Digital Technologies Research and Applications

Article

Operationalizing SAMR Redefinition in EFL Reading: AI as a Mediating Tool for Literacy Innovation

Downloads

https://doi.org/10.54963/dtra.v5i1.1900
Duc, D. H. (2026). Operationalizing SAMR Redefinition in EFL Reading: AI as a Mediating Tool for Literacy Innovation. Digital Technologies Research and Applications, 5(1), 66–82. https://doi.org/10.54963/dtra.v5i1.1900
Volume 5 Issue 1: March 2026
Copyright (c) 2026 Doan Hoang Duc
Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.

Copyright

The authors shall retain the copyright of their work but allow the Publisher to publish, copy, distribute, and convey the work.

License

Digital Technologies Research and Applications (DTRA)  publishes accepted manuscripts under Creative Commons Attribution 4.0 International (CC BY 4.0). Authors who submit their papers for publication by DTRA agree to have the CC BY 4.0 license applied to their work, and that anyone is allowed to reuse the article or part of it free of charge for any purpose, including commercial use. As long as the author and original source are properly cited, anyone may copy, redistribute, reuse, and transform the content.

Authors

  • Doan Hoang Duc

    English Department, FPT University, Hanoi City 100000, Vietnam

Received: 18 November 2025; Revised: 10 December 2025; Accepted: 23 January 2026; Published: 5 February 2026

This study examines how Artificial Intelligence (AI) can leverage the concept of Redefinition—the highest level of the Substitution-Augmentation-Modification-Redefinition (SAMR) model—to transform the teaching of English as a Foreign Language (EFL) reading from comprehension-focused activities to inquiry-driven and effective reading and writing activities. Based on socio-cultural theory, Project-Based Learning (PBL), and theoretical frameworks of digital competence, this study conceptualizes AI as a cognitive mediating framework that supports knowledge transformation rather than task automation. Using a multi-method action research design, interventions were conducted with 130 Vietnamese university students enrolled in intermediate-level English reading courses over eight weeks. Traditional reading activities were redesigned into AI-assisted projects, including multimedia infographics, podcasts, and investigative reading tasks. Quantitative data were collected through a validated 40-item questionnaire, and qualitative insights were gathered from semistructured interviews with six purposefully selected participants. The study results indicate that AI-assisted redefined reading tasks promoted higher-order thinking, synthesis, and practical application, demonstrating the successful implementation of the SAMR Redefinition methodology. Students perceive AI as a supportive learning partner, enhancing comprehension, reducing frustration, and increasing motivation and confidence. A fairly strong positive correlation (r = 0.62) was found between purposeful AI use and student engagement and reading performance. Qualitative results further suggest that AI supports continuous reading, collaborative preparation, and multimodal knowledge building, while encouraging responsible and critical use of AI. The study proposes an AI-powered SAMR framework and a pedagogical sequence of read-interpret-transform-create, providing a scalable model for teaching English as a transformative foreign language reading.

Keywords:

SAMR Redefinition AI-Supported Reading EFL Pedagogy Digital Literacy Project-Based Learning (PBL) Learner Engagement

