The DOI matches its Crossref record; 34 of the 34 references it cites were found in a registry or an index.
Generative AI and Higher Education Assessments: A Competency-Based Analysis
Open accessAbstract
The development and introduction of AI language models have transformed the way humans and institutions interact with technology, enabling natural and intuitive communication between humans and machines. This paper conducts a competence-based analysis of an emerging AI language model’s task response to provide insight into its language proficiency, critical analysis and reasoning ability, and structure and relevance of the response. A multidisciplinary approach is adopted, drawing from fields such as Accounting, Education, Management, Social Work and Law, to evaluate the responses generated by the AI to higher education assignments. This paper offers insights into the strengths and limitations of language-based AI responses and identifies implications for the design and implementation of higher education assessments.
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 emergence of GenAI in higher education has raised concerns about academic integrity, authenticity of student work, and potential reduction in students' development of essential cognitive and creative skills.
“The disruptive nature of GenAI as a technology and its emergence in higher education has raised a range of concerns around academic integrity, the authenticity of student work, and the potential reduction in students’ development of essential cognitive and creative skills [3, 4].”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [4]
The Implications section identifies ChatGPT as a potential threat to academic integrity and discusses cheating and disclosure of AI contributions to assignments, supporting concerns about the authenticity of student work. The theoretical framework says AI can relieve students of thinking and applying concepts, but the paper does not specifically link GenAI use to reduced development of students’ creative skills; its discussion of creativity concerns the quality of ChatGPT’s writing. (LLM, conf=0.83)
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
Exploring the impact of artificial intelligence on teaching and learning in higher education
Popenici, S. A. D., & Kerr, S. (2017) · Research and Practice in Technology Enhanced Learning · doi:10.1186/s41039-017-0062-8 · Cited by 2,019
The forthcoming artificial intelligence (AI) revolution: Its impact on society and firms
Makridakis, S. (2017) · Futures · doi:10.1016/j.futures.2017.03.006 · Cited by 1,866
Intelligent tutoring systems and learning outcomes: A meta-analysis
Ma, W., Adesope, O. O., Nesbit, J. C., & Liu, Q. (2014) · Journal of Educational Psychology · doi:10.1037/a0037123 · Cited by 840
ChatGPT for (Finance) research: The Bananarama Conjecture
Dowling, M., & Lucey, B. M. (2023) · Finance research letters · doi:10.1016/j.frl.2023.103662 · Cited by 648
Artificial Intelligence in Education (AIEd): a high-level academic and industry note 2021
Chaudhry, M. A., & Kazim, E. (2022) · AI and Ethics · doi:10.1007/s43681-021-00074-z · Cited by 425
ChatGPT: The End of Online Exam Integrity?
Susnjak, T. (2022) · arXiv · doi:10.48550/arxiv.2212.09292 · Cited by 370
García-Peñalvo, F. J. (2023) · Education in the Knowledge Society (EKS) · doi:10.14201/eks.31279 · Cited by 331
Towards artificial intelligence-based assessment systems
Luckin, R. (2017) · Nature Human Behaviour · doi:10.1038/s41562-016-0028 · Cited by 303
Plagiarism in the age of massive generative pre-trained transformers (GPT-3)
Dehouche, N. (2021) · Ethics in Science and Environmental Politics · doi:10.3354/esep00195 · Cited by 296
ChatGPT is not all you need. A state-of-the-art review of large generative AI models
Gozalo-Brizuela, R., & Garrido-Merchan, E. C. (2023) · arXiv · doi:10.48550/arxiv.2301.04655 · Cited by 245
Multiple-Choice Exams: An Obstacle for Higher-Level Thinking in Introductory Science Classes
Stanger-Hall, K. F. (2012) · CBE—Life Sciences Education · doi:10.1187/cbe.11-11-0100 · Cited by 243
A Review of Competency‐Based Learning: Tools, Assessments, and Recommendations
Henri, M., Johnson, M., & Nepal, B. P. (2017) · Journal of Engineering Education · doi:10.1002/jee.20180 · Cited by 242
Can GPT-3 pass a writer’s turing test?
