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Abstract

The rapid integration of artificial intelligence into higher education has reshaped how postgraduate students approach academic reading and learning. This study explores postgraduate English Education students’ perceptions of ChatGPT as a substitute for traditional reading, focusing on how the tool influences their cognitive engagement and reading behaviour. Anchored in the Technology Acceptance Model (TAM), Cognitive Offloading Theory, and Reading Literacy Theory, the research employs a quantitative survey design involving 108 master’s students from the Graduate Program of Universitas Negeri Makassar. A structured questionnaire measured four constructs: Perceived Usefulness (PU), Perceived Ease of Use (PEOU), Cognitive Offloading (COG), and Reading Habits Impact (RHI). Data were analysed using descriptive statistics, correlation, and reliability testing. The findings reveal that students perceive ChatGPT as highly useful (M = 4.23) and easy to use (M = 4.35). These positive perceptions correlate strongly with cognitive offloading (r = 0.61), indicating frequent reliance on ChatGPT to simplify learning tasks. However, a significant negative correlation between cognitive offloading and reading habits (r = –0.54) suggests that increased dependence on ChatGPT reduces students’ motivation for traditional reading. Overall, the study highlights a dual outcome, while ChatGPT enhances learning efficiency and accessibility, it simultaneously contributes to the decline of deep reading practices. The results underscore the need for balanced AI integration that promotes critical reading, reflective thinking, and responsible technology use in postgraduate education.

Keywords

ChatGPT English Language Learning Reading Literacy Technology Acceptance

Article Details

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How to Cite
Tahir, M., & Jahrir, A. S. (2025). Exploring English Education Post Graduated Students’ Perfections of ChatGPT as Subtitute for Traditional Reading: A Case of Universitas Negeri Makassar. FOSTER: Journal of English Language Teaching, 6(4), 422-436. https://doi.org/10.24256/foster-jelt.v6i4.310
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References

  1. Abbas, A. F., & Al-Lawati, E. H. (2025). Revisiting artificial intelligence in start-ups: A theoretical perspective on integration, opportunities, challenges, and strategic advancement. Journal of the International Council for Small Business, 1–44. https://doi.org/10.1080/26437015.2025.2549059
  2. Ajayi, A., & Ogunleye, T. (2023). Digital resource usage and reading habits among university students in Nigeria. International Journal of Educational Technology in Higher Education, 20(4), 1–15. https://doi.org/10.5281/zenodo.10028965
  3. Albayati, H. (2024). Investigating undergraduate students' perceptions and awareness of using ChatGPT as a regular assistance tool: A user acceptance perspective study. Computers and Education: Artificial Intelligence, 6, 100203. https://doi.org/10.1016/j.caeai.2024.100203
  4. Aljanabi, A. R. A., Al-Maroof, R. A., & Salloum, S. A. (2024). Students’ acceptance of ChatGPT in higher education: Extending the technology acceptance model. Education and Information Technologies, 29(5), 5331–5348.
  5. Al-Seghayer, K. (2024). Comparative efficacy of digital and nondigital texts on reading comprehension and EFL learners’ perceptions of their merits. https://doi.org/10.64152/10125/73589
  6. Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS Quarterly, 13(3), 319–340. https://doi.org/10.2307/249008
  7. Gerlich, R. (2025). Structured prompting and reflective engagement in AI-assisted learning: Reducing cognitive offloading among postgraduate students. Frontiers in Education, 10, 1398721.
  8. Guthrie, J. T., & Wigfield, A. (2000). Engagement and Motivation in Reading. In M. L. Kamil, P. B. Mosenthal, P. D. Pearson, & R. Barr (Eds.), Handbook of Reading Research (3rd Ed.). New York, NY: Longman.
  9. Hossain, K. A. (2025). Emotional Intelligence and Artificial Intelligence: Relation and Exploring the World of Human Touch and Machine.
  10. Khan, A., Alam, S., & Chen, C. (2024). Exploring learners’ experiences and perceptions of ChatGPT as a learning tool in higher education. Computers and Education: Artificial Intelligence, 6, 100219. https://doi.org/10.1016/j.caeai.2024.100219
  11. Kulal, A. (2025). Cognitive Risks of AI: Literacy, Trust, and Critical Thinking. Journal of Computer Information Systems, 1–13. https://doi.org/10.1080/08874417.2025.2582050
  12. Mirza, K. B., & Jabeen, M. (2025). University library leaders' technological initiatives for reshaping reading habits in the digital era: A twin cities case study. The Journal of Academic Librarianship, 51(3), 103038. https://doi.org/10.1016/j.acalib.2025.103038
  13. Moradi, H. (2025). Integrating AI in higher education: factors influencing ChatGPT acceptance among Chinese university EFL students. International Journal of Educational Technology in Higher Education, 22(1), 30.
  14. Papadopoulos, P. M., Demetriadis, S. N., Stamelos, I. G., & Tsoukalas, I. A. (2009). Prompting students’ context-generating cognitive activity in ill-structured domains: Does the prompting mode affect learning? Educational Technology Research and Development, 57(2), 193–210. https://doi.org/10.1007/s11423-008-9105-6
  15. Pérez-Mira, B., López, D., & García, J. (2025). Study on students’ use of ChatGPT in higher education: Benefits, costs, and attitudes. Education Sciences, 15(2), 217. https://doi.org/10.3390/educsci15020217
  16. Ravšelj, D., Keržič, D., Tomaževič, N., Umek, L., Brezovar, N., Iahad, N. A., ... & Aristovnik, A. (2025). Higher education students’ perceptions of ChatGPT: A global study of early reactions. PLoS One, 20(2), e0315011. https://doi.org/10.1371/journal.pone.0315011
  17. Risko, E. F., & Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
  18. Schorr, E. (2023). A meta-analysis of TAM studies in educational technology adoption (2010–2022). British Journal of Educational Technology, 54(5), 1452–1471. https://doi.org/10.1111/bjet.13227
  19. She, M., Xu, Y., Luo, Y., & Hu, P. (2025). Understanding university students’ acceptance and use of fragmented academic reading: An extension of the UTAUT2 model. SAGE Open, 15(3), 21582440251377881. https://doi.org/10.1177/21582440251377881
  20. Sohn, H., Acosta, K., Brownell, M. T., Gage, N. A., Tompson, E., & Pudvah, C. (2023). A meta–analysis of interventions to improve reading comprehension outcomes for adolescents with reading difficulties. Learning Disabilities Research & Practice, 38(2), 85–103. https://doi.org/10.1111/ldrp.1230
  21. Zhou, T. (2012). Examining location-based services usage from the perspectives of unified theory of acceptance and use of technology and privacy risk. Journal of Electronic Commerce Research, 13(2), 135.