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ChatGPT: Bullshit spewer or the end of traditional assessments in higher education?
Open accessAbstract
ChatGPT is the world’s most advanced chatbot thus far. Unlike other chatbots, it can create impressive prose within seconds, and it has created much hype and doomsday predictions when it comes to student assessment in higher education and a host of other matters. ChatGPT is a state-of-the-art language model (a variant of OpenAI’s Generative Pretrained Transformer (GPT) language model) designed to generate text that can be indistinguishable from text written by humans. It can engage in conversation with users in a seemingly natural and intuitive way. In this article, we briefly tell the story of OpenAI, the organisation behind ChatGPT. We highlight the fundamental change from a not-for-profit organisation to a commercial business model. In terms of our methods, we conducted an extensive literature review and experimented with this artificial intelligence (AI) software. Our literature review shows our review to be amongst the first peer-reviewed academic journal articles to explore ChatGPT and its relevance for higher education (especially assessment, learning and teaching). After a description of ChatGPT’s functionality and a summary of its strengths and limitations, we focus on the technology’s implications for higher education and discuss what is the future of learning, teaching and assessment in higher education in the context of AI chatbots such as ChatGPT. We position ChatGPT in the context of current Artificial Intelligence in Education (AIEd) research, discuss student-facing, teacher-facing and system-facing applications, and analyse opportunities and threats. We conclude the article with recommendations for students, teachers and higher education institutions. Many of them focus on assessment.
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“The advent of GenAI has unveiled a plethora of threats to higher education and the broader societal fabric, encompassing concerns over authorship, copyright infringement, and the potential for misuse in activities such as spamming, disseminating fake news, creating malware, and hacking (Marcus, 2023;Rudolph et al., 2023aRudolph et al., , 2023b)).”
From Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania (2024) · cites this paper as Rudolph et al., 2023a
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“It is considered by many as the most powerful AI tool ever crated [9].”
From New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023) · cites this paper as [9]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“While AI technology offers promising ability for personalized learning experiences, efficient assessment, and adaptive feedback, it also presents novel challenges to the dynamics of the traditional educational bond [9].”
From New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023) · cites this paper as [9]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“The diverse learning trajectories observed in these cases illustrate how AI tools can facilitate non-linear, adaptive learning processes by enabling students to dynamically integrate new information with prior knowledge and external resources [15, 16].”
From Scaffolding Creativity: Integrating Generative AI Tools and Real-World Experiences in Business Education (2025) · cites this paper as [16]
Experimental — accuracy not yet measuredThe source backs part of the claim, not all of it — or backs it for a different population, condition or comparison.
In the citing paper
“It is more student-friendly and can provide more need-based assistance than other AWE tools, as suggested by Guo et al. (2022) and Rudolph et al. (2023).”
From Impact of ChatGPT on ESL students’ academic writing skills: a mixed methods intervention study (2024) · cites this paper as Rudolph et al. (2023)
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“On the other hand, the promotion of learner autonomy and peer collaboration are similar to findings in the AWE literature (Dai et al., 2023;Rudolph et al., 2023).”
From Impact of ChatGPT on ESL students’ academic writing skills: a mixed methods intervention study (2024) · cites this paper as Rudolph et al., 2023
Experimental — accuracy not yet measuredThe cited source says what the claim attributes to it.
In the citing paper
“GPT-3's training on a vast corpus of 45 terabytes of text, sourced from a variety of internet-based entities like Common Crawl and Wikipedia, set a precedent for the scale of data utilised in AI development (Brown et al., 2020;Rudolph et al., 2023a).”
From Higher Education's Generative Artificial Intelligence Paradox: The Meaning of Chatbot Mania (2024) · cites this paper as Rudolph et al., 2023a
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In the citing paper
“They highlight the transformative potential of generative AI in revolutionizing academic practices but also caution about its ability to facilitate academic dishonesty, such as through generating test answers [25].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [25]
The cited source says what the claim attributes to it.
In the citing paper
“They highlight the transformative potential of generative AI in revolutionizing academic practices but also caution about its ability to facilitate academic dishonesty, such as through generating test answers [25].”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [25]
In the citing paper
“However, concerns have been raised about ethical consideration and decreased reliability of the existing examinations.”
From Developing Medical Education Curriculum Reform Strategies to Address the Impact of Generative AI: Qualitative Study (2023) · cites this paper as [8]
In the citing paper
“On the other hand, the impact of GAI on education is disputed [6], with studies raising concerns about the ethical considerations of ChatGPT [7], evaluation practices [8], scientific integrity [9], and potential negative effects on students’ higher order thinking skills [10].”
