CoChat
Sign Up
Cited by 36Artificial Intelligence in EducationArtificial Intelligence in Healthcare and EducationAcademic integrity and plagiarism

Students’ Perceptions of ChatGPT in Higher Education: A Study of Academic Enhancement, Procrastination, and Ethical Concerns

Open access
Uppal, Komal, Hajian, Shiva·2024·European Journal of Educational Research
DOI 10.12973/eu-jer.14.1.199
Open this paper in CoChat — free accountOpen PDF

Abstract

The integration of AI tools in education is reshaping how students view and interact with their learning experiences. As AI usage continues to grow, it becomes increasingly important to understand how students' perceptions of these technologies impact their academic performance and learning behaviours. To investigate these effects, we conducted a correlational study with a sample of 44 students to examining the relationship between students' perceptions of ChatGPT’s utility—focusing on usage frequency, perceived usefulness, accuracy, reliability, and time efficiency—and key academic outcomes, including content mastery, confidence in knowledge, and grade improvement. Additionally, we explored how these perceptions influence student behaviours, such as reliance on ChatGPT, procrastination tendencies, and the potential risk of plagiarism. The canonical correlation analysis revealed a statistically significant relationship between students' perceptions of ChatGPT's utility and their academic outcomes. Students who viewed ChatGPT as reliable and efficient tended to report higher grades, improved understanding of the material, and greater confidence in their knowledge. Furthermore, the bivariate correlation analysis revealed a significant relationship between dependency on ChatGPT and procrastination (r = 0.546, p < 0.001), indicating that a higher reliance on AI tools may contribute to increased procrastination. No statistically significant association was identified between ChatGPT dependency and the risk of plagiarism. Future research should prioritize the development of strategies that promote effective use of AI while minimizing the risk of overreliance. Such efforts can enhance academic integrity and support independent learning. Educators play a critical role in this process by guiding students to balance the advantages of AI with the cultivation of critical thinking skills and adherence to ethical academic practices.

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.12973/eu-jer.14.1.199

Cites

26 of 26 references verified0 citation errors · 0 not checkable

Statistics

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Access to AI tools is a significant determinant of productivity and academic engagement.

    “Uppal and Hajian (2025) demonstrate that the access to AI tools is a significant determinant of productivity and academic engagement.”

    From Mapping the impact of generative AI in higher education: A scoping review of psychological and equity dimensions. (2026) · cites this paper as Uppal and Hajian (2025)

    The Introduction (page unavailable) says that positive perceptions of AI tools can foster greater engagement, and the Discussion (page unavailable) links perceived usefulness and increased ChatGPT use to improved academic performance. The paper does not demonstrate that access to AI tools is a significant determinant of either productivity or academic engagement; its study examines perceptions and use rather than access. (LLM, conf=0.78)

    Experimental — accuracy not yet measured

  • Low-income students are restrained in using advanced features, which restricts their academic benefits.

    “Research shows that low-income students are restrained in using advanced features, which restricts their academic benefits (Blahopoulou and Ortiz-Bonnin, 2025; Uppal and Hajian, 2025).”

    From Mapping the impact of generative AI in higher education: A scoping review of psychological and equity dimensions. (2026) · cites this paper as Uppal and Hajian, 2025

    The Literature Review mentions ChatGPT’s advanced features and potential educational benefits, but does not say that low-income students have restricted access to those features. Searches for low-income students, socioeconomic status, and financial barriers found no passage linking income to feature access or academic benefits. (LLM, conf=0.75)

