Optimizing L2 English Reading Speed and Comprehension Through Artificial Intelligence: A Psycholinguistic and Pedagogical Framework

المؤلفون

  • Saaid Ali Omar English Department, Faculty of Arts, University of Zawia, Zawia, Libya Author

DOI:

https://doi.org/10.65405/sjh.2.3.87

الكلمات المفتاحية:

L2 English Reading; Reading Fluency; Artificial Intelligence; Intelligent Tutoring Systems; Adaptive Learning

الملخص

Artificial intelligence (AI) is proposed as a means of improving second-language (L2) English reading. The pedagogical problem, however, is not maximizing words per minute but helping readers allocate processing resources so that lexical access becomes more efficient without sacrificing syntactic integration, inference, monitoring, and transfer. This paper synthesizes research on L2 reading, evidence on fluency and comprehension interventions, studies of intelligent tutoring and generative AI, and scholarship on assessment and educational ethics. The synthesis supports a layered model in which reading performance emerges from interactions among lexical and orthographic quality, decoding, sentence and discourse integration, working memory, prior knowledge, strategic regulation, motivation, and text-purpose conditions. Speed and comprehension are related but dissociable outcomes; therefore, AI should optimize a conditional speed–accuracy profile rather than a reading-rate target. The paper proposes a teacher-governed AI architecture combining learner modeling, text selection, repeated and extensive reading, vocabulary recurrence, strategy coaching, uncertainty-aware feedback, and delayed transfer assessment. These mechanisms are recommendations derived from converging evidence, not claims that AI automatically produces durable L2 gains. Evaluation should include rate, accuracy, literal and inferential comprehension, vocabulary breadth and depth, immediate and delayed transfer, learner agency, fairness, and teacher workload. The paper concludes that AI is most defensible as a scaffold whose assistance fades as learners develop self-regulated reading, while teachers retain responsibility for interpretation, assessment, and equitable participation.

المراجع

Alrawashdeh, G. S., Fyffe, S., Azevedo, R. F. L., & Castillo, N. M. (2024). Exploring the impact of personalized and adaptive learning technologies on reading literacy: A global meta-analysis. Educational Research Review, 42, Article 100587. https://doi.org/10.1016/j.edurev.2023.100587

Bell, T. (2001). Extensive reading: Speed and comprehension. The Reading Matrix, 1(1).

Çelik, F., Yangın Ersanlı, C., & Arslanbay, G. (2024). Does AI simplification of authentic blog texts improve reading comprehension, inferencing, and anxiety? A one-shot intervention in Turkish EFL context. The International Review of Research in Open and Distributed Learning, 25(3), 287–303. https://doi.org/10.19173/irrodl.v25i3.7779

Chang, Y.-F. (2006). On the use of the immediate recall task as a measure of second language reading comprehension. Language Testing, 23(4), 520–543. https://doi.org/10.1191/0265532206lt340oa

Clahsen, H., & Felser, C. (2018). Some notes on the Shallow Structure Hypothesis. Studies in Second Language Acquisition, 40(3), 693–706. https://doi.org/10.1017/S0272263117000250

Cop, U., Drieghe, D., & Duyck, W. (2015). Eye movement patterns in natural reading: A comparison of monolingual and bilingual reading of a novel. PLOS ONE, 10(8), Article e0134008. https://doi.org/10.1371/journal.pone.0134008

Council of Europe. (2001). Common European framework of reference for languages: Learning, teaching, assessment. Cambridge University Press.

Cunnings, I. (2022). Working memory and L2 sentence processing. In J. W. Schwieter & Z. Wen (Eds.), The Cambridge handbook of working memory and language (pp. 593–612). Cambridge University Press. https://doi.org/10.1017/9781108955638.033

European Commission, High-Level Expert Group on Artificial Intelligence. (2019). Ethics guidelines for trustworthy AI.

Godwin-Jones, R. (2024). Distributed agency in second language learning and teaching through generative AI. Language Learning & Technology, 28(2), 5–31.

Gorsuch, G., & Taguchi, E. (2008). Repeated reading for developing reading fluency and reading comprehension: The case of EFL learners in Vietnam. System, 36(2), 253–278. https://doi.org/10.1016/j.system.2007.09.009

Grabe, W., & Yamashita, J. (2022). Reading assessment. In W. Grabe & J. Yamashita, Reading in a second language: Moving from theory to practice (pp. 460–486). Cambridge University Press. https://doi.org/10.1017/9781108878944.021

Grabe, W., & Yamashita, J. (2022). Reading fluency, reading rate, and comprehension. In W. Grabe & J. Yamashita, Reading in a second language: Moving from theory to practice (pp. 403–418). Cambridge University Press. https://doi.org/10.1017/9781108878944.018

Jeon, E. H. (2012). Oral reading fluency in second language reading. Reading in a Foreign Language, 24(2), 186–208. https://doi.org/10.64152/10125/66860

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, E.-Y., & Day, R. R. (2016). The effectiveness of extensive reading on reading proficiency: A meta-analysis. Reading in a Foreign Language, 28(2), 246–265.

