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.
Overview
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
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?
In the citing paper
“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)
Citations
Cites
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483 · cited 1×
“The perceived usefulness and ease of use are determinants for the behavioral intention to use technology according to the technology acceptance model (TAM) [34].”
MethodUser Acceptance of Information Technology: Toward A Unified View1
Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003) · MIS Quarterly · doi:10.2307/30036540 · Cited by 45,035 · cited 1×
“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].”
MethodChatGPT for Good? On Opportunities and Challenges of Large Language Models for Education
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · doi:10.35542/osf.io/5er8f · Cited by 7,552
ChatGPT for good? On opportunities and challenges of large language models for education
Kasneci, E., Sessler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., Gasser, U., Groh, G., Günnemann, S., Hüllermeier, E., et al. (2023) · Learning and Individual Differences · doi:10.1016/j.lindif.2023.102274 · Cited by 7,326 · cited 4×
“…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
ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.9 · Cited by 1,806 · cited 3×
“Therefore, its inconsistent factual precision has been acknowledged as a notable drawback according to [24].”
BackgroundInfluentialChatbots: History, technology, and applications
Adamopoulou, E., & Moussiades, L. (2020) · Machine Learning with Applications · doi:10.1016/j.mlwa.2020.100006 · Cited by 1,090
Flagged first, then by citations
Research works printed but not linked: 4
- 2023, Taguette the Free and Open-Source Qualitative Data Analysis Tool
- owens, 2023, How Nature Readers are Using ChatGPT—Nature com
- van dis, 2023, ChatGPT Five Priorities for Research—Nature com
- mcmurtrie, 2023, Teaching Experts are Worried About ChatGPT But Not for the Reasons you Think—chronicle com
Cited by
Sorted by citations
Items 501–502 of 502
Generative AI acceptance among students in LIS programs in Türkiye
Öztürk, H. (2026) · Reference Services Review · doi:10.1108/rsr-04-2026-0070
Chai, S., Zhu, G., He, Z., Zhou, F., Wu, H., & Tongwu, J. (2026) · Asia Pacific Journal of Education · doi:10.1080/02188791.2026.2751966
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
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