    Experimental — accuracy not yet measured

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Cites

Flagged first, then by citations

Research works printed but not linked: 49

  1. Balım, A. G. (2009). The Effects of discovery learning on students' success and inquiry learning skills. Eurasian Journal of Educational Research, 35, 1-20. https://bit.ly/3ZkeQVO
  2. Bannert, M. (2002). Managing cognitive load-recent trends in cognitive load theory. Learning and Instruction, 12(1), 139- 146. https://doi.org/10.1016/S0959-4752(01)00021-4 · DOI · Paper page
  3. Beltozar-Clemente, S., Díaz-Vega, E., Zapata-Paulini, J., & Tejeda-Navarrete, R. E. (2024). We can rely on ChatGPT as an educational tutor: A cross-sectional study of its performance, accuracy, and limitations in university admission tests. International Journal of Engineering Pedagogy, 14(1), 50-60. https://doi.org/10.3991/ijep.v14i1.46787 · DOI
  4. Biswas, S. (2023). Role of Chat GPT in education. Journal of ENT Surgery Research, 1(1), 1-3. https://bit.ly/4faM9kb
  5. Chakraborty Samant, A., Tyagi, I., Vybhavi, J., Jha, H., & Patel, J. (2024). ChatGPT dependency disorder in healthcare practice: An Editorial. Cureus, 16(8), Article e66155. https://doi.org/10.7759/cureus.66155 · DOI
  6. Chellappa, V., & Luximon, Y. (2024). Understanding the perception of design students towards ChatGPT. Computers and Education. Artificial Intelligence, 7, Article 100281. https://doi.org/10.1016/j.caeai.2024.100281 · DOI
  7. Cho, H. J., Melloch, M. R., & Levesque-Bristol, C. (2021). Enhanced student perceptions of learning and performance using concept-point-recovery teaching sessions: A mixed-method approach. International Journal of STEM Education, 8, Article 32. https://doi.org/10.1186/s40594-021-00276-1 · DOI
  8. Choudhury, A., & Shamszare, H. (2023). Investigating the impact of user trust on the adoption and use of ChatGPT: Survey analysis. Journal of Medical Internet Research, 25, Article e47184. https://doi.org/10.2196/47184 · DOI · Paper page
  9. Chu, S. K.-W., & Law, N. (2007). Development of information search expertise: Postgraduates' knowledge of searching skills. Portal: Libraries and the Academy, 7(3), 295-316. https://doi.org/10.1353/pla.2007.0028 · DOI
  10. Clark, T. M. (2023). Investigating the use of an Artificial Intelligence chatbot with general chemistry exam questions. Journal of Chemical Education, 100(5), 1905-1916. https://doi.org/10.1021/acs.jchemed.3c00027 · DOI
  11. Das, S. R., & J. V., M. (2024). Perceptions of higher education students towards ChatGPT usage. International Journal of Technology in Education, 7(1), 86-106. https://doi.org/10.46328/ijte.583 · DOI
  12. Deci, E. L., & Ryan, R. M. (2012). Self-determination theory. Handbook of Theories of Social Psychology, 1(20), 416-436. https://doi.org/10.4135/9781446249215. · DOI · Paper page
  13. n21 · DOI · Paper page
  14. Degirmenci, N., Baglama, B., & Yucesoy, Y. (2020). The use of technology in dyslexia: An analysis of recent trends. International Journal of Emerging Technologies in Learning, 15(5), 30-39. https://doi.org/10.3991/ijet.v15i05.11921 · DOI
  15. Farhi, F., Jeljeli, R., Aburezeq, I., Dweikat, F. F., Al-shami, S. A., & Slamene, R. (2023). Analyzing the students' views, concerns, and perceived ethics about Chat GPT usage. Computers and Education. Artificial Intelligence, 5, Article 100180. https://doi.org/10.1016/j.caeai.2023.100180 · DOI · Paper page
  16. Firaina, R., & Sulisworo, D. (2023). Exploring the usage of ChatGPT in higher education: Frequency and impact on productivity. Buletin Edukasi Indonesia, 2(01), 39-46. https://doi.org/10.56741/bei.v2i01.310 · DOI
  17. Fullan, M., Azorín, C., Harris, A., & Jones, M. (2024). Artificial intelligence and school leadership: Challenges, opportunities and implications. School Leadership and Management, 44(4), 339-346. https://doi.org/10.1080/13632434.2023.2246856 · DOI · Paper page
  18. Glaser, N. (2023). Exploring the potential of ChatGPT as an educational technology: An emerging technology report. Technology, Knowledge and Learning, 28, 1945-1952. https://doi.org/10.1007/s10758-023-09684-4 · DOI
