Time to Revisit Existing Student’s Performance Evaluation Approach in Higher Education Sector in a New Era of ChatGPT — A Case Study
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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.
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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).
“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
The Introduction describes universities’ concern about AI-assisted student work and a resulting discussion of whether existing performance evaluations remain legitimate. Section 1.4 specifically raises the question of essays, but the passages do not establish an immediate community-wide reaction or address thesis writing specifically. (LLM, conf=0.82)
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- 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
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Safety Research › 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 ›
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