The DOI matches its Crossref record; 36 of the 36 references it cites were found in a registry or an index.
Exploring Students’ Perceptions of ChatGPT: Thematic Analysis and Follow-Up Survey
Open accessAbstract
ChatGPT has sparked both excitement and skepticism in education. To analyze its impact on teaching and learning it is crucial to understand how students perceive ChatGPT and assess its potential and challenges. Toward this, we conducted a two-stage study with senior students in a computer engineering program ( $n=56$ ). In the first stage, we asked the students to evaluate ChatGPT using their own words after they used it to complete one learning activity. The returned responses (3136 words) were analyzed by coding and theme building (36 codes and 15 themes). In the second stage, we used the derived codes and themes to create a 27-item questionnaire. The students responded to this questionnaire three weeks later after completing other activities with the help of ChatGPT. The results show that the students admire the capabilities of ChatGPT and find it interesting, motivating, and helpful for study and work. They find it easy to use and appreciate its human-like interface that provides well-structured responses and good explanations. However, many students feel that ChatGPT’s answers are not always accurate and most of them believe that it requires good background knowledge to work with since it does not replace human intelligence. So, most students think that ChatGPT needs to be improved but are optimistic that this will happen soon. When it comes to the negative impact of ChatGPT on learning, academic integrity, jobs, and life, the students are divided. We conclude that ChatGPT can and should be used for learning. However, students should be aware of its limitations. Educators should try using ChatGPT and guide students on effective prompting techniques and how to assess generated responses. The developers should improve their models to enhance the accuracy of given answers. The study provides insights into the capabilities and limitations of ChatGPT in education and informs future research and development.
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“Similarly, Shoufan [27,28] underscores both the benefits and risks of using ChatGPT in educational contexts, particularly its ability to provide accurate answers that could be exploited in exams.”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [28]
In the citing paper
“While Shoufan (2023) found that only 6% of their participants viewed AI systems as a major threat to learning or academic integrity, our findings differed significantly.”
From A Survey on Student Use of Generative AI Chatbots for Academic Research (2024) · cites this paper as Shoufan (2023)
In the citing paper
“This supports Shoufan's (2023) finding that students recognize the possibility of inaccuracies in generative AI outputs and believe a review of the output is needed.”
From A Survey on Student Use of Generative AI Chatbots for Academic Research (2024) · cites this paper as Shoufan (2023)
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“The perceived usefulness and ease of use are determinants for the behavioral intention to use technology according to the technology acceptance model (TAM) [34].”
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“More research is needed to understand these and other factors using modern models such as the unified theory of acceptance and use of technology (UTAUT) [37].”
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“…fairness, excessive reliance on ChatGPT by students and teachers, lack of expertise in integrating this technology in teaching, the difficulty of distinguishing model-generated from student-generated answers, cost of training and maintenance, data privacy and security, and sustainable usage [23].”
BackgroundInfluentialLiu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023) · ACM Computing Surveys · doi:10.1145/3560815 · Cited by 4,361 · cited 1×
“Prompt engineering is an emerging topic in large language models that provides an understanding of how prompting affects the performance of these models [32], [33].”
BackgroundLiu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2021) · arXiv · doi:10.48550/arxiv.2107.13586 · Cited by 4,352 · cited 1×
“Prompt engineering is an emerging topic in large language models that provides an understanding of how prompting affects the performance of these models [32], [33].”
BackgroundKung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., Madriaga, M., Aggabao, R., Diaz-Candido, G., Maningo, J., et al. (2023) · PLOS Digital Health · doi:10.1371/journal.pdig.0000198 · Cited by 4,115 · cited 2×
“The authors suggested that ChatGPT could be used in medical education and potentially support clinical decision-making [27].”
BackgroundMethodGilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023) · JMIR Medical Education · doi:10.2196/45312 · Cited by 2,078
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“Therefore, its inconsistent factual precision has been acknowledged as a notable drawback according to [24].”
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Wang, S., Zhong, Y., & Li, W. (2026) · Discover Artificial Intelligence · doi:10.1007/s44163-026-01985-8
“The green cluster is represented by Chan and Hu [19], Shoufan [68], and Grassini [35], focusing mainly on applications, student perceptions, and practical effects of generative AI in higher education.”
Omarbekova, A., Miłosz, M., Bekmanova, G., Rakymkan, Y., Nazyrova, A., & Lamasheva, Z. (2026) · Education Sciences · doi:10.3390/educsci16091382
Adam, A. S., Hwang, G., & Mubarok, H. (2026) · Educational Technology Research and Development · doi:10.1007/s11423-026-10709-9 · Cited by 1
Üztemur, S., Avcı, G., Gökalp, A., & Değirmenci, N. (2026) · SAGE Open · doi:10.1177/21582440261486100
Liu, C., Wei, L., & Tseng, J. C. R. (2026) · Education and Information Technologies · doi:10.1007/s10639-026-14107-9
“Even when feedback is provided, students often struggle to follow up or explore the issue from multiple perspectives (Shoufan, 2023).”
