Optimising the Interface Between Artificial Intelligence and Human Intelligence in Higher Education
Open accessAbstract
If you thought the recent pandemic was a major disruptor, society is on the cusp of a far more pervasive and ubiquitous shift in human existence. The rapid development and roll-out of Artificial Intelligence (AI) technologies, is looming as a quintessential disruptor of quantum proportions and will eventually permeate most, if not all, corners of our lives. This paper investigates the initial fall-out and potential impact of AI generative technologies on the higher education sector. When ChatGPT and similar software programs were released in late 2022, a knee-jerk reaction from some Government authorities, and institutions was to ban their use in education. Others recommended the development of academic policies and strategies to mitigate the risks to academic integrity and quality assurance. Pragmatic and adventurous educators embraced the opportunities that AI technologies offer to enhance the education sector and expand opportunities for life-long learning. Whilst there is general consensus that that higher education will need to undergo major reform to address the changes that AI will force on the future of learning and higher education institutions. The way forward is less clear. The proposition explored in this paper is that in order to filter, interpret, evaluate and apply AI generated content it might be helpful to consider the challenge through a research lens. The preliminary result is a Framework which focuses on the interface between artificial intelligence and human intelligence in the development and design of future-orientated curriculum, pedagogy, learning activities and assessments in higher education.
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?
GenAI can dynamically adjust content delivery and assessment methods based on continuous data analysis, modifying learning activities in real-time.
“GenAI can dynamically adjust content delivery and assessment methods based on continuous data analysis, modifying learning activities in real-time [26, 32].”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [32]
The Artificial Intelligence and Human Intelligence Interface Framework discusses how educators might redesign learning activities and assessments, but does not describe GenAI adjusting them through continuous data analysis. Searches for adaptive delivery, real-time modification, and student-performance data found no passage addressing that capability. (LLM, conf=0.76)
Experimental — accuracy not yet measuredPersonalised learning approaches lead to improved student engagement, better understanding of material, and enhanced academic performance.
“The use of personalised learning approaches leads to improved student engagement, better understanding of the material, and enhanced academic performance [26, 30–33].”
From Generative AI in Higher Education: Balancing Innovation and Integrity (2025) · cites this paper as [32]
The Artificial Intelligence and Human Intelligence Interface Framework (p. 20) recommends assessments linked to personal contexts, but does not say that personalised learning improves engagement, understanding, or academic performance. Searches for personalised learning, student engagement, and academic performance found no passage making that connection. (LLM, conf=0.76)
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.
Cites
Sparks of Artificial General Intelligence: Early experiments with GPT-4
Bubeck, S., Chandrasekaran, V., Eldan, R., Gehrke, J., Horvitz, E., Kamar, E., Lee, P., Lee, Y. T., Li, Y., Lundberg, S., et al. (2023) · arXiv · doi:10.48550/arxiv.2303.12712 · Cited by 1,526 · see citing works
(2017) · Cited by 761 · see citing works
Human- versus Artificial Intelligence
Korteling, J. E. (., van de Boer-Visschedijk, G. C., Blankendaal, R. A. M., Boonekamp, R. C., & Eikelboom, A. R. (2021) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2021.622364 · Cited by 619 · see citing works
AI bot ChatGPT writes smart essays — should professors worry?
Stokel-Walker, C. (2022) · Nature · doi:10.1038/d41586-022-04397-7 · Cited by 543 · see citing works
The Alignment Problem: Machine Learning and Human Values
Christian, B. (2021) · Perspectives on Science and Christian Faith · doi:10.56315/pscf12-21christian · Cited by 311 · see citing works
The death of the short-form physics essay in the coming AI revolution
Yeadon, W., Inyang, O.-O., Mizouri, A., Peach, A., & Testrow, C. P. (2023) · Physics Education · doi:10.1088/1361-6552/acc5cf · Cited by 199 · see citing works
Bommarito, M. J., & Katz, D. M. (2022) · doi:10.2139/ssrn.4314839 · Cited by 181 · see citing works
The alignment problem from a deep learning perspective
Ngo, R., Chan, L., & Mindermann, S. (2022) · arXiv · doi:10.48550/arxiv.2209.00626 · Cited by 68 · see citing works
Current and Near-Term AI as a Potential Existential Risk Factor
Bucknall, B. S., & Dori-Hacohen, S. (2022) · Proceedings of the AAAI/ACM Conference on AI Ethics and Society · doi:10.1145/3514094.3534146 · Cited by 48 · see citing works
The role of Artificial Intelligence in Education: Current Trends and Future Prospects
Yousuf, M., & Wahid, A. (2021) · International Conference on Information Science and Communications Technologies (ICISCT) · doi:10.1109/icisct52966.2021.9670009 · Cited by 40 · see citing works
Artificial Intelligence (AI) and its Potential Impact on the Future of Higher Education
Bennett, L., & Abusalem, A. (2024) · Athens Journal of Education · doi:10.30958/aje.11-3-2 · Cited by 24 · see citing works
Self-citationResearch and development (R&D)
(2014) · doi:10.1787/09614029-en · Cited by 5 · see citing works
The university challenge changing universities in a changing world
Byrne, E., & Clarke, C. (2020) · Cited by 5 · see citing works
Flagged first, then by citations
2 of the 15 references couldn’t be matched to a known paper.
Research works printed but not linked: 5
- Carroll, L. (1865). Alice's Adventures in Wonderland. The Original 1865 Edition With Complete Illustrations By Sir John Tenniel (A Classic Novel of Lewis Carroll) 2021, Paperback.
- Chamorro-Premuzic, T. (2023). I, Human: AI, Automation, and the Quest to Reclaim What Makes Us Human. Harvard Business Review Press.
- Chui, M., