The DOI matches its Crossref record; 38 of the 38 references it cites were found in a registry or an index.
Time to Revisit Existing Student’s Performance Evaluation Approach in Higher Education Sector in a New Era of ChatGPT — A Case Study
Open accessAbstract
response based on provided input-it has become crucial for HEIs to be exposed to the implications of AI-based tools on students' learning outcomes, commonly measured using an assessment-based approach to improve program quality, teaching effectiveness, and other learning support.An empirical study has been conducted to test the ChatGPT capability of solving a variety of assignments (from different level courses of undergraduate degree programs) to compare its performance with the highest scored student(s).Further, the ChatGPT-generated assignments were tested using the best-known tools for plagiarism detection to determine whether they could pass the academic integrity tests, including Turnitin, GPTZero, and Copyleaks.The study reported the limitations of the Bot and highlighted the implications of the newly launched AI-based ChatGPT in academia, which calls for HEIs' managers and regulators to revisit their existing practices used to monitor students' learning progress and improve their educational programs.
Overview
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
Verification
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?
The source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“This has invoked an immediate reaction from the higher education community as the question arose as to how it may affect various forms of student performance evaluation (such as essay and thesis writing) (Chaudhry et al. 2023; Yu, 2023; Farazouli et al. 2024).”
From AI and ethics: Investigating the first policy responses of higher education institutions to the challenge of generative AI (2024) · cites this paper as Chaudhry et al. 2023
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.
Citations
Cites
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, L., & Polosukhin, I. (2017) · arXiv · doi:10.48550/arxiv.1706.03762 · Cited by 120,404
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
Advances in neural information processing systems 7
(1996) · Computers & Mathematics with Applications · doi:10.1016/s0898-1221(96)90463-0 · Cited by 14,759
I.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290 · cited 1×
“As Pavlik (2023) reported, ChatGPT arguably has the potential to pass the Turing test (meaning humans through interactive conversations with machines cannot tell if he/she is interacting with a machine or a person, as proposed by Turing (1950).”
BackgroundClassrooms: Goals, structures, and student motivation.
Ames, C. (1992) · Journal of Educational Psychology · doi:10.1037/0022-0663.84.3.261 · Cited by 6,468
Kluger, A. N., & DeNisi, A. (1996) · Psychological Bulletin · doi:10.1037/0033-2909.119.2.254 · Cited by 5,882
Douglas, O. B., & Holland, B. F. (2015) · MacMillan Co eBooks · doi:10.1037/14910-020 · Cited by 2,283
What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023) · Smart Learning Environments · doi:10.1186/s40561-023-00237-x · Cited by 2,003 · cited 2×
“…consequences of students’ relying too heavily on chatbots for learning purposes so that timely action can be taken by the universities in preparing effective guidelines and policies to facilitate the appropriate and ethical use of this new technology in education sector (Tlili et al., 2023).”
BackgroundChatting about ChatGPT: how may AI and GPT impact academia and libraries?
Lund, B. D., & Wang, T. (2023) · Library Hi Tech News · doi:10.1108/lhtn-01-2023-0009 · Cited by 1,355
The ABET “Professional Skills” - Can They Be Taught? Can They Be Assessed?
Shuman, L. J., Besterfield‐Sacre, M., & McGourty, J. (2005) · Journal of Engineering Education · doi:10.1002/j.2168-9830.2005.tb00828.x · Cited by 1,167
Research works printed but not linked: 17
- Aasen, P. (2012). Accountability under ambiguity. Dilemmas and contradictions in education. In A.-L. Østern, K. Smith, T. Krüger, M.-B. Postholm, & T. Ryghaug (Eds.), Teacher education research between national identity and global trends (pp. 77–30). Academics.
- Altbach, P. G. (2004). The question of corruption in academe. International Higher Education, 34(March), 8–10.