Zhao, Q., & Li, S. (2026) · Behavioral Sciences · doi:10.3390/bs16091626 · Cited by 1
“Current literature suggests this phenomenon stems from students’ predominant conceptualization of GenAI as either (1) a tool (Min et al., 2025) or (2) a secondary information source (Shoufan, 2023), rather than as a teacher or colleague.”
InfluentialBeyond Coding: How Students Use Generative AI Across the Full Software Development Lifecycle
Matti, S., Szendrei, R., & Pintér, B. (2026) · Lecture notes in networks and systems · doi:10.1007/978-3-032-32732-1_14
Thota, Y. R., Rafatirad, S., Homayoun, H., & Nikoubin, T. (2026) · SN Computer Science · doi:10.1007/s42979-026-05337-2
ChatGPT for interdisciplinary learning: Does it equally benefit STEM and non-STEM students
Zhu, G., Fan, X., Hou, C., Zhong, T., Zhu, W., SEOW, P. S. K., Chen, S. A., Rajalingam, P., Low, K. Y., & Tan, L. P. (2026) · Computers and Education Artificial Intelligence · doi:10.1016/j.caeai.2026.100681
Hadjichristofi, C., Gerokonstantis, D., Papadeas, P. V., & Vescoukis, V. (2026) · Annals of Computer Science and Information Systems · doi:10.15439/2026f9512
“Several studies report generally positive student attitudes toward tools such as ChatGPT, while also noting concerns about answer accuracy and quality, over-reliance, ethics, and possible effects on learning [10], [11], [12].”
The Determinants of Generative Artificial Intelligence Tools in Education: A Systematic Review
Alanazi, A. F., & Moaiad, Y. A. (2026) · Technology and Innovation Conference (TIC) · doi:10.1109/tic68483.2026.11702852
“Moreover, many empirically grounded studies have identified adoption determinants, where perceived ease of use and usefulness enhance the intention to adopt, and perceived risk, privacy concerns, and lack of trust lower it [9].”
Shan, X., Ye, X., Tu, Y., He, Z., Liu, Z., Zheng, S., Zeng, T., & Zhu, H. (2026) · Sustainability · doi:10.3390/su181910031
Chandra, Y. G., Rivandi, A. I., Marciano, K., Priskilla, M., & Hutasoit, C. S. (2026) · International Conference on ICT for Smart Society (ICISS) · doi:10.1109/iciss71466.2026.11715197
Perceptions of AI-assisted learning and learner development among Moroccan secondary school students
Kandirou, R., Youssfi, S. E., Atlassi, M. E., Dahman, A., & Hammoumi, M. M. E. (2026) · Discover Education · doi:10.1007/s44217-026-02109-1
“Students have generally reported positive perceptions of ChatGPT, particularly regarding its potential to facilitate written corrective feedback and support independent learning [43].”
Lu, G., Zhang, H., Ba, S., & Yang, L. (2026) · Journal of Educational Computing Research · doi:10.1177/07356331261494805
ChatGPT as cognitive scaffold: computational thinking and metacognition in AI-embedded instruction
Chichekian, T., & Trudeau, J. (2026) · Interactive Learning Environments · doi:10.1080/10494820.2026.2744894
Ribeiro, F. G. C., Rodrigues, L., da Costa, N. T., Xavier, C., Lobo, J., Cavalcanti, A. P., Gašević, D., Drachsler, H., & Mello, R. F. (2026) · Anais do Workshop de Informática na Escola · doi:10.5753/wie.2026.28044
“As detailed below, this analysis was based on the five-phase process proposed by [Braun and Clarke 2006, Braun and Clarke 2012, Shoufan 2023]: 1.”
Philosophical foundations of AI-mediated inquiry: filling the gap in automated knowledge production
Pirzada, Z. A., Shah, S. M. A., & Mahar, S. A. (2026) · Journal of Information Communication and Ethics in Society · doi:10.1108/jices-07-2025-0166
Gündoğdu, B., & Merç, A. (2026) · Assessing Writing · doi:10.1016/j.asw.2026.101135
Metadata
- Authors
- Shoufan, A.
- Affiliations
- Khalifa University of Science and Technology (AE)
- Corresponding author
- Shoufan, Abdulhadi
- ORCID
- Shoufan, Abdulhadi
- Venue
- IEEE Access
- Published
- 2023
- Volume
- 11
- Pages
- 38805-38818
- ISSN
- 2169-3536 (ISSN-L)
- OpenAlex
- W4366377796
- Semantic Scholar
- 587b50d31e99f37489e1a2b338d8ddb07026da0b
- Cited by
- 499
- References
- 34
- Citations by year
- 2026: 91 · 2025: 138 · 2024: 211 · 2023: 59
- FWCI
- 117.58
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- IEEE Access (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- Mohammed Bin Rashid Smart Learning Program, Ministry of Education, United Arab Emirates
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Keywords
- ChatGPT, student perceptions, large language models in education, computer engineering students, academic integrity, prompting techniques, response accuracy
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