From Developing Medical Education Curriculum Reform Strategies to Address the Impact of Generative AI: Qualitative Study (2023) · cites this paper as [8]
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“Assessment content could also be focused on students’ reflective analysis of specific classroom discussions or teaching content, or the latest topics in a given area, with the hope that GenAI tools are not updated yet to include the relevant data (Rudolph et al., 2023).”
InfluentialMohammed, I. A., Bello, A., & Ayuba, B. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-13293-8 · Cited by 23
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Lee, A. V. Y. (2024) · Asia Pacific Journal of Education · doi:10.1080/02188791.2024.2305171 · Cited by 20
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Khurma, O. A., Ali, N., & Hashem, R. (2023) · International Journal of Emerging Technologies in Learning (iJET) · doi:10.3991/ijet.v18i14.40935 · Cited by 24
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“Selected examples in healthcare and education include AI for clinical data systems, educational genomics mapping, health-care social media, and ChatGPT (Burke, 2021; Leming et al., 2023; Rudolph et al., 2023).”
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AI and Human Reasoning: Qualitative Research in the Age of Large Language Models
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Quo Vadis, University? A Roadmap for AI and Ethics in Higher Education
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Can large language models be sensitive to culture suicide risk assessment?
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“Nevertheless, the current findings enhance our knowledge about the capabilities of ChatGPT, demonstrating that this GenAI platform encompasses more than merely theoretical and semantic knowledge (Kung et al., 2023; Rudolph et al., 2023).”
Inteligencia artificial y redacción científica: aspectos éticos en el uso de las nuevas tecnologías
Barrios, I. (2023) · Medicina Clínica y Social · doi:10.52379/mcs.v7i2.278 · Cited by 19
ChatGPT and generation 'Z': a study on the usage rates of ChatGPT
Babu, M. A., Yusuf, K. M., Eni, L. N., Jaman, S. M. S., & Sharmin, M. R. (2024) · Social Sciences & Humanities Open · doi:10.1016/j.ssaho.2024.101163 · Cited by 19
Enhancing Academic Performance with Generative AI-Based Quiz Platform
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AI Chatbots for Language Practices
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“These issues will require further research to be satisfactorily addressed, including speaking anxiety, cultural appropriateness, and lack of immediate feedback (Ghafar, 2023; Rudolph et al., 2023; Xu et al., 2023).”
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“Its ability to excel in tasks such as grammatical correction and sentence rephrasing makes it a strong candidate for providing detailed ACF, potentially alleviating educators’ workloads (Guo & Wang, 2024; Rudolph et al., 2023; Tossell et al., 2024).”
Peters, M., & Angelov, D. (2025) · International Journal for Educational Integrity · doi:10.1007/s40979-025-00201-x · Cited by 27
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ChatGPT in Education, Healthcare, and Cybersecurity: Opportunities and Challenges
Mohammed, S. P., & Hossain, G. (2024) · IEEE Annual Computing and Communication Workshop and Conference (CCWC) · doi:10.1109/ccwc60891.2024.10427923 · Cited by 18
“ChatGPT can inspire creativity and produce high-quality essays [4].”
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“The diverse learning trajectories observed in these cases illustrate how AI tools can facilitate non-linear, adaptive learning processes by enabling students to dynamically integrate new information with prior knowledge and external resources [15, 16].”
Mentigo: An Intelligent Agent for Mentoring Students in the Creative Problem Solving Process
Zha, S., Liu, Y., Zheng, C., Xu, J., Yu, F., Gong, J., & Xu, Y. (2025) · Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3706598.3713952 · Cited by 19
“Recent studies highlight the need for more structured strategies and guidelines for integrating generative AI into educational settings [25, 38, 39].”
Agency in the age of generative AI: a critical review of educational implications
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“The risks of GenAI tools in education also relate to academic integrity and ownership of work (Cotton et al., 2023; Perkins, 2023; Rudolph et al., 2023), as well as the undetectability of GenAI outputs in submitted student work (Perkins et al., 2023; Perkins, Roe, et al., 2024; Chaka, 2024;…”
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“It’s essential to consider recent literature focusing on students’ potential overtrust and overreliance on language model outputs in assessment contexts (Rudolph et al., 2023).”
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Reconsidering teacher assessment literacy in GenAI-enhanced environments: A scoping review
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Artificial Intelligence Threatens Critical Thinking in Education Systems
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Plagio a la Inteligencia Artificial en estudiantes de bachillerato: un problema real
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Integrating Large Language Models into Higher Education: Guidelines for Effective Implementation
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“[13] conducted a critical examination of ChatGPT's potential impact on traditional assessment methods within higher education.”