  19. Grájeda, A., Burgos, J., Córdova, P., & Sanjinés, A. (2024). Assessing student-perceived impact of using artificial intelligence tools: Construction of a synthetic index of application in higher education. Cogent Education, 11(1), Article 2287917. https://doi.org/10.1080/2331186X.2023.2287917 · DOI
  20. Hajian, S. (2021). What types of feedback enhance the effectiveness of self-explanation in a simulation-based learning environment? [Doctoral dissertation, Simon Fraser University]. Simon Fraser University. https://bit.ly/3V1HF8b
  21. Hajian, S., Jain, M., Liu, A. L., Obaid, T., Fukuda, M., Winne, P. H., & Nesbit, J. C. (2021). Enhancing scientific discovery learning by just-in-time prompts in a simulation-assisted inquiry environment. European Journal of Educational Research, 10(2), 989-1007. https://doi.org/10.12973/eu-jer.10.2.989 · DOI
  22. Hajian, S., Obaid, T., Jain, M., & Nesbit, J. (2019). Inquiry learning with an interactive physics simulation: What exploratory strategies lead to success? Journal of Interactive Learning Research, 30(4), 451-476. https://www.learntechlib.org/primary/p/210235/
  23. Johnson, D., Goodman, R., Patrinely, J., Stone, C., Zimmerman, E., Donald, R., Chang, S., Berkowitz, S., Finn, A., Jahangir, E., Scoville, E., Reese, T., Friedman, D., Bastarache, J., van der Heijden, Y., Wright, J., Carter, N., Alexander, M., Choe, J., … Wheless, L. (2023). Assessing the accuracy and reliability of AI-Generated medical responses: An evaluation of the Chat- GPT model. Research Square. https://doi.org/10.21203/rs.3.rs-2566942/v1 · DOI · Paper page
  24. Kalyuga, S. (2009). Managing cognitive load in adaptive ICT-based learning. Systemics, Cybernetics and Informatics, 7(5), 16-21. https://bit.ly/4fX1Zj1
  25. Karr-Wisniewski, P., & Lu, Y. (2010). When more is too much: Operationalizing technology overload and exploring its impact on knowledge worker productivity. Computers in Human Behavior, 26(5), 1061-1072. https://doi.org/10.1016/j.chb.2010.03.008 · DOI
  26. Khan, R. A., Jawaid, M., Khan, A. R., & Sajjad, M. (2023). ChatGPT -Reshaping medical education and clinical management. Pakistan Journal of Medical Sciences, 39(2), 605-607. https://doi.org/10.12669/pjms.39.2.7653 · DOI · Paper page
  27. Kirschner, P. A. (2002). Cognitive load theory: Implications of cognitive load theory on the design of learning. Learning and Instruction, 12(1), 1-10. https://doi.org/10.1016/S0959-4752(01)00014-7 · DOI · Paper page
  28. Kokku, R., Sundararajan, S., Dey, P., Sindhgatta, R., Nitta, S., & Sengupta, B. (2018). Augmenting Classrooms with AI for personalized education. 2018 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), (pp. 6976-6980). IEEE. https://doi.org/10.1109/ICASSP.2018.8461812 · DOI
  29. Lange, C. (2023). The relationship between e-learning personalisation and cognitive load. Open Learning: The Journal of Open, Distance and e-Learning, 38(3), 228-242. https://doi.org/10.1080/02680513.2021.2019577 · DOI
  30. Magalhães Araujo, S., & Cruz-Correia, R. (2024). Incorporating ChatGPT in medical informatics education: Mixed methods study on student perceptions and experiential integration proposals. JMIR Medical Education, 10, Article e51151. https://doi.org/10.2196/51151 · DOI · Paper page
  31. Ngo, T. T. A. (2023). The perception by university students of the use of ChatGPT in education. International Journal of Emerging Technologies in Learning, 18(17), 4-19. https://doi.org/10.3991/ijet.v18i17.39019 · DOI
  32. Nguyen, Q. N., & Sidorova, A. (2018). Understanding user interactions with a chatbot: A self-determination theory approach. AMCIS 2018 Proceedings, 3. https://aisel.aisnet.org/amcis2018/HCI/Presentations/3
  33. Nguyen Minh, A. (2024). Leveraging ChatGPT for enhancing English writing skills and critical thinking in university freshmen. Journal of Knowledge Learning and Science Technology, 3(2), 51-62. https://doi.org/10.60087/jklst.vol3.n2.p62 · DOI
  34. Niemiec, C. P., & Ryan, R. M. (2009). Autonomy, competence, and relatedness in the classroom. Theory and research in education, 7(2), 133-144. https://doi.org/10.1177/1477878509104318 · DOI · Paper page
  35. Opara, E. C., Adalikwu, M.-E. T. Regular users, & Tolorunleke, C. A. (2023). ChatGPT for teaching, learning and research: Prospects and Challenges. Global Academic Journal of Humanities and Social Sciences, 5(2), 33-40. https://doi.org/10.36348/gajhss.2023.v05i02.001 · DOI
  36. Rajala, J., Hukkanen, J., Hartikainen, M., & Niemelä, P. (2023). "\"Call me Kiran\"-ChatGPT as a tutoring Chatbot in a computer science course. In Proceedings of the International Academic Mindtrek Conference (pp. 83-94). Association for Computing Machinery. https://doi.org/10.1145/3616961.3616974 · DOI
  37. Romero-Rodríguez, J., Ramírez-Montoya, M., Buenestado-Fernández, M., & Lara-Lara, F. (2023). Use of ChatGPT at university as a tool for complex thinking: Students' perceived usefulness. Journal of New Approaches in Educational Research, 12(2), 323-339. https://doi.org/10.7821/naer.2023.7.1458 · DOI · Paper page
  38. Roumeliotis, K. I., & Tselikas, N. D. (2023). ChatGPT and Open-AI models: A preliminary review. Future Internet, 15(6), Article 192. https://doi.org/10.3390/fi15060192 · DOI
  39. Rutkowski, A.-F., & Saunders, C. S. (2018). Emotional and cognitive overload: The dark side of information technology. Routledge. https://doi.org/10.4324/9781315167275 · DOI
  40. Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55(1), 68-78. https://doi.org/10.1037/0003-066X.55.1.68 · DOI · Paper page
  41. Sadegh-Zadeh, S.-A., Movahhedi, T., Hajiyavand, A. M., & Dearn, K. D. (2023). Exploring undergraduates' perceptions of and engagement in an AI-enhanced online course. Frontiers in Education, 8, Article 1252543. https://doi.org/10.3389/feduc.2023.1252543 · DOI
  42. Selwyn, N. (2017). Education and technology: Key issues and debates (2nd ed.). Bloomsbury Academic. https://doi.org/10.5040/9781474235952 · DOI
  43. Siregar, F. H., Hasmayni, B., & Lubis, A. H. (2023). The analysis of ChatGPT usage impact on learning motivation among scout students. International Journal of Research and Review, 10(7), 632-638. https://doi.org/10.52403/ijrr.20230774 · DOI
  44. Sweller, J. (2011). Cognitive load theory. In J. P. Mestre, & B. H. Ross (Eds.), The psychology of learning and motivation: Cognition in education (pp. 37-76). Elsevier Academic Press. https://doi.org/10.1016/B978-0-12-387691-1.00002- 8 · DOI · Paper page
  45. Tabachnick, B. G., & Fidell, L. S. (2013). Using multivariate statistics (6th ed.). Pearson.
  46. Vaccino-Salvadore, S. (2023). Exploring the ethical dimensions of using ChatGPT in language learning and beyond. Languages, 8(3), Article 191. https://doi.org/10.3390/languages8030191 · DOI
  47. Valova, I., Mladenova, T., & Kanev, G. (2024). Students' perception of ChatGPT usage in education. International Journal of Advanced Computer Science and Applications, 15(1), 466-473. https://doi.org/10.14569/IJACSA.2024.0150143 · DOI
  48. Venkatesh, V., & Davis, F. D. (1996). A model of the antecedents of perceived ease of use: Development and test. Decision Sciences, 27(3), 451-481. https://doi.org/10.1111/j.1540-5915.1996.tb00860.x · DOI
  49. Wekerle, C., Daumiller, M., & Kollar, I. (2022). Using digital technology to promote higher education learning: The importance of different learning activities and their relations to learning outcomes. Journal of Research on Technology in Education, 54(1), 1-17. https://doi.org/10.1080/15391523.2020.1799455 · DOI

Cited by

Sorted by citations

Authors
Uppal, K., & Hajian, S.
Affiliations
Kwantlen Polytechnic University (CA)
ORCID
Venue
European Journal of Educational Research
Publisher
Eurasian Society of Educational Research
Published
2024-12-26
Volume
14
Issue
1
Pages
199-211
ISSN
2165-8714 (ISSN-L)
DOI
10.12973/eu-jer.14.1.199
OpenAlex
W4405801900
Cited by
36
References
26
Citations by year
2026: 24 · 2025: 12
FWCI
13.54
Type
journal article
Language
en
Open access
Locations
European Journal of Educational Research (publishedVersion)
Funders
Kwantlen Polytechnic University
Topics
  • Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
  • Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
  • Social Sciences › Safety Research › Academic integrity and plagiarism
Keywords
ChatGPT, student perceptions, artificial intelligence in higher education, procrastination, plagiarism risk, academic integrity, perceived usefulness of AI, AI dependency, academic performance, content mastery, knowledge confidence, AI ethics

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review