Joh, J., & Plakans, L. (2017). Working memory in L2 reading comprehension: The influence of prior knowledge. System, 70, 107–120. https://doi.org/10.1016/j.system.2017.07.007

Joo, D., & Belcher, D. (2025). Human-AI collaborative reading in academic contexts: An exploratory case study. Journal of English for Academic Purposes, 78, Article 101571. https://doi.org/10.1016/j.jeap.2025.101571

Kaur, P., Kumar, H., & Kaushal, S. (2023). Technology-assisted language learning adaptive systems: A comprehensive review. International Journal of Cognitive Computing in Engineering, 4, 301–313. https://doi.org/10.1016/j.ijcce.2023.09.002

Klella, A. S. (2024). Accessibility and inclusivity in digital language education. University of Zawia Journal of Educational and Psychological Sciences, 13(2). https://journals.zu.edu.ly/index.php/UZJEPS/article/view/1063

Klella, A. S. (2026). Artificial intelligence in higher education and its transformative role in educational technologies and learning systems. University of Zawia Journal of Educational and Psychological Sciences, 15(1). https://doi.org/10.26629/uzjeps.2026.05

Klella, A. S. A., & Al Garaghoolee, I. S. D. (2026). Cognitive and functional approaches to English language futurity in EFL contexts. Al-Farooq Journal of Sciences, 2(Supplement 3), 401–416.

Klella, A. S. A., & Mrghem, Z. M. O. (2024). Artificial intelligence and human cognition: A systematic review of thought provocation through AI ChatGPT prompts. ATRAS Journal, 5(3), 432–444. https://asjp.cerist.dz/en/article/253661

Kuperman, V., Siegelman, N., Schroeder, S., Acartürk, C., Alexeeva, S., Amenta, S., Bertram, R., Bonandrini, R., Brysbaert, M., Chernova, D., Da Fonseca, S. M., Dirix, N., Duyck, W., Fella, A., Frost, R., Gattei, C. A., Kalaitzi, A., Lõo, K., Marelli, M., Nisbet, K., Papadopoulos, T. C., Protopapas, A., Savo, S., Shalom, D. E., Slioussar, N., Stein, R., Sui, L., Taboh, A., Tønnesen, V., & Usal, K. A. (2023). Text reading in English as a second language: Evidence from the Multilingual Eye-Movements Corpus. Studies in Second Language Acquisition, 45(1), 3–37. https://doi.org/10.1017/S0272263121000954

Laufer, B., & Aviad-Levitzky, T. (2017). What type of vocabulary knowledge predicts reading comprehension: Word meaning recall or word meaning recognition? The Modern Language Journal, 101(4), 729–741. https://doi.org/10.1111/modl.12431

Lee, J., Hicke, Y., Yu, R., Brooks, C., & Kizilcec, R. F. (2024). The life cycle of large language models in education: A framework for understanding sources of bias. British Journal of Educational Technology, 55(5), 1982–2002. https://doi.org/10.1111/bjet.13505

Lee, S., Choe, H., Zou, D., & Jeon, J. (2025). Generative AI (GenAI) in the language classroom: A systematic review. Interactive Learning Environments, 335–359. https://doi.org/10.1080/10494820.2025.2498537

Li, B., Lowell, V. L., Wang, C., & Li, X. (2024). A systematic review of the first year of publications on ChatGPT and language education: Examining research on ChatGPT’s use in language learning and teaching. Computers and Education: Artificial Intelligence, 7, Article 100266. https://doi.org/10.1016/j.caeai.2024.100266

Li, B., Tan, Y. L., Wang, C., & Lowell, V. (2025). Two years of innovation: A systematic review of empirical generative AI research in language learning and teaching. Computers and Education: Artificial Intelligence, 9, Article 100445. https://doi.org/10.1016/j.caeai.2025.100445

Lin, Z., & Chen, H. (2024). Investigating the capability of ChatGPT for generating multiple-choice reading comprehension items. System, 123, Article 103344. https://doi.org/10.1016/j.system.2024.103344

Ma, Q., Crosthwaite, P., Sun, D., & Zou, D. (2024). Exploring ChatGPT literacy in language education: A global perspective and comprehensive approach. Computers and Education: Artificial Intelligence, 7, Article 100278. https://doi.org/10.1016/j.caeai.2024.100278

Magliano, J. P., Millis, K. K., The R-SAT Development Team, & Levinstein, I. (2011). Assessing comprehension during reading with the Reading Strategy Assessment Tool (RSAT). Metacognition and Learning, 6(2), 131–154. https://doi.org/10.1007/s11409-010-9064-2