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Тихонова, Е. В., & Raitskaya, L. (2023) · Journal of language and Education · doi:10.17323/jle.2023.18119 · Cited by 22
“…by ChatGPT, potential for education (Crompton, & Burke, 2023; Ivakhnenko, & Nikolskiy, 2023; Fuchs, 2023; Kikalishvili, 2023; Su, & Yang, 2023; Rudolph, Tan, & Tan, 2023), ethical challenges for pub - lishing, ChatGPT and assessment in education (Rudolph, & Tan, 2023), AI-based bot impact on…”
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Arista, A., Shuib, L. M., & Ismail, M. A. (2023) · International Conference on Informatics, Multimedia, Cyber and Information System (ICIMCIS) · doi:10.1109/icimcis60089.2023.10349053 · Cited by 20
“[34]”
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“Furthermore, Rudolph et al. [12] discuss the potential impact of ChatGPT on formal assessments in higher education, while Sallam [13] explores the utility of ChatGPT in healthcare education, research, and practice.”
BackgroundMapping the individual, social and biospheric impacts of Foundation Models
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Hao, L., Kun, T., Leng, C. H., & Salleh, U. K. M. (2024) · Thinking Skills and Creativity · doi:10.1016/j.tsc.2024.101578 · Cited by 18
Threading the GenAI needle: Unpacking the ups and downs of GenAI for higher education stakeholders
Neupane, A., Shahi, T. B., Cowling, M., & Tanna, D. (2024) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2024.7.2.4 · Cited by 17
Teacher perspectives of ChatGPT as a pedagogical tool in the K-12 setting: a case study
Bateman, T. (2024) · Quality Assurance in Education · doi:10.1108/qae-02-2024-0042 · Cited by 17
A Bibliometric Analysis of Artificial Intelligence Applications in Global Higher Education
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Does Artificial Intelligence Cause More Harm than Good in Schools?
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“With the integration of AI into existing educational platforms and applications, teachers are currently confronted with a manageable challenge rather than an apocalyptic scenario (Rudolph et al., 2023).”
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Artificial Intelligence (ChatGPT) and Bloom’s Taxonomy in Theoretical Computer Science Education
Habiballa, H., Kotyrba, M., Volná, E., Bradáč, V., & Dusek, M. (2025) · Applied Sciences · doi:10.3390/app15020581 · Cited by 23
Awareness, perception, and adoption of ChatGPT in African HEIs: A multi-dimensional analysis
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A Systematic Review of Research on ChatGPT in Higher Education
Döş, İ. (2025) · The European Educational Researcher · doi:10.31757/euer.824 · Cited by 34
“Some universities allow the use of ChatGPT for brainstorming or language correction but prohibit submission of AI-generated content as final work unless disclosed and appropriately cited (Rudolph et al., 2023).”
Enhancing statistics education through Project‐Based Learning ( PBL ) and the emergence of ChatGPT
Al‐Labadi, L., & Ly, A. (2025) · Teaching Statistics · doi:10.1111/test.12405 · Cited by 25
In ChatGPT they trust: a study of students' perceptions and misuse of ChatGPT in higher education
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“Artificial Intelligence in Education (AIEd) is currently one of the most debated topics in all areas of education research [19, 60, 67].”
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Early Research Trends on ChatGPT: Insights from Altmetrics and Science Mapping Analysis
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Los Riesgos de la Inteligencia Artificial en la Educación
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In-Page Navigation Aids for Screen-Reader Users with Automatic Topicalisation and Labelling
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“For this reason, the evaluation could not be conducted with the same user group as the previous evaluation.”
Field courses for dummies: To what extent can ChatGPT design a higher education field course?
Tupper, M. H., Hendy, I. W., & Shipway, J. R. (2024) · Innovations in Education and Teaching International · doi:10.1080/14703297.2024.2316716 · Cited by 38
“ChatGPT is a language model trained on a vast corpus of text data from the internet, allowing it to generate coherent and contextually relevant responses to user inputs (Rudolph et al., 2023).”
Metadata
- Authors
- Rudolph, J., Tan, S., & Tan, S.
- Venue
- Journal of Applied Learning & Teaching
- Publisher
- Open Access Publishing Association
- Published
- 2023-01-25
- Volume
- 6
- Issue
- 1
- ISSN
- 2591-801X (ISSN-L)
- OpenAlex
- W4317910584
- Semantic Scholar
- 9460f9fbc532416c8ec39591577032a5fa4a79b3
- Cited by
- 1,806
- References
- 84
- Citations by year
- 2026: 258 · 2025: 481 · 2024: 648 · 2023: 418
- FWCI
- 424.07
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- other-oa
- Locations
- Journal of Applied Learning & Teaching (publishedVersion, other-oa)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Health Sciences › Medicine › Health Informatics › Artificial Intelligence in Healthcare and Education
- Social Sciences › Education › Academic integrity and plagiarism
- Keywords
- ChatGPT, generative AI, student assessment, higher education, AI chatbots, artificial intelligence in education, OpenAI, future of teaching and learning, academic integrity
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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