Keywords
academic reading
learner experience
How to Cite
Abstract
Generative artificial intelligence (GenAI)- powered tools have been widely used to enhance language learning skills. However, little is known about their role in shaping learners’ experiences during academic reading. To address this gap, our study engaged twelve postgraduate students in evaluating reading reports generated by Fullpicture, an academic reading tool powered by ChatGPT, over seven weeks. Students reflected on their experiences using the GenAI tool for academic reading and rated its effectiveness on a 6-point Likert scale. Based on the three-level model of reading comprehension, learners’ reported benefits of the tool include the literal level, which encompasses the facilitation of quick comprehension and the reduction of cognitive load; the inferential level, which includes the interpretation of the author’s tone and purpose and the reinforcement of personal beliefs; and the critical level, which involves the promotion of reflective reading practices and the generation of new perspectives for academic writing. The perceived challenges can also be categorized into three levels: selective summarization and misrepresentation of details at the literal level; overgeneralization of interpretations and irrelevance to personal reading and writing goals at the inferential level; and a lack of practical suggestions and weak evidence to support critiques at the critical level. The analysis of questionnaire results also revealed the supporting role of GenAI-powered tools for L2 learners in evaluating arguments, evidence quality, or the generalizability of research; however, their limited effectiveness was also identified, especially concerning the adequacy of discussion and theoretical frameworks. The study suggested that learners should collaborate with AI instead of fully relying on it to support academic reading.
References
Alptekin, C. (2006). Cultural familiarity in inferential and literal comprehension in L2 reading. System, 34(4), 494-508. https://doi.org/10.1016/j.system.2006.05.003
Alptekin, C. E. M., & ErÇEtin, G. (2011). Effects of Working Memory Capacity and Content Familiarity on Literal and Inferential Comprehension in L2 Reading. TESOL Quarterly, 45(2), 235-266. https://doi.org/10.5054/tq.2011.247705
Alqahtani, T., Badreldin, H. A., Alrashed, M., Alshaya, A. I., Alghamdi, S. S., bin Saleh, K., Alowais, S. A., Alshaya, O. A., Rahman, I., Al Yami, M. S., & Albekairy, A. M. (2023). The emergent role of artificial intelligence, natural learning processing, and large language models in higher education and research. Research in Social and Administrative Pharmacy, 19(8), 1236-1242. https://doi.org/10.1016/j.sapharm.2023.05.016
Basaraba, D., Yovanoff, P., Alonzo, J., & Tindal, G. (2013). Examining the structure of reading comprehension: do literal, inferential, and evaluative comprehension truly exist? Reading and Writing, 26(3), 349-379. https://doi.org/10.1007/s11145-012-9372-9
Carnine, D. W., Silbert, J., Kame'enui, E. J., Slocum, T. A., & Travers, P. A. (2015). Direct instruction reading (6th ed.). Pearson.
Cheng, X., Yin, L., Lin, C., Shi, Z., Zheng, H., Zhu, L., Liu, X., Chen, K., & Dong, R. (2024). Chatbot dialogic reading boosts comprehension for Chinese kindergarteners with higher language skills. Journal of Experimental Child Psychology, 240, 105842. https://doi.org/10.1016/j.jecp.2023.105842
Cheung, K. K. C., Pun, J. K. H., & Li, W. (2024). Students’ Holistic Reading of Socio-Scientific Texts on Climate Change in a ChatGPT Scenario. Research in Science Education. https://doi.org/10.1007/s11165-024-10177-2
Conrad, S. M. (1996). Investigating academic texts with corpus-based techniques: An example from biology. Linguistics and Education, 8(3), 299-326. https://doi.org/10.1016/S0898-5898(96)90025-X
Creswell, J. W., & Clark, V. L. P. (2017). Designing and conducting mixed methods research. Sage publications.