References

  1. Hamilton, E.R.; Rosenberg, J.M.; Akcaoglu, M. The Substitution Augmentation Modification Redefinition (SAMR) Model: A Critical Review and Suggestions for Its Use. TechTrends 2016, 60, 433–441. DOI: https://doi.org/10.1007/s11528-016-0091-y
  2. Blundell, C.N.; Mukherjee, M.; Nykvist, S. A scoping review of the application of the SAMR model in research. Comput. Educ. Open 2022, 3, 100093. DOI: https://doi.org/10.1016/j.caeo.2022.100093
  3. Vygotsky, L.S. Mind in Society: Development of Higher Psychological Processes; Harvard University Press: Cambridge, MA, USA, 1980. DOI: https://doi.org/10.2307/j.ctvjf9vz4
  4. Romrell, D.; Kidder, L.C.; Wood, E. The SAMR Model as a Framework for Evaluating mLearning. Online Learn. 2014, 18, DOI: https://doi.org/10.24059/olj.v18i2.435
  5. Kessler, G. Technology and the future of language teaching. Foreign Lang. Ann. 2018, 51, 205–218. DOI: https://doi.org/10.1111/flan.12318
  6. Shadiev, R.; Yang, M. Review of Studies on Technology-Enhanced Language Learning and Teaching. Sustainability 2020, 12, 524. DOI: https://doi.org/10.3390/su12020524
  7. Ratten, V.; Jones, P. Generative artificial intelligence (ChatGPT): Implications for management educators. Int. J. Manag. Educ. 2023, 21, 100857. DOI: https://doi.org/10.1016/j.ijme.2023.100857
  8. Thomas, J.W. A Review of Research on Project-Based Learning; Autodesk Foundation: San Rafael, CA, USA, 2000.
  9. Warschauer, M. A literacy approach to the digital divide. Educ Technol. 2007, 7, 4–10.
  10. Lai, C.; Morrison, B. Towards an Agenda for Learner Preparation in Technology-Enhanced Language Learning Environments. CALICO J. 2013, 30, 154–162. DOI: https://doi.org/10.11139/cj.30.2.154-162
  11. Lo, C.K.; Hew, K.F.; Jong, M.S. The influence of ChatGPT on student engagement: A systematic review and future research agenda. Comput. Educ. 2024, 219, 105100. DOI: https://doi.org/10.1016/j.compedu.2024.105100
  12. Xia, Q.; Zhang, P.; Huang, W.; et al. The impact of generative AI on university students’ learning outcomes via Bloom’s taxonomy: a meta-analysis and pattern mining approach. Asia Pac. J. Educ. 2025, 1–31. DOI: https://doi.org/10.1080/02188791.2025.2530503
  13. Dewey, J. Experience and Education; Macmillan: New York, NY, USA, 1938.
  14. Kolb, D.A. Experiential Learning: Experience as the Source of Learning and Development; Prentice-Hall: Englewood Cliffs, NJ, USA, 1984.
  15. Cope, B.; Kalantzis, M. “Multiliteracies”: New Literacies, New Learning. Pedagog.: Int. J. 2009, 4, 164–195. DOI: https://doi.org/10.1080/15544800903076044
  16. Grabe, W.P.; Stoller, F.L. Teaching and Researching: Reading; Routledge: London, UK, 2013. DOI: https://doi.org/10.4324/9781315833743
  17. Wallace, C. Critical Reading in Language Education; Palgrave Macmillan: London, UK, 2003. DOI: https://doi.org/10.1057/9780230514447
  18. Coiro, J.; Knobel, M.; Lankshear, C.; et al. Handbook of Research on New Literacies; Routledge: London, UK, 2014. DOI: https://doi.org/10.4324/9781410618894
  19. Janks, H. Literacy and Power; Routledge: New York, NY, USA, 2010.
  20. Freire, P. Pedagogy of the Oppressed; Continuum: New York, NY, USA, New York, 2000.
  21. Anderson, L.W.; Krathwohl, D. A Taxonomy For Learning, Teaching, and Assessing: A Revision of Bloom’s Taxonomy; Longman: New York, NY, USA, 2001.
  22. Reinking, D.; Bradley, B.A. On Formative and Design Experiments: Approaches to Language and Literacy Research; Teachers College Press: New York, NY, USA, 2008.
  23. Robin, B.R. The power of digital storytelling to support teaching and learning. Digit Educ Rev. 2016, 30, 17–29.
  24. Mayer, R.E. Multimedia Learning, 2nd ed; Cambridge University Press: New York, NY, USA, 2009.
  25. Lea, M.R.; Street, B.V. Student writing in higher education: An academic literacies approach. Stud. High. Educ. 1998, 23, 157–172. DOI: https://doi.org/10.1080/03075079812331380364
  26. Bandura, A. Social Cognitive Theory: An Agentic Perspective. Annu. Rev. Psychol. 2001, 52, 1–26. DOI: https://doi.org/10.1146/annurev.psych.52.1.1
  27. Hubbard, P. CALL and the Future of Language Teacher Education. CALICO J. 2008, 25, 175–188. DOI: https://doi.org/10.1558/cj.v25i2.175-188
  28. Jonassen, D. Designing Constructivist Learning Environments. In Instructional-design Theories and Models; Routledge: London, UK, 2013; pp. 215–239. DOI: https://doi.org/10.4324/9781410603784-12
  29. Kasneci, E.; Sessler, K.; Küchemann, S.; et al. ChatGPT for good? On opportunities and challenges of large language models for education. Learn. Individ. Differ. 2023, 103, 102274. DOI: https://doi.org/10.1016/j.lindif.2023.102274
  30. Dwivedi, Y.K.; Kshetri, N.; Hughes, L.; et al. “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. Int. J. Inf. Manage. 2023, 71, 102642. DOI: https://doi.org/10.1016/j.ijinfomgt.2023.102642
  31. Su, Y.; Lin, Y.; Lai, C. Collaborating with ChatGPT in argumentative writing classrooms. Assess. Writ. 2023, 57, 100752. DOI: https://doi.org/10.1016/j.asw.2023.100752