Mežek, Š., McGrath, L., Negretti, R., & Berggren, J. (2022). Scaffolding L2 academic reading and self-regulation through task and feedback. TESOL Quarterly, 56(1), 41–67. https://doi.org/10.1002/tesq.3018

Mézière, D. C., Yu, L., Reichle, E. D., von der Malsburg, T., & McArthur, G. (2023). Using eye-tracking measures to predict reading comprehension. Reading Research Quarterly, 58, 425–449. https://doi.org/10.1002/rrq.498

Miao, F., & Cukurova, M. (2024). AI competency framework for teachers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391104_eng

Miao, F., & Holmes, W. (2023). Guidance for generative AI in education and research. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000386693_eng

Miao, F., Shiohira, K., & Lao, N. (2024). AI competency framework for students. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000391105_eng

Mrghem, Z. M. O., & Klella, A. S. A. (2026). Cybersecurity Awareness in Higher Education: A Literature Review of Educational Practices and Digital Safety. North African Journal of Scientific Publishing (NAJSP), 4(2), 264-274. https://najsp.com/index.php/home/article/view/892

Nisbet, K., Bertram, R., Erlinghagen, C., Pieczykolan, A., & Kuperman, V. (2022). Quantifying the difference in reading fluency between L1 and L2 readers of English. Studies in Second Language Acquisition, 44(2), 407–434. https://doi.org/10.1017/S0272263117000250

Organisation for Economic Co-operation and Development. (n.d.). Digital divide in education. Retrieved September 19, 2026, from https://www.oecd.org/en/topics/sub-issues/digital-divide-in-education.html

Ozer, O. (2024). AI language models: A breach of academic integrity in online language learning? Studies in Language Assessment, 13(1), 237–260. https://www.altaanz.org/uploads/5/9/0/8/5908292/7._sila_13_1__ozer.pdf

Rayner, K., Schotter, E. R., Masson, M. E. J., Potter, M. C., & Treiman, R. (2016). So much to read, so little time: How do we read, and can speed reading help? Psychological Science in the Public Interest, 17(1), 4–34. https://doi.org/10.1177/1529100615623267

Sangers, N. L., van der Sande, L., Welie, C., Dobber, M., & van Steensel, R. (2025). Learning a language through reading: A meta-analysis of studies on the effects of extensive reading on second and foreign language learning. Educational Psychology Review, 37(4), Article 96. https://doi.org/10.1007/s10648-025-10068-6

Segalowitz, S. J., Segalowitz, N. S., & Wood, A. G. (1998). Assessing the development of automaticity in second language word recognition. Applied Psycholinguistics, 19(1), 53–67. https://doi.org/10.1017/S0142716400010572

Shafiee Rad, H. (2025). Reinforcing L2 reading comprehension through artificial intelligence intervention: Refining engagement to foster self-regulated learning. Smart Learning Environments, 12, Article 23. https://doi.org/10.1186/s40561-025-00377-2

Shang, J., Huang, Y., Xu, M., Huang, Y., Shen, X., Wang, G., Wang, Y., & Zhang, L. (2025). Competition between human learners and ChatGPT: Enhancing university EFL students’ reading comprehension and critical thinking through competitive questioning. Computer Assisted Language Learning. Advance online publication. https://doi.org/10.1080/09588221.2025.2577356

Spichtig, A. N., Gehsmann, K. M., Pascoe, J. P., & Ferrara, J. D. (2019). The impact of adaptive, web-based, scaffolded silent reading instruction on the reading achievement of students in grades 4 and 5. The Elementary School Journal, 119(3), 443–467. https://doi.org/10.1086/701705

Traxler, M. J., Long, D. L., Tooley, K. M., Johns, C. L., Zirnstein, M., & Jonathan, E. (2012). Individual differences in eye-movements during reading: Working memory and speed-of-processing effects. Journal of Eye Movement Research, 5(1), Article 5. https://pmc.ncbi.nlm.nih.gov/articles/PMC4467465/

Tywoniw, R. (2023). Compensatory effects of individual differences, language proficiency, and reading behavior: An eye-tracking study of second language reading assessment. Frontiers in Communication, 8, Article 1176986. https://doi.org/10.3389/fcomm.2023.1176986

U.S. Department of Education, Office of Educational Technology. (2023). Artificial intelligence and the future of teaching and learning: Insights and recommendations. https://www.ed.gov/sites/ed/files/documents/ai-report/ai-report-core-messages.pdf

United Nations. (2006). Convention on the Rights of Persons with Disabilities. Office of the United Nations High Commissioner for Human Rights. https://www.ohchr.org/en/instruments-mechanisms/instruments/convention-rights-persons-disabilities

