Mapping out a research agenda for generative artificial intelligence in tertiary education
Open accessAbstract
Generative artificial intelligence (AI) has taken the world by storm. In this editorial, we outline some of the key areas of tertiary education impacted by large language models and associated applications that will require re-thinking and research to address in the short to medium term. Given how rapidly generative AI developments are currently occurring, this editorial is speculative. Although there is a long history of research on AI in education, the current situation is both unprecedented and seemingly not something that the AI in education community fully predicted. We also outline the editorial position of AJET in regards to generative AI to assist authors using tools such as ChatGPT as any part of the research or writing process. This is a rapidly evolving space. We have attempted to provide some clarity in this editorial while acknowledging that we may need to revisit some or all of what we offer here in the weeks and months ahead.
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
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 instant grading provided on assessments ensures that students receive timely and constructive feedback on their performance.
“In addition, the instant grading provided on assessments ensures that students receive timely and constructive feedback on their performance [44,50].”
From New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution (2023) · cites this paper as [50]
The Assessment redesign section describes real-time formative feedback on students’ work, supporting the timely-feedback part of the claim. It does not describe instant grading of assessments, and it warns that AI feedback can be misleading or wrong, so the claim that such grading ensures constructive feedback is overstated. No page number is available in our copy. (LLM, conf=0.88)
Experimental — accuracy not yet measuredTechnological breakthroughs in GenAI have massively reduced the cost and effort required for students to engage in cheating behaviour.
“technological breakthroughs in GenAI have massively reduced the cost and effort required for students to engage in cheating behaviour (Lodge, Thompson, and Corrin 2023).”
From How does GenAI affect trust in teacher-student relationships? Insights from students’ assessment experiences (2025) · cites this paper as Lodge, Thompson, and Corrin 2023
In the context of GenAI, complexity plays a unique role.
“In the context of GenAI, complexity plays a unique role [1].”
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Cites
Chapter 12: What does the Anthropocene hold for citizenship?
(2019) · edoc (University of Basel) · doi:10.1007/978-3 · Cited by 11,533 · see citing works
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · see citing works
Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-019-0171-0 · Cited by 7,242 · see citing works
Artificial Intelligence in Education: A Review
Chen, L., Chen, P., & Lin, Z. (2020) · IEEE Access · doi:10.1109/access.2020.2988510 · Cited by 4,133 · see citing works
Ethical principles for artificial intelligence in education
Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B.-P. T. (2023) · Education and Information Technologies · doi:10.1007/s10639-022-11316-w · Cited by 1,228 · see citing works
Xie, H., Chu, H.-C., Hwang, G.-J., & Wang, C.-C. (2019) · Computers & Education · doi:10.1016/j.compedu.2019.103599 · Cited by 713 · see citing works
Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT
Dai, W., Lin, J., Jin, F., Li, T., Tsai, Y.-S., Gasevic, D., & Chen, G. (2023) · doi:10.35542/osf.io/hcgzj · Cited by 395 · see citing works
Can Large Language Models Provide Feedback to Students? A Case Study on ChatGPT
Dai, W., Lin, J., Jin, H., Li, T., Tsai, Y.-S., Gašević, D., & Chen, G. (2023) · 2023 IEEE International Conference on Advanced Learning Technologies (ICALT) · doi:10.1109/icalt58122.2023.00100 · Cited by 395 · see citing works
Peng, H., Ma, S., & Spector, J. M. (2019) · Smart Learning Environments · doi:10.1186/s40561-019-0089-y · Cited by 346 · see citing works
Human and artificial intelligence collaboration for socially shared regulation in learning
Järvelä, S., Nguyen, A., & Hadwin, A. (2023) · British Journal of Educational Technology · doi:10.1111/bjet.13325 · Cited by 342 · see citing works
Empowering learners for the age of artificial intelligence
Gašević, D., Siemens, G., & Sadiq, S. (2023) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2023.100130 · Cited by 234 · see citing works
The Routledge International Handbook of Research on Teaching Thinking
(2015) · Routledge eBooks · doi:10.4324/9781315797021 · Cited by 201 · see citing works
Implementation of adaptive learning at higher education institutions by means of Moodle LMS
Морзе, Н., Varchenko-Trotsenko, L. O., Terletska, T. S., & Smyrnova‐Trybulska, E. (2021) · Journal of Physics Conference Series · doi:10.1088/1742-6596/1840/1/012062 · Cited by 176 · see citing works
Teaching and Learning with AI in Higher Education: A Scoping Review
Kuka, L., Hörmann, C., & Sabitzer, B. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-04286-7_26 · Cited by 22 · see citing works
Flagged first, then by citations
Research works printed but not linked: 2
- Bender, E. M., Gebru, T., McMillan-Major, A., & Shmitchell, S. (2021). On the dangers of stochastic parrots: Can language models be too big? In Proceedings of the 2021 ACM conference on fairness, accountability, and transparency (pp. 610-623).
- Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28-47.
Cited by
Sorted by citations
New Era of Artificial Intelligence in Education: Towards a Sustainable Multifaceted Revolution
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023) · Sustainability · doi:10.3390/su151612451 · Cited by 1,089 · see citing works
Bahroun, Z., Anane, C., Ahmed, V., & Zacca, A. (2023) · Sustainability · doi:10.3390/su151712983 · Cited by 784 · see citing works
Lee, D., Arnold, M., Srivastava, A., Plastow, K., Strelan, P., Ploeckl, F., Lekkas, D., & Palmer, E. (2024) · Computers and Education: Artificial Intelligence · doi:10.1016/j.caeai.2024.100221 · Cited by 402 · see citing works
Luo (Jess), J. (2024) · Assessment & Evaluation in Higher Education · doi:10.1080/02602938.2024.2309963 · Cited by 366 · see citing works
“Among these criticisms, a large number of opinion articles and research papers have been published (e.g. Chan 2023a; Lodge, Thompson, and Corrin 2023; Rudolph, Tan, and Tan 2023).”