  32. Rahman, Md.M.; Watanobe, Y. ChatGPT for Education and Research: Opportunities, Threats, and Strategies. Appl. Sci. 2023, 13, 5783. DOI: https://doi.org/10.3390/app13095783
  33. Yousefi, M.; Askari, M.I. Exploring the Effectiveness of Artificial Intelligence (AI) on Reading Comprehension among Iranian EFL Learners. J. New Trends Engl. Lang. Learn. 2024. DOI: https://doi.org/10.71528/2024.202406221123351
  34. Zhang, L.; Xu, M.; Huang, Y.; et al. Reading differently with ChatGPT: EFL learners’ use of Question-Answer Relationship strategy. Read. Foreign Lang. 2025, 37, 1–21. DOI: https://doi.org/10.64152/10125/67520
  35. Teng, M.F. “ChatGPT is the companion, not enemies”: EFL learners’ perceptions and experiences in using ChatGPT for feedback in writing. Comput. Educ.: Artif. Intell. 2024, 7, 100270. DOI: https://doi.org/10.1016/j.caeai.2024.100270
  36. Zhao, Y.; Yue, Y.; Sun, Z.; et al. Does Generative Artificial Intelligence Improve Students’ Higher-Order Thinking? A Meta-Analysis Based on 29 Experiments and Quasi-Experiments. J. Intell. 2025, 13, 160. DOI: https://doi.org/10.3390/jintelligence13120160
  37. Wang, J.; Fan, W. The effect of ChatGPT on students’ learning performance, learning perception, and higher-order thinking: insights from a meta-analysis. Humanit Soc Sci Commun 2025, 12, 621. DOI: https://doi.org/10.1057/s41599-025-04787-y
  38. Carr, W.; Kemmis, S. Becoming Critical: Education Knowledge and Action Research; Routledge: London, UK, 2003, DOI: https://doi.org/10.4324/9780203496626
  39. Creswell, J.; Plano Clark, V. Designing and Conducting Mixed Methods Research, 3rd ed.; Sage: Thousand Oaks, CA, USA, 2018.
  40. Patton, M.Q. Qualitative Research and Evaluation Methods, 4th ed.; Sage: Thousand Oaks, CA, USA, 2015.
  41. Nunnally J.C. Psychometric Theory, 2nd ed.; McGraw-Hill: New York, NY, USA, 1978.
  42. Yin, R.K. Qualitative Research from Start to Finish, 2nd ed.; Guilford: New York, NY, USA, 2014.
  43. Puentedura, R. SAMR: A Contextualized Introduction; Hippasus: Williamstown, MA, USA, 2014.
  44. Kokotsaki, D.; Menzies, V.; Wiggins, A. Project-based learning: A review of the literature. Improv. Sch. 2016, 19, 267–277. DOI: https://doi.org/10.1177/1365480216659733
  45. Braun, V.; Clarke, V. Using thematic analysis in psychology. Qual. Res. Psychol. 2006, 3, 77–101. DOI: https://doi.org/10.1191/1478088706qp063oa
  46. Denzin, N.K. The Research Act: A Theoretical Introduction to Sociological Methods, 3rd ed.; Routledge: London, UK, 2012.
  47. British Educational Research Association. Ethical Guidelines for Educational Research, 4th ed.; BERA: London, UK, 2018.
  48. Li, L.; Zhang, X.; Zou, B.; et al. AI partner or peer partner? Exploring AI-mediated interaction in EFL pronunciation from a socio-cultural perspective. Learn. Cult. Soc. Interact. 2025, 55, 100958. DOI: https://doi.org/10.1016/j.lcsi.2025.100958
  49. Laupichler, M.C.; Aster, A.; Schirch, J.; et al. Artificial intelligence literacy in higher and adult education: A scoping literature review. Comput. Educ.: Artif. Intell. 2022, 3, 100101. DOI: https://doi.org/10.1016/j.caeai.2022.100101
  50. Ci, F.; Jiang, L. The integration of generative artificial intelligence into digital multimodal composing in second language classrooms: a scoping review from the perspective of tasks. Digit. Appl. Linguist. 2025, 2, 102939. DOI: https://doi.org/10.29140/dal.v2.102939
  51. Chandel, P.; Lim, F.V. Generative AI and Literacy Development in the Language Classroom: A Systematic Review of Literature. Ubiquitous Learn.: Int. J. 2024, 18, 31–49. DOI: https://doi.org/10.18848/1835-9795/CGP/v18i02/31-49
  52. Xie, W.; Zou, D.; Wu, W.; et al. Exploring the relationship between growth mindset, self-efficacy, anxiety, and perceived learning performance in AI-enhanced language learning. Artif Intell Lang Educ. 2025, 1.
  53. Lee, S.; Choe, H.; Zou, D.; et al. Generative AI (GenAI) in the language classroom: A systematic review. Interact. Learn. Environ. 2026, 34, 335–359. DOI: https://doi.org/10.1080/10494820.2025.2498537
  54. Ng, D.T.K.; Leung, J.K.L.; Chu, S.K.W.; et al. Conceptualizing AI literacy: An exploratory review. Comput. Educ.: Artif. Intell. 2021, 2, 100041. DOI: https://doi.org/10.1016/j.caeai.2021.100041
  55. Li, B.; Tan, Y.L.; Wang, C.; et al. Two years of innovation: A systematic review of empirical generative AI research in language learning and teaching. Comput. Educ.: Artif. Intell. 2025, 9, 100445. DOI: https://doi.org/10.1016/j.caeai.2025.100445
  56. Sanz-Tejeda, A.; Domínguez-Oller, J.C.; Baldaquí-Escandell, J.M.; et al. The impact of generative AI on academic reading and writing: a synthesis of recent evidence (2023–2025). Front. Educ. 2026, 10, 1711718. DOI: https://doi.org/10.3389/feduc.2025.1711718
  57. Kyaw, E.M.; Deng, J. Systematic review of artificial intelligence integration in English language teaching: Trends, applications, and pedagogical implications. Engl Educ Linguist Lit J. 2025, 4, 153–181. Available online: https://journal.unupurwokerto.ac.id/index.php/educalitra/article/view/502