, p = 0.02, partial η² = 0.07, indicating a significant effect of ChatGPT-supported pre-reading scaffolding on students’ reading comprehension. The findings suggest that ChatGPT can serve as a complementary scaffolding tool that supports vocabulary preparation, background knowledge activation, and individualized pre-reading support. However, its integration should remain guided by teacher expertise to ensure effective and responsible use in EFL reading instruction.
Keywords
Full Text:
PDFReferences
Ajideh, P. (2006). Schema-theory based considerations on pre-reading activities in ESP textbooks. Asian EFL Journal, 16, 1–19.
Alemi, M., & Ebadi, S. (2010). The effects of pre-reading activities on ESP reading comprehension. Journal of Language Teaching and Research, 1(5), 569–577.
Alyami, N. (2026). Investigating ChatGPT-mediated mind mapping to facilitate EFL learners’ reading comprehension. PLoS ONE, 21(5). https://doi.org/10.1371/journal.pone.0336185
Brown, H. D. (2004). Language Assessment: Principles and Classroom Practices. Pearson Education.
Capili, B., & Anastasi, J. A. (2024). An Introduction to the Quasi-Experimental Design (Nonrandomized Design). Journal of Nursing Education and Practice.
Chea, P., & Xiao, Y. (2024). Artificial intelligence in higher education: The power and damage of AI-assisted tools on academic English reading skills. Journal of General Education and Humanities, 3(3), 287–306.
Cho, Y. A., & Ma, J. H. (2020). The effects of schema activation and reading strategy use on L2 reading comprehension. English Teaching, 75(3), 49–68.
Crompton, H., Edmett, A., Ichaporia, N., & Burke, D. (2024). Artificial Intelligence and English Language Teaching: Affordances and Challenges. British Journal of Educational Technology, 55(6), 2503–2529. https://doi.org/10.1111/bjet.13460
Grabe, W., & Stoller, F. L. (2019). Teaching and researching reading (3rd ed.). Routledge.
Graves, M. F., & Graves, B. B. (2003). Scaffolding reading experiences: Designs for student success. Christopher-Gordon.
Johnson, C. W. (1986). A More Rigorous Quasi-Experimental Alternative to the One-Group Pretest-Posttest Design. Educational and Psychological Measurement, 46(3), 585–591. https://doi.org/10.1177/0013164486463011
Johnson, R. B., & Christensen, L. (2014). Educational Research quantitative, qualitative, and mixed approaches.
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Günnemann, S., Hüllermeier, E., & Krusche, S. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Kim, H., Cha, Y., & Kim, N. (2025). Integrating generative AI and machine translation into EFL reading and vocabulary instruction. Multimedia-Assisted Language Learning, 28(3), 55–80.
Kim, J., Yu, S., Detrick, R., & Li, N. (2025). Exploring students’ perspectives on generative AI-assisted academic writing. Education and Information Technologies, 30(1), 1265–1300.
Lin, C.-C., Lin, T.-H., & Tang, C.-K. (2025). Enhancing English reading comprehension, learning motivation and attitude through AI-supported pre-reading scaffolding. Journal of Computer Assisted Learning, 41(6). https://doi.org/10.1111/jcal.70150
Shadish, W. R., Cook, T. D., & Campbell, D. T. (2002). Experimental and Quasi-Experimental Designs for Generalized Causal Inference. Houghton Mifflin.
Shadish, W. R., & Luellen, J. K. (2018). Quasi-Experimental Design. In Handbook of Complementary Methods in Education Research. Routledge.
Sirita, U., & Todd, R. W. (2025). Exploring and comparing scaffolding strategies of ChatGPT-3.5 and a customized GPT for reading comprehension. REFLections, 32(3), 1793–1819.
Sweller, J. (1988). Cognitive load during problem solving: Effects on learning. Cognitive Science, 12(2), 257–285.
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.
van Breukelen, G. J. P. (2013). ANCOVA versus change from baseline in nonrandomized studies: The difference. Multivariate Behavioral Research, 48(6), 895–922. https://doi.org/10.1080/00273171.2013.831743
Vygotsky, L. S. (1978a). Mind in Society: The Development of Higher Psychological Processes. In Harvard University Press. https://doi.org/https://doi.org/10.2307/j.ctvjf9vz4
Vygotsky, L. S. (1978b). Mind in Society The Development of Higher Psychological Processes, Knox and Carol. Trans, Harvard Business School Press, Cambridge ….
DOI: https://doi.org/10.31004/jele.v11i5.3048
Refbacks
- There are currently no refbacks.
Copyright (c) 2026 Khoiriyah, Jenny Ika Misela, Mahendra Puji
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