InfluentialThe influence of AI text generators on critical thinking skills in UK business schools
Essien, A., Bukoye, O. T., O’Dea, X., & Kremantzis, M. (2024) · Studies in Higher Education · doi:10.1080/03075079.2024.2316881 · Cited by 252 · see citing works
Samala, A. D., Rawas, S., Wang, T., Reed, J. M., Kim, J., Howard, N.-J., & Ertz, M. (2025) · Education and Information Technologies · doi:10.1007/s10639-024-12936-0 · Cited by 229 · see citing works
Al-Abdullatif, A. M. (2024) · Education Sciences · doi:10.3390/educsci14111209 · Cited by 169 · see citing works
The Educational Affordances and Challenges of ChatGPT: State of the Field
Crompton, H., & Burke, D. (2024) · TechTrends · doi:10.1007/s11528-024-00939-0 · Cited by 141 · see citing works
“ChatGPT can assist teachers in the creation of content, lesson plans, and learning activities (Lodge et al., 2023; Trust et al., 2023).”
InfluentialMastering knowledge: the impact of generative AI on student learning outcomes
Pallant, J. L., Blijlevens, J., Campbell, A., & Jopp, R. (2026) · Studies in Higher Education · doi:10.1080/03075079.2025.2487570 · Cited by 94 · see citing works
Race with the machines: assessing the capability of generative AI in solving authentic assessments
Nguyen Thanh, B., Vo, D. T. H., Nguyen Nhat, M., Pham, T. T. T., Thai Trung, H., & Ha Xuan, S. (2023) · Australasian Journal of Educational Technology · doi:10.14742/ajet.8902 · Cited by 87 · see citing works
“Our proposed framework and findings help educators and tertiary education to navigate through the rising challenges with regard to assessment integrity and design as pointed out by Lodge et al. (2023).”
BackgroundMethodInfluentialLuo (Jess), J. (2025) · Teaching in Higher Education · doi:10.1080/13562517.2024.2341005 · Cited by 85 · see citing works
Lodge, J. M., Yang, S., Furze, L., & Dawson, P. (2023) · Learning: Research and Practice · doi:10.1080/23735082.2023.2261106 · Cited by 90 · see citing works
“It is already emerging that there are a range of issues related to the fundamental predictive nature of generative AI (see Lodge et al., 2023) suggesting that such a simple relationship between learners and machines is not what is apparent.”
BackgroundSelf-citationLiu, X., & Zhong, B. (2025) · Educational Research Review · doi:10.1016/j.edurev.2025.100741 · Cited by 78 · see citing works
Learning with Generative Artificial Intelligence Within a Network of Co-Regulation
Lodge, J. M., De Barba, P., & Broadbent, J. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/1.20.7.02 · Cited by 73 · see citing works
Self-citationVan Wyk, M. M. (2024) · Journal of Applied Learning and Teaching · doi:10.37074/jalt.2024.7.1.15 · Cited by 35 · see citing works
Stratton-Maher, D., & Kelly, J. (2026) · Nurse Education Today · doi:10.1016/j.nedt.2025.106961 · Cited by 12 · see citing works
Learning with Generative Artificial Intelligence Within a Network of Co-Regulation
Lodge, J. M., De Barba, P., & Broadbent, J. (2024) · Journal of University Teaching and Learning Practice · doi:10.53761/m2v9an32 · Cited by 9 · see citing works
Self-citationEditorial: Framing the Future with a Research Agenda for Artificial Intelligence in Higher Education
Fitzgerald, R., Amantha Kumar, J., Roe, J., Roehrer, E., & Yang, J. (2025) · Journal of University Teaching and Learning Practice · doi:10.53761/jwt7ra63 · Cited by 8 · see citing works
Faculty as street-level bureaucrats: discretionary decision-making in the era of generative AI
Alsharefeen, R. (2025) · Frontiers in Education · doi:10.3389/feduc.2025.1662657 · Cited by 7 · see citing works
- Authors
- Lodge, J. M., Thompson, K., & Corrin, L.
- Affiliations
- The University of Queensland (AU)
- Queensland University of Technology (AU)
- Deakin University (AU)
- Venue
- Australasian Journal of Educational Technology
- Published
- 2023-05-04
- Volume
- 39
- Issue
- 1
- Pages
- 1-8
- ISSN
- 1449-3098 (ISSN-L)
- 1449-5554
- OpenAlex
- W4368617336
- Cited by
- 209
- References
- 15
- Citations by year
- 2026: 62 · 2025: 67 · 2024: 60 · 2023: 20
- Type
- journal article
- Language
- en
- Open access
- diamond
- doi.org
- Licence
- Locations
- Australasian Journal of Educational Technology (publishedVersion, cc-by-nc-nd)
- QUT ePrints (Queensland University of Technology) (submittedVersion, cc-by-nc-nd)
- Figshare (submittedVersion, cc-by)
- Topics
- Physical Sciences › Computer Science › Information Systems › 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
- generative AI, large language models, higher education, AI in education research, research agenda, ChatGPT, academic writing, AI-assisted research
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