Darwin, Rusdin, D., Mukminatien, N., Suryati, N., Laksmi, E. D., & Marzuki. (2024). Critical thinking in the AI era: An exploration of EFL students’ perceptions, benefits, and limitations. Cogent Education, 11(1), 2290342. https://doi.org/10.1080/2331186X.2023.2290342
de-la-Peña, C., & Luque-Rojas, M. J. (2021). Levels of Reading Comprehension in Higher Education: Systematic Review and Meta-Analysis. Frontiers in Psychology, 12. https://doi.org/10.3389/fpsyg.2021.712901
Elbro, C., & and Buch-Iversen, I. (2013). Activation of Background Knowledge for Inference Making: Effects on Reading Comprehension. Scientific Studies of Reading, 17(6), 435-452. https://doi.org/10.1080/10888438.2013.774005
Fazio, X., Gallagher, T. L., & DeKlerk, C. (2022). Exploring Adolescents’ Critical Reading of Socioscientific Topics Using Multimodal Texts. International Journal of Science and Mathematics Education, 20(1), 93-116. https://doi.org/10.1007/s10763-022-10280-8
Feng, L. (2024). 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), 1961-1994. https://doi.org/10.1177/07356331241268349
Goldman, S. R. (2024). Reading is complex: Implications for research and practice. Educational Psychologist, 59(4), 315-325. https://doi.org/10.1080/00461520.2024.2418062
Green, D. A. (2009). New academics’ perceptions of the language of teaching and learning: identifying and overcoming linguistic barriers. International Journal for Academic Development, 14(1), 33-45. https://doi.org/10.1080/13601440802659254
Herber, H. L. (1978). Teaching Reading in Content Areas. Prentice-Hall. https://books.google.com/books?id=RWFBezXMpVIC
Hudson, R. F., Pullen, P. C., Lane, H. B., & Torgesen, J. K. (2008). The Complex Nature of Reading Fluency: A Multidimensional View. Reading & Writing Quarterly, 25(1), 4-32. https://doi.org/10.1080/10573560802491208
Jeon, E. H., & Yamashita, J. (2014). L2 Reading Comprehension and Its Correlates: A Meta-Analysis. Language Learning, 64(1), 160-212. https://doi.org/10.1111/lang.12034
Jeon, J. (2024). Exploring AI chatbot affordances in the EFL classroom: young learners’ experiences and perspectives. Computer Assisted Language Learning, 37(1-2), 1-26. https://doi.org/10.1080/09588221.2021.2021241
Johnson, T. E., Archibald, T. N., & Tenenbaum, G. (2010). Individual and team annotation effects on students’ reading comprehension, critical thinking, and meta-cognitive skills. Computers in Human Behavior, 26(6), 1496-1507. https://doi.org/10.1016/j.chb.2010.05.014
Kiili, C., Leu, D. J., Marttunen, M., Hautala, J., & Leppänen, P. H. T. (2018). Exploring early adolescents’ evaluation of academic and commercial online resources related to health. Reading and Writing, 31(3), 533-557. https://doi.org/10.1007/s11145-017-9797-2
Kim, J., Yu, S., Detrick, R., & Li, N. (2024). Exploring students’ perspectives on Generative AI-assisted academic writing. Education and Information Technologies. https://doi.org/10.1007/s10639-024-12878-7
Kohnke, L. (2024). Exploring EAP students' perceptions of GenAI and traditional grammar-checking tools for language learning. Computers and Education: Artificial Intelligence, 7, 100279. https://doi.org/10.1016/j.caeai.2024.100279
Ku, K. Y. L., & Ho, I. T. (2010). Metacognitive strategies that enhance critical thinking. Metacognition and Learning, 5(3), 251-267. https://doi.org/10.1007/s11409-010-9060-6
Lin, H., Chen, Z., Wei, W., & Lu, H. (2025). GenAI tools in academic reading: A study on AI-assisted metacognitive strategies and emotional reactions. In E. C. K. Cheng (Ed.), Innovating education with AI: AETS 2024. Lecture Notes in Educational Technology (pp. 98–112). Springer, Singapore. https://doi.org/10.1007/978-981-96-4952-5_7
Lin, H., Xiong, Z., Tang, H., Jiang, S., Wei, W., & Fang, K. (2024). Implementing generative AI agent game to support reading of classical Chinese literature: A needs analysis. In 2024 4th International Conference on Educational Technology (ICET) (pp. 86–91). IEEE. https://doi.org/10.1109/ICET62460.2024.10868296
Liu, G. L., Darvin, R., & Ma, C. (2024). Exploring AI-mediated informal digital learning of English (AI-IDLE): a mixed-method investigation of Chinese EFL learners’ AI adoption and experiences. Computer Assisted Language Learning, 1-29. https://doi.org/10.1080/09588221.2024.2310288
Liu, M., Zhang, J., Nyagoga, L. M., & Liu, L. (2024). Student-AI Question Cocreation for Enhancing Reading Comprehension. IEEE Transactions on Learning Technologies, 17, 815-826. https://doi.org/10.1109/TLT.2023.3333439
