Use of ChatGPT and Generative AI in Higher Education: Opportunities, Obstacles and Impact on Student Performance
Open accessAbstract
A generative form of Artificial Intelligence now pervades all domains. Artificial intelligence technologies, especially ChatGPT, are increasingly prevalent in a multiplicity of fields, especially in education. This study investigated the impact of generative AI usage on student outcomes, analyzed usage patterns of ChatGPT and highlighted the challenges and advantages of integrating ChatGPT in higher education. Employing the descriptive survey method, the study's sample comprised graduate and post graduate students from Govt. Sadiq College Women University Bahawalpur, totaling (354) participants. Data collected through self-structured, closed-ended questionnaire was analyzed using SPSS software. Descriptive statistics along with correlation analyses and one-way ANOVA were used to analyze the data. The findings indicated ChatGPT is used by 51% students regularly and perceived as an effective way to rapidly complete academic tasks, provide personalized tutoring and provide 24/7 accessibility and alternative strategies, by most participant but they believe that over reliance on ChatGPT, limits student’s exploration capabilities, critical thinking and academic honesty.
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.
Integrity signals
Registry
Cites
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.
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?
Students' utilization of ChatGPT correlated with enhancements in intellectual ability, critical thinking, vocabulary, grammar and language competency, content generation.
“Zafar et al. (2024) discovered that students' utilization of ChatGPT correlated with enhancements in intellectual ability, critical thinking, vocabulary, grammar and language competency, content generation, suggesting that generative AI can augment academic talents rather than solely supplant student labor.”
From <b>Beyond Access: Socioeconomic, Academic and Disciplinary Determinants of Generative AI Use in Higher Education</b> (2026) · cites this paper as Zafar et al. (2024)
Data Analysis and Interpretation (p. 9) reports that ChatGPT users perceived improvements in critical thinking, content-idea generation, vocabulary, grammar, intellectual capacity, and language skills; the Findings (p. 10) summarize the same results. However, these are self-reported perceptions, not measured enhancements. The paper’s reported correlations concern broader variables, not the relationship between usage and each skill named in the claim. (LLM, conf=0.84)
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
No references could be resolved yet.
Research works printed but not linked: 20
- Bandura, A. (1977). Social learning theory. Englewood Cliffs . https://www.academia.edu/download/90990222/Kelompok\_4\_Materi\_Gestalt\_Bandu ra.pdf Belkhir, A. (2023, November). Improving ChatGPT's emotional intelligence through prompt engineering [Mémoireaccepté]. Université du Québec à Montréal. Bentley, S. V., Naughtin, C. K., McGrath, M. J., Irons, J. L., & Cooper, P. S. (2024). The digital divide in action: How experiences of digital technology shape future relationships with artificial intelligence. AI and Ethics . Božić, V., &Poola, I. (2023). Chat GPT and education. Preprint . https://www.researchgate.net/profile/Velibor-Bozic2/publication/369926506\_Chat\_GPT\_and\_education/links/64350844ad9b6d17dc4d3 a79/Chat-GPT-and-education.pdf Carter, M. J., & Fuller, C. (2015). Symbolic interactionism. Sociopedia. Isa , 1 (1), 1 -17. Cetinic, E., & She, J. (2022). Understanding and Creating Art with AI: Review and Outlook. ACM Trans. Multimedia Comput. Commun. Appl. , 18 (2), 66:1-66:22. Chaves, A. P., Egbert, J., Hocking, T., Doerry, E., &Gerosa, M. A. (2022). Chatbots Language Design: The Influence of Language Variation on User Experience with Tourist Assistant Chatbots. ACM Transactions on Computer-Human Interaction , 29 (2), 1 -38. Chen, Y., Jensen, S., Albert, L. J., Gupta, S., & Lee, T. (2023). Artificial intelligence (AI) student assistants in the classroom: Designing chatbots to support student success. Information Systems Frontiers , 25 (1), 161 -182. Cooper, G. (2023). Examining Science Education in ChatGPT: An Exploratory Study of Generative Artificial Intelligence. Journal of Science Education and Technology , 32 (3), 444 -452.
- Cotton, D. R. E., Cotton, P. A., & Shipway, J. R. (2024). Chatting and cheating: Ensuring academic integrity in the era of ChatGPT. Innovations in Education and Teaching International , 61 (2), 228 -239.
- Dafoe, A. (2015). On Technological Determinism: A Typology, Scope Conditions, and a Mechanism. Science, Technology, & Human Values , 40 (6), 1047 -1076.
- Hung, J., & Chen, J. (2023). The benefits, risks and regulation of using ChatGPT in Chinese academia: A content analysis. Social Sciences , 12 (7), 380.
- Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023a). ChatGPT and a New Academic Reality: AI-Written Research Papers and the Ethics of the Large Language Models in Scholarly Publishing. Journal of the Association for Information Science and Technology , 74 (5), Article 5.
- Lund, B. D., Wang, T., Mannuru, N. R., Nie, B., Shimray, S., & Wang, Z. (2023b). CHATGPT and a new academic reality: Artificial Intelligence -Written research papers and the ethics of the large language models in scholarly publishing. Journal of the Association for Information Science and Technology , 74 (5), 570 -581.
- Lungren, M. P., Fishman, E. K., Chu, L. C., Rizk, R. C., & Rowe, S. P. (2024). More is different: Large language models in health care. Journal of the American College of Radiology , 21 (7), 1151 -1154.
- Mahapatra, S. (2024). Impact of ChatGPT on ESL students' academic writing skills: A mixed methods intervention study. Smart Learning Environments , 11 (1), 9.
- Rashid, M. M., Atilgan, N., Dobres, J., Day, S., Penkova, V., Küçük, M., Clapp, S. R., & Sawyer, B. D. (2024). Humanizing AI in Education: A Readability Comparison of LLM and Human-Created Educational Content. Proceedings of the Human Factors and Ergonomics Society Annual Meeting , 10711813241261689.
- Rasul, T., Nair, S., Kalendra, D., Robin, M., de Oliveira Santini, F., Ladeira, W. J., Sun, M., Day, I., Rather, R. A., & Heathcote, L. (2023). The role of ChatGPT in higher education: Benefits, challenges, and future research directions. Journal of Applied Learning and Teaching , 6 (1), 41 -56.
- Rawas, S. (2024). AI: The future of humanity. Discover Artificial Intelligence , 4 (1), 25.
- Sallam, M. (2023). ChatGPT utility in healthcare education, research, and practice: Systematic review on the promising perspectives and valid concerns. Healthcare , 11 (6), 887. https://www.mdpi.com/2227-9032/11/6/887
- Skavronskaya, L., Hadinejad, A. (Hana), &Cotterell, D. (2023). Reversing the threat of artificial intelligence to opportunity: A discussion of ChatGPT in tourism education. Journal of Teaching in Travel & Tourism , 23 (2), 253 -258.
- Sok, S., &Heng, K. (2024). Opportunities, challenges, and strategies for using ChatGPT in higher education: A literature review. Journal of Digital Educational Technology , 4 (1), Article 1.
- Teng, Y., Zhang, J., & Sun, T. (2023). Data -driven decision -making model based on artificial intelligence in higher education system of colleges and universities. Expert Systems , 40 (4), e12820.
- Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., &Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments , 10 (1), 15.
- Venkateswaran, P. S., Ayasrah, F. T. M., Nomula, V. K., Paramasivan, P., Anand, P., &Bogeshwaran, K. (2024). Applications of artificial intelligence tools in higher education. In Data-Driven Decision Making for Long-Term Business Success (pp. 124 -136). IGI Global.
- Wang, Z., Yang, Q., Shi, L., Yu, J., Liang, Q., Li, F., & Xiang, S. (2024). AVESFormer: Efficient Transformer Design for Real-Time Audio-Visual Segmentation (No. arXiv:2408.01708). arXiv. · arXiv
- Yalazi-Dawani, S. (2023). Integrating Artificial Intelligence into Creativity Education: Developing a Creative Problem-Solving Course for Higher Education [PhD Thesis, Doctoral dissertation, State University of New York].
- Zhai, C., &Wibowo, S. (2023). A systematic review on artificial intelligence dialogue systems for enhancing English as foreign language students' interactional competence in the university. Computers and Education: Artificial Intelligence , 4 , 100134.
Cited by
Sorted by citations
Zafar, S., Shaheen, F., & Khan, S. A. (2026) · Journal of Political Stability Archive · doi:10.63468/jpsa.4.2.17
Self-citation
- Authors
- Zafar, S., Shaheen, F., & Rehan, J.
- Affiliations
- Government Sadiq College Women University (PK)
- Venue
- iRASD Journal of Educational Research
- Published
- 2024-10-28
- Volume
- 5
- Issue
- 1
- Pages
- 01-12
- ISSN
- 2791-0636 (ISSN-L)
- 2791-0644
- OpenAlex
- W4403835038
- Cited by
- 32
- References
- 0
- Citations by year
- 2026: 17 · 2025: 14 · 2024: 1
- FWCI
- 8.45
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- iRASD journal of educational research. (publishedVersion, cc-by-nc)
- Topics
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › AI in Service Interactions
- Keywords
- ChatGPT, generative AI, higher education, student performance, personalized tutoring, critical thinking, academic integrity, overreliance on AI
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