- 10.2139/ssrn.4337484 · DOI · Paper page
- CAIE-Cambridge Assessment International Education. (2021). Education brief: School evaluation. Retrieved from https://www.cambridgeinternational.org/Images/637044-education-brief-school-evaluation.pdf
- 10.2139/ssrn.4405254 · DOI
- CERI. (2008). Assessment for Learning Formative Assessment. Paper presented in OECD/CERI International Conference titled Learning in the 21st Century: Research, Innovation and Policy. Retrieved from https://www.oecd.org/site/educeri21st/40600533.pdf
- Chen Y. Chen Y. & Heffernan N. (2020). Personalized math tutoring with a conversational agent. arXiv preprint arXiv:2012.12121. · arXiv
- Deepa, S., & Seth, M. (2013). Do soft skills matter? – Implications for educators based on recruiters’ perspectives. IUP Journal of Soft Skills, 7(1), 7–20.
- Gilson, A., Safranek, C., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2022). How well does ChatGPT do when taking the medical licensing exams? The implications of large language models for medical education and knowledge assessment. medRxiv, 9(Feb), e45312.
- Kim, S., Park, J., & Lee, H. (2019). Automated essay scoring using a deep learning model. Journal of Educational Technology Development and Exchange, 2(1), 1–17.
- Ludeman, R. (2005). Student leadership and moral accountability. Journal of College and Character, 2, Retrieved from. http://www.collegevalues.org/ethics.cfmin=460&d=1
- Owings, L. (2002). Cheaters never win but they keep professors on their toes. Silicon Valley/San Jose Business Journal, 8(August), 19.
- 10.1108/S2044-9968 · DOI
- 10.21203/rs.3.rs-2610655/v1 · DOI
- Telecom Review. (2023). UAE: Sheikh Mohammed Requests Study on ChatGPT. 10 February 2023. Retrieved from https://www.telecomreview.com/articles/reports-and-coverage/6773-uae-sheikh-mohammed-requests-study-on-chatgpt
- Van Dis E. Bollen J. Zuidema W. Rooij R. & Bockting C. (2023). ChatGPT: Five priorities for research Nature 3 February 2023. Retrieved from https://www.nature.com/articles/d41586-023-00288-7
- Willmot, P., & Colman, B. (2016). Interpersonal skills in engineering education, AAEE2016 CONFERENCE, Coffs Harbour, Australia.
Cited by
Sorted by citations
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023) · Sustainability · doi:10.3390/su151712983 · Cited by 784
Does ChatGPT enhance student learning? A systematic review and meta-analysis of experimental studies
Deng, R., Jiang, M., Yu, X., Lu, Y., & Liu, S. (2025) · Computers & Education · doi:10.1016/j.compedu.2024.105224 · Cited by 531
A Scoping Review on How Generative Artificial Intelligence Transforms Assessment in Higher Education
Xia, Q., Weng, X., Ouyang, F., Lin, T. J., & Chiu, T. K. F. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00468-z · Cited by 493
Abbas, M., Jam, F. A., & Khan, T. I. (2024) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-024-00444-7 · Cited by 531
“However, the extensive use of ChatGPT brings a number of challenges for higher education (Bahroun et al., 2023; Chan, 2023; Chaudhry et al., 2023; Dalalah & Dalalah, 2023).”
BackgroundInfluentialExploring Artificial Intelligence in Academic Essay: Higher Education Student's Perspective
Malik, A. R., Pratiwi, Y., Andajani, K., Numertayasa, I. W., Suharti, S., Darwis, A., & Marzuki (2023) · International Journal of Educational Research Open · doi:10.1016/j.ijedro.2023.100296 · Cited by 418
Metadata
- Authors
- Chaudhry, I. S., Sarwary, S. A. M., El Refae, G. A., & Chabchoub, H.
- Affiliations
- Al Ain University (AE)
- Portland State University (US)
- Al Ain University
- Corresponding author
- Chaudhry, Iffat Sabir
- Venue
- Cogent Education
- Publisher
- Informa UK Limited
- Published
- 2023-05-14
- Volume
- 10
- Issue
- 1
- ISSN
- 2331-186X (ISSN-L)
- OpenAlex
- W4376564338
- Cited by
- 262
- References
- 37
- Citations by year
- 2026: 63 · 2025: 85 · 2024: 89 · 2023: 25
- FWCI
- 61.50
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Cogent Education (publishedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- funding
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Social Sciences › Education › Academic integrity and plagiarism
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Keywords
- ChatGPT, student assessment, higher education, plagiarism detection, academic integrity, Turnitin, GPTZero, undergraduate students, artificial intelligence in academia, learning outcomes
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