Elkins, K., & Chun, J. (2020) · Journal of Cultural Analytics · doi:10.22148/001c.17212 · Cited by 202
Steenbergen-Hu, S., & Cooper, H. (2013) · Journal of Educational Psychology · doi:10.1037/a0032447 · Cited by 175
Evaluation of competence-based teaching in higher education: From theory to practice
Bergsmann, E., Schultes, M.-T., Winter, P., Schober, B., & Spiel, C. (2015) · Evaluation and Program Planning · doi:10.1016/j.evalprogplan.2015.03.001 · Cited by 152
Teacher workload: using ICT to release time to teach
Selwood, I. D., & Pilkington, R. M. (2005) · Educational Review · doi:10.1080/0013191042000308341 · Cited by 91
Approaches to Learning by Students in the Biological Sciences: Implications for teaching
Watters, D. J., & Watters, J. J. (2006) · International Journal of Science Education · doi:10.1080/09500690600621282 · Cited by 86
(2015) · Educational Technology & Society · Cited by 68
Assessing and Developing Program Outcomes through Workplace Competencies
Brumm, T. J., & Hanneman, L. F. (2006) · Iowa State University Digital Repository (Iowa State University) · Cited by 56
Modeling and Measuring Competencies in Higher Education: Tasks and Challenges
Watkins, V. (2016) · Journal of Professional Continuing and Online Education · doi:10.18741/p9rp4x · Cited by 49
Competency-based Outcomes Assessment for Agricultural Engineering Programs
Brumm, T. J., Steward, B. L., & Kaleita, A. L. (2006) · Iowa State University Digital Repository (Iowa State University) · Cited by 38
A Competency Based MSIS Curriculum
Sutcliffe, N. G., Chan, S. S., & Nakayama, M. (2005) · Journal of the Association for Information Systems · Cited by 36
Competency-Based Assessments: Leveraging Artificial Intelligence to Predict Subcompetency Content
Booth, G. J., Ross, B., Cronin, W. A., McElrath, A. D., Cyr, K. L., Hodgson, J. A., Sibley, C., Ismawan, J. M., Zuehl, A., Slotto, J. G., et al. (2022) · Academic Medicine · doi:10.1097/acm.0000000000005115 · Cited by 32
Thompson, N., & Spanuth, S. (2018) · SSRN Electronic Journal · doi:10.2139/ssrn.3287769 · Cited by 29
Biotechniques laboratory: An enabling course in the biological sciences
Trapani, G. D., & Clarke, F. M. (2012) · Biochemistry and Molecular Biology Education · doi:10.1002/bmb.20573 · Cited by 25
Testing Empathy with Robots: A Model in Four Dimensions and Sixteen Items
Tisseron, S., Tordo, F., & Baddoura, R. (2014) · International Journal of Social Robotics · doi:10.1007/s12369-014-0268-5 · Cited by 22
Student Professional Development: Competency-Based Learning and Assessment
Baughman, J., Brumm, T. J., & Mickelson, S. K. (2012) · The Journal of Technology Studies · doi:10.21061/jots.v38i2.a.6 · Cited by 21
Whisenhunt, B., Cathey, C. L., Hudson, D. L., & Needy, L. (2022) · Scholarship of Teaching and Learning in Psychology · doi:10.1037/stl0000242 · Cited by 19
Bensah, E. C., Ahiekpor, J. C., & Boateng, C. D. (2011) · Education for Chemical Engineers · doi:10.1016/j.ece.2011.04.001 · Cited by 14
Zlatkin‐Troitschanskaia, O. (2021) · International Journal of Chinese Education · doi:10.1177/22125868211006205 · Cited by 13
Talk, Tutor, Explore, Learn: Intelligent Tutoring and Exploration for Robust Learning
Grawemeyer, B., Gutiérrez-Santos, S., Holmes, W., et al. (2015) · UCL Discovery (University College London) · Cited by 12
Kepanen, P., Määttä, K., & Uusiautti, S. (2019) · Human Arenas · doi:10.1007/s42087-019-00080-y · Cited by 12
Lucey, B. M., & Dowling, M. (2023) · doi:10.64628/ab.rs649ypmk · Cited by 8
(2013) · Journal of Educational Psychology · doi:10.1037/a0034752.supp · Cited by 3
Flagged first, then by citations
Research works printed but not linked: 3
- http://dx.doi.org/10.1002/bmb.20573 · DOI
- Samarakou, M., Fylladitakis, E., Prentakis, P., & Athineos, S. (2014). Implementation of artificial intelligence assessment in engineering laboratory education. International Conference e-Learning. https://fles.eric.ed.gov/fulltext/ED557 263.pdf.
- Utterberg, M., Tallvid, M., Lundin, J., & Lindström, B. (2021). Intelligent tutoring systems: Why teachers abandoned a technology aimed at automating teaching processes. http://hdl.handle.net/10125/70798
Cited by
Sorted by citations
Generative AI in Higher Education: Balancing Innovation and Integrity
Francis, N. J., Jones, S., & Smith, D. P. (2025) · British Journal of Biomedical Science · doi:10.3389/bjbs.2024.14048 · Cited by 295
Metadata
- Authors
- Tenakwah, E. S., Boadu, G., Tenakwah, E. J., Parzakonis, M., Brady, M., Kansiime, P., Said, S., Ayilu, R., Radavoi, C., & Berman, A.
- Affiliations
- Charles Darwin University (AU)
- Corresponding author
- Boadu, Gideon
- ORCID
- Venue
- Research Square
- Published
- 2023-05-25
- OpenAlex
- W4378211874
- Cited by
- 11
- References
- 34
- Citations by year
- 2026: 2 · 2025: 3 · 2024: 5 · 2023: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- Research Square (acceptedVersion, cc-by)
- 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 › Artificial Intelligence Applications
- Keywords
- generative AI, AI language models, higher education assessment, critical reasoning, language proficiency, assessment design, academic writing, multidisciplinary assessment
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