Liu, X., Gu, M. M., & Jin, T. (2021). Strategy use in collaborative academic reading: Understanding how undergraduate students co-construct comprehension of academic texts. Language Teaching Research, 28(4), 1391-1411. https://doi.org/10.1177/13621688211025688
Luk, J., & Hui, D. (2017). Examining multiple readings of popular culture by ESL students in Hong Kong. Language, Culture and Curriculum, 30(2), 212-230. https://doi.org/10.1080/07908318.2016.1241258
Monib, W. K., Qazi, A., & Mahmud, M. M. (2025). Exploring learners’ experiences and perceptions of ChatGPT as a learning tool in higher education. Education and Information Technologies, 30(1), 917-939. https://doi.org/10.1007/s10639-024-13065-4
Oates, A., & Johnson, D. (2025). ChatGPT in the Classroom: Evaluating its Role in Fostering Critical Evaluation Skills. International Journal of Artificial Intelligence in Education. https://doi.org/10.1007/s40593-024-00452-8
Peng, Z., Liu, Y., Zhou, H., Xu, Z., & Ma, X. (2022). CReBot: Exploring interactive question prompts for critical paper reading. International Journal of Human-Computer Studies, 167, 102898. https://doi.org/10.1016/j.ijhcs.2022.102898
Pretorius, E. J., & Spaull, N. (2016). Exploring relationships between oral reading fluency and reading comprehension amongst English second language readers in South Africa. Reading and Writing, 29(7), 1449-1471. https://doi.org/10.1007/s11145-016-9645-9
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. https://doi.org/10.1093/ijl/ecad030
Rouet, J.-F., Britt, M. A., & Durik, A. M. (2017). RESOLV: Readers' Representation of Reading Contexts and Tasks. Educational Psychologist, 52(3), 200-215. https://doi.org/10.1080/00461520.2017.1329015
Saadatnia, M., Ketabi, S., & Tavakoli, M. (2016). EFL Learners’ Levels of Comprehension Across Text Structures: A Comparison of Literal and Inferential Comprehension of Descriptive and Enumerative Expository Texts. Journal of Psycholinguistic Research, 45(6), 1499-1513. https://doi.org/10.1007/s10936-016-9414-6
Sheorey, R., & Mokhtari, K. (2001). Differences in the metacognitive awareness of reading strategies among native and non-native readers. System, 29(4), 431-449. https://doi.org/10.1016/S0346-251X(01)00039-2
Snow, C. (2002). Reading for understanding: Toward an R&D program in reading comprehension. Rand Corporation.
Srinivasan, V., & Murthy, H. (2021). Improving reading and comprehension in K-12: Evidence from a large-scale AI technology intervention in India. Computers and Education: Artificial Intelligence, 2, 100019. https://doi.org/10.1016/j.caeai.2021.100019
Taki, S. (2016). Metacognitive online reading strategy use: Readers' perceptions in L1 and L2. Journal of Research in Reading, 39(4), 409-427. https://doi.org/10.1111/1467-9817.12048
Toyokawa, Y., Horikoshi, I., Majumdar, R., & Ogata, H. (2023). Challenges and opportunities of AI in inclusive education: a case study of data-enhanced active reading in Japan. Smart Learning Environments, 10(1), 67. https://doi.org/10.1186/s40561-023-00286-2
Wallace, M., & Wray, A. (2021). Critical Reading and Writing for Postgraduates. SAGE. https://books.google.com/books?id=3UbYzQEACAAJ
Wang, C., & Gierl, M. J. (2011). Using the Attribute Hierarchy Method to Make Diagnostic Inferences about Examinees’ Cognitive Skills in Critical Reading. Journal of Educational Measurement, 48(2), 165-187. https://doi.org/10.1111/j.1745-3984.2011.00142.x
Wang, C. (2024). Exploring Students’ Generative AI-Assisted Writing Processes: Perceptions and Experiences from Native and Nonnative English Speakers. Technology, Knowledge and Learning. https://doi.org/10.1007/s10758-024-09744-3
Yang, A. C. M., Chen, I. Y. L., Flanagan, B., & Ogata, H. (2021). From Human Grading to Machine Grading: Automatic Diagnosis of e-Book Text Marking Skills in Precision Education. Educational Technology & Society, 24(1), 164–175. https://www.jstor.org/stable/26977865
Yüzlü, M. Y., & Dikilitaş, K. (2022). Translanguaging as a way to fostering EFL learners’ criticality in a hybrid course design. System, 110, 102926. https://doi.org/10.1016/j.system.2022.102926
Zhang, D., Wu, J. G., & Fu, Z. (2024). From shy to fly: Facilitating EFL learners’ willingness to communicate with an AI chatbot and an intelligent tutoring system. System, 127, 103501. https://doi.org/10.1016/j.system.2024.103501
Zou, B., Liviero, S., Ma, Q., Zhang, W., Du, Y., & Xing, P. (2024). Exploring EFL learners’ perceived promise and limitations of using an artificial intelligence speech evaluation system for speaking practice. System, 126, 103497. https://doi.org/10.1016/j.system.2024.10349
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