Wang, X., & Feng, Y. (2024). An experimental study of ChatGPT-assisted improvement of Chinese college students’ English reading skills: A case study of Dear Life. In Proceedings of the 15th International Conference on Education Technology and Computers (pp. 21–26). Association for Computing Machinery. https://doi.org/10.1145/3629296.3629300

Wang, Y.-H. (2016). Promoting contextual vocabulary learning through an adaptive computer-assisted EFL reading system. Journal of Computer Assisted Learning, 32(4), 291–303. https://doi.org/10.1111/jcal.12132

Webb, S., Uchihara, T., & Yanagisawa, A. (2023). How effective is second language incidental vocabulary learning? A meta-analysis. Language Teaching, 56(2), 161–180. https://doi.org/10.1017/S0261444822000507

Weissburg, I., Anand, S., Levy, S., & Jeong, H. (2025). LLMs are biased teachers: Evaluating LLM bias in personalized education. In Findings of the Association for Computational Linguistics: NAACL 2025 (pp. 5665–5713). Association for Computational Linguistics. https://doi.org/10.18653/v1/2025.findings-naacl.314

Wen, Z., & Chu, S. K. W. (2025). Using generative AI for reading question creation based on PIRLS 2011 framework. Cogent Education, 12(1), Article 2458653. https://doi.org/10.1080/2331186X.2025.2458653

Whitford, V., & Titone, D. (2012). Second-language experience modulates first- and second-language word frequency effects: Evidence from eye movement measures of natural paragraph reading. Psychonomic Bulletin & Review, 19, 73–80. https://doi.org/10.3758/s13423-011-0179-5

Wijekumar, K. K., Meyer, B. J. F., & Lei, P. (2012). Large-scale randomized controlled trial with 4th graders using intelligent tutoring of the structure strategy to improve nonfiction reading comprehension. Educational Technology Research and Development, 60, 987–1013. https://doi.org/10.1007/s11423-012-9263-4

World Wide Web Consortium. (2024). Web Content Accessibility Guidelines (WCAG) 2.2. https://www.w3.org/TR/WCAG22/

Xu, Z., Wijekumar, K. K., Ramirez, G., Hu, X., & Irey, R. (2019). The effectiveness of intelligent tutoring systems on K–12 students’ reading comprehension: A meta-analysis. British Journal of Educational Technology, 50(6), 3119–3137. https://doi.org/10.1111/bjet.12758

Xuan, Q., Cheung, A., & Sun, D. (2022). The effectiveness of formative assessment for enhancing reading achievement in K–12 classrooms: A meta-analysis. Frontiers in Psychology, 13, Article 990196. https://doi.org/10.3389/fpsyg.2022.990196

Yang, Y., & Qian, D. D. (2020). Promoting L2 English learners’ reading proficiency through computerized dynamic assessment. Computer Assisted Language Learning, 33(5–6), 628–652. https://doi.org/10.1080/09588221.2019.1585882

Yang, Y.-H., Chu, H.-C., & Tseng, W.-T. (2021). Text difficulty in extensive reading: Reading comprehension and reading motivation. Reading in a Foreign Language, 33(1), 78–102. https://doi.org/10.64152/10125/67394

Yapp, D. J., de Graaff, R., & van den Bergh, H. (2021). Improving second language reading comprehension through reading strategies: A meta-analysis of L2 reading strategy interventions. Journal of Second Language Studies, 4(1), 154–192. https://doi.org/10.1075/jsls.19013.yap

Yousefi, M. H., & Biria, R. (2018). The effectiveness of L2 vocabulary instruction: A meta-analysis. Asian-Pacific Journal of Second and Foreign Language Education, 3, Article 21. https://doi.org/10.1186/s40862-018-0062-2

Yuan, H. (2025). Artificial intelligence in language learning: Biometric feedback and adaptive reading for improved comprehension and reduced anxiety. Humanities and Social Sciences Communications, 12, Article 556. https://www.nature.com/articles/s41599-025-04878-w

Zhai, C., Wibowo, S., & Li, L. D. (2024). The effects of over-reliance on AI dialogue systems on students’ cognitive abilities: A systematic review. Smart Learning Environments, 11, Article 28. https://doi.org/10.1186/s40561-024-00316-7

Zhang, S., & Zhang, X. (2022). The relationship between vocabulary knowledge and L2 reading/listening comprehension: A meta-analysis. Language Teaching Research, 26(4), 696–725. https://doi.org/10.1177/1362168820913998

Zhang, S., Shan, C., Lee, J. S. Y., Che, S., & Kim, J. H. (2023). Effect of chatbot-assisted language learning: A meta-analysis. Education and Information Technologies, 28, 15223–15243. https://doi.org/10.1007/s10639-023-11805-6

التنزيلات

منشور

2026-09-23