INTEGRATING GENAI INTO EVIDENCE-BASED READING PRACTICES FOR DYSLEXIA: INSIGHTS FROM A CLASSROOM CASE STUDY IN GREEK EFL EDUCATION
DOI:
https://doi.org/10.46827/ejoe.v11i1.6591Keywords:
Dyslexia, EFL, Greek, reading, generative artificial intelligenceAbstract
Learning to read in a foreign language can present additional challenges for learners with dyslexia, particularly when the orthographic structure of the second language differs significantly from that of the learner’s first language. Greek is considered a relatively transparent orthography, whereas English represents a deep orthography characterized by inconsistent grapheme-phoneme correspondences. This cross-linguistic difference may create additional barriers for Greek learners with dyslexia when learning English as a foreign language. The present study examines the reading difficulties of a Greek learner with dyslexia and explores the potential impact of a structured instructional intervention supported by generative artificial intelligence (GenAI) tools. The study employed a qualitative classroom-based case study design with a pre-post intervention component. The participant was a thirteen-year-old Greek learner of English diagnosed with dyslexia. Data were collected through oral reading assessments, miscue analysis, and systematic classroom observations. The intervention integrated multisensory phonological instruction, repeated reading activities, and GenAI-assisted development of adapted reading materials. Findings indicate improvements in decoding accuracy, reading fluency, and self-monitoring behaviour following the intervention. The learner produced fewer pronunciation errors, increased reading stability, and greater independence when encountering unfamiliar words. The results suggest that structured literacy approaches combined with GenAI-assisted material design may support the development of reading skills for dyslexic learners in English as a foreign language contexts. Although the findings are limited to a single case, the study shows the potential of combining evidence-based reading instruction with emerging educational technologies to support inclusive language education.References
Birsh, J. R., & Carreker, S. (Eds.). (2018). Multisensory teaching of basic language skills (4th ed.). Paul H. Brookes Publishing. Retrieved from https://brookespublishing.com/wp-content/uploads/2021/01/Birsh_4e_excerpt.pdf
Carioti, D., Masia, M. F., Travellini, S., & Berlingeri, M. (2021). Orthographic depth and developmental dyslexia: A meta-analytic study. Annals of Dyslexia, 71(3), 399–438. https://doi.org/10.1007/s11881-021-00226-0
CAST. (2018). Universal Design for Learning guidelines version 2.2. http://udlguidelines.cast.org
Creswell, J. W., & Creswell, J. D. (2018). Research design: Qualitative, quantitative, and mixed methods approaches (5th ed.). SAGE. Retrieved from uk.sagepub.com/en-gb/eur/research-design/book270550
Damianidou, E. (2025). Artificial intelligence and disability: Using ChatGPT to improve the well-being and understand the needs of students with autism spectrum disorder. In Technology for inclusion and participation for all: Recent achievements and future directions (pp. 18–24). Springer. https://doi.org/10.1007/978-3-032-01628-7_3
Daniels, P. T., & Share, D. L. (2018). Writing system variation and its consequences for reading and dyslexia. Scientific Studies of Reading, 22(2), 101–116. https://doi.org/10.1080/10888438.2017.1379082
Giaouri, S., & Charisi, M. (2025). Enhancing IEP design in inclusive primary settings through ChatGPT: A mixed-methods study with special educators. Education Sciences, 15(8), 1065. https://doi.org/10.3390/educsci15081065
Goodman, Y. M., Watson, D. J., & Burke, C. L. (2005). Reading miscue inventory: From evaluation to instruction (2nd ed.). Richard C. Owen Publishers. Retrieved from https://books.google.es/books/about/Reading_Miscue_Inventory_from_Evaluation.html?id=uX90AAAACAAJ&redir_esc=y
Hall, C., Dahl-Leonard, K., Cho, E., Solari, E. J., Capin, P., Conner, C. L., Henry, A. R., Cook, L., Hayes, L., Vargas, I., Richmond, C. L., & Kehoe, K. F. (2023). Forty years of reading intervention research for elementary students with or at risk for dyslexia: A systematic review and meta-analysis. Reading Research Quarterly, 58(2), 285–312. https://doi.org/10.1002/rrq.477
Hatzidaki, A., Gianneli, M., Petrakis, E., Makaronas, N., & Aslanides, I. M. (2011). Reading and visual processing in Greek dyslexic children: An eye-movement study. Dyslexia, 17(1), 85–104. https://doi.org/10.1002/dys.416
He, Y., Chen, E., & Fletcher, R. (2025). Exploring generative AI in learning: Neurodivergent and disabled students’ voices in higher education. In Artificial Intelligence in Education (pp. 478–485). Springer. https://doi.org/10.1007/978-3-031-98462-4_60
Holmes, W., & Tuomi, I. (2022). State of the art and practice in AI in education. European Journal of Education, 57(4), 542–570. https://doi.org/10.1111/ejed.12533
Landerl, K., Wimmer, H., & Frith, U. (1997). The impact of orthographic consistency on dyslexia: A German-English comparison. Cognition, 63(3), 315–334. https://doi.org/10.1016/S0010-0277(97)00005-X
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson. Retrieved from https://static.googleusercontent.com/media/edu.google.com/en//pdfs/Intelligence-Unleashed-Publication.pdf
Mavrommati, T. D., & Miles, T. R. (2002). A pictographic method for teaching spelling to Greek dyslexic children. Dyslexia, 8(2), 86–101. https://doi.org/10.1002/dys.216
Merriam, S. B., & Tisdell, E. J. (2016). Qualitative research: A guide to design and implementation (4th ed.). Jossey-Bass. Retrieved from https://books.google.es/books/about/Qualitative_Research.html?id=JFN_BwAAQBAJ&redir_esc=y
Mishra, P., & Koehler, M. J. (2006). Technological pedagogical content knowledge: A framework for teacher knowledge. Teachers College Record, 108(6), 1017–1054. Retrieved from https://rediie.cl/wp-content/uploads/Mishra-Koehler.pdf
Moll, K., Ramus, F., Bartling, J., Bruder, J., Kunze, S., Neuhoff, N., Streiftau, S., Lyytinen, H., Leppänen, P. H. T., Lohvansuu, K., Tóth, D., Honbolygó, F., Csépe, V., Bogliotti, C., Iannuzzi, S., Démonet, J.-F., Longeras, E., Valdois, S., George, F., Soares-Boucaud, I., le Heuzey, M.-F., Billard, C., O’Donovan, M., Hill, G., Williams, J., Brandeis, D., Maurer, U., Schulz, E., van der Mark, S., Müller-Myhsok, B., Schulte-Körne, G., & Landerl, K. (2014). Cognitive mechanisms underlying reading and spelling development in five European orthographies. Learning and Instruction, 29, 65–77. https://doi.org/10.1016/j.learninstruc.2013.09.003
Nikolopoulos, D., Goulandris, N., & Snowling, M. J. (2003). Developmental dyslexia in Greek. In N. Goulandris (Ed.), Dyslexia in different languages: Cross-linguistic comparisons (pp. 53–67). Whurr.
