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Vol. 13 No. 4 (2026)
International Multidisciplinary Journal for Research & Development

Articles

SELF-REGULATED LEARNING AND AI: A THEORETICAL PERSPECTIVE ON ACADEMIC READING

Published 2026-04-15

  • IRGASHEVA MADINA IRGASHOVNA

IRGASHEVA MADINA IRGASHOVNA
Uzbek State University of World Languages English Faculty-1, The Department of the English Language Applied Sciences Teacher

Abstract

This article examines the intersection of self-regulated learning (SRL) theory and artificial intelligence (AI) technologies in the context of academic reading. Drawing on contemporary theoretical frameworks, we explore how AI-powered tools can support and enhance self-regulated reading processes among university students. The study synthesizes existing literature on SRL, academic reading strategies, and AI applications in education to develop a comprehensive theoretical model. Our analysis reveals that AI technologies offer significant potential for supporting metacognitive monitoring, strategy selection, and self-assessment during academic reading tasks. However, the effective integration of AI tools requires careful consideration of pedagogical principles, learner autonomy, and the development of critical digital literacy skills.

Keywords

self-regulated learning, artificial intelligence, academic reading, metacognition, digital literacy

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References

  1. Holmes W., Bialik M., Fadel C. Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston: Center for Curriculum Redesign; 2019. 248 p.
  2. Shanahan T., Shanahan C. What is disciplinary literacy and why does it matter? Topics in Language Disorders. 2012;32(1):7-18.
  3. Leu D.J., Forzani E., Rhoads C., Maykel C., Kennedy C., Timbrell N. The new literacies of online research and comprehension: Rethinking the reading achievement gap. Reading Research Quarterly. 2015;50(1):37-59.
  4. Panadero E. A review of self-regulated learning: Six models and four directions for research. Frontiers in Psychology. 2017;8:422.
  5. Zimmerman B.J. Self-regulated learning and academic achievement: An overview. Educational Psychologist. 1990;25(1):3-17.
  6. Afflerbach P., Cho B.Y., Kim J.Y. Conceptualizing and assessing reading comprehension: Why performance measures are not enough. International Electronic Journal of Elementary Education. 2019;11(3):185-194.
  7. Roll I., Wylie R. Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education. 2016;26(2):582-599.
  8. Luckin R., Holmes W., Griffiths M., Forcier L.B. Intelligence Unleashed: An Argument for AI in Education. London: Pearson; 2016. 88 p.
  9. Hart C. Doing a Literature Review: Releasing the Social Science Research Imagination. London: SAGE Publications; 1998. 230 p.
  10. Moher D., Liberati A., Tetzlaff J., Altman D.G. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. Annals of Internal Medicine. 2009;151(4):264-269.
  11. Pintrich P.R. The role of metacognitive knowledge in learning, teaching, and assessing. Theory into Practice. 2002;41(4):219-225.
  12. Azevedo R., Hadwin A.F. Scaffolding self-regulated learning and metacognition: Implications for the design of computer-based scaffolds. Instructional Science. 2005;33(5-6):367-379.
  13. Winne P.H., Jamieson-Noel D. Exploring students' calibration of self-reports about study tactics and achievement. Contemporary Educational Psychology. 2002;27(4):551-572.
  14. Graesser A.C., McNamara D.S., VanLehn K. Scaffolding deep comprehension strategies through Point&Query, AutoTutor, and iSTART. Educational Psychologist. 2005;40(4):225-234.
  15. Azevedo R., Feyzi-Behnagh R., Duffy M., Harley J.M., Trevors G.J. Metacognition and self-regulated learning in student-centered learning environments. In: Jonassen D.H., Land S.M., editors. Theoretical Foundations of Student-Centered Learning Environments. New York: Routledge; 2012. p. 171-197.
  16. VanLehn K. The relative effectiveness of human tutoring, intelligent tutoring systems, and other tutoring systems. Educational Psychologist. 2011;46(4):197-221.
  17. Shute V.J., Ventura M., Kim Y.J. Assessment and learning of qualitative physics in Newton's Playground. Journal of Educational Research. 2013;106(6):423-430.
  18. Kintsch W. Comprehension: A Paradigm for Cognition. Cambridge: Cambridge University Press; 1998. 461 p.

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