Protopapas, A., & Vlahou, E. L. (2009). A comparative quantitative analysis of Greek orthographic transparency. Behavior Research Methods, 41(4), 991–1008. https://doi.org/10.3758/BRM.41.4.991
Rasinski, T. V. (2010). The fluent reader: Oral reading strategies for building word recognition, fluency, and comprehension (2nd ed.). Scholastic. Retrieved from https://books.google.es/books/about/The_Fluent_Reader.html?id=6LTSIaZYA4YC&redir_esc=y
Rizos, I., Foykas, E., & Georgakopoulos, S. V. (2024). Enhancing mathematics education for students with special educational needs through generative AI: A case study in Greece. Contemporary Educational Technology, 16(4), ep535. https://doi.org/10.30935/cedtech/15487
Ronksley-Pavia, M., Nguyen, L., Wheeley, E., Rose, J., Neumann, M. M., Bigum, C., & Neumann, D. L. (2025). A scoping literature review of generative artificial intelligence for supporting neurodivergent school students. Computers & Education: Artificial Intelligence, 9, 100437. https://doi.org/10.1016/j.caeai.2025.100437
Seiradakis, E. V. (2024). ChatGPT as a learning tool for migrant students with disabilities in upper secondary vocational education: An intersectional study. In New media pedagogy: Research trends, methodological challenges, and successful implementations (pp. 205–219). Springer. https://doi.org/10.1007/978-3-031-95627-0_14
Seiradakis, E. V. (2025). ChatGPT use: Drivers and barriers in English for academic purposes among migrant students with learning disabilities. In Reimagining intelligent computer-assisted language education (pp. 309–330). IGI Global Scientific Publishing. https://doi.org/10.4018/979-8-3693-4310-4.ch011
Snowling, M. J., & Hulme, C. (2021). Annual Research Review: Reading disorders revisited–the critical importance of oral language. Journal of Child Psychology and Psychiatry, 62(5), 635-653. https://doi.org/10.1111/jcpp.13324
Shaywitz, S. (2020). Overcoming dyslexia (2nd ed.). Knopf. Retrieved from https://books.google.es/books/about/Overcoming_Dyslexia_2020_Edition.html?id=jHU02hSTCJsC&redir_esc=y
Toki, E. I. (2024). Using eye-tracking to assess dyslexia: A systematic review of emerging evidence. Education Sciences, 14(11), 1256. https://doi.org/10.3390/educsci14111256
Wallbank, A. J. (2025). Supporting learning differences for assessments: Universal Design for Learning, GenAI, and assessment as learning. In Academic writing, assessment, and neurodiversity: Pedagogies for inclusion. Routledge. Retrieved from https://www.routledge.com/Academic-Writing-Assessment-and-Neurodiversity-Pedagogies-for-Inclusion/Wallbank/p/book/9781032505497
Yin, R. K. (2018). Case study research and applications: Design and methods (6th ed.). SAGE. Retrieved from https://books.google.es/books/about/Case_Study_Research_and_Applications.html?id=6DwmDwAAQBAJ&redir_esc=y
Ziegler, J. C., & Goswami, U. (2005). Reading acquisition, developmental dyslexia, and skilled reading across languages: A psycholinguistic grain size theory. Psychological Bulletin, 131(1), 3–29. https://doi.org/10.1037/0033-2909.131.1.3
Downloads
Published
Issue
Section
License
Authors who publish with this journal agree to the following terms:
- Authors retain copyright and grant the journal right of first publication with the work simultaneously licensed under a Creative Commons Attribution License that allows others to share the work with an acknowledgement of the work's authorship and initial publication in this journal.
- Authors are able to enter into separate, additional contractual arrangements for the non-exclusive distribution of the journal's published version of the work (e.g., post it to an institutional repository or publish it in a book), with an acknowledgement of its initial publication in this journal.
- Authors are permitted and encouraged to post their work online (e.g., in institutional repositories or on their website) prior to and during the submission process, as it can lead to productive exchanges, as well as earlier and greater citation of published work (See The Effect of Open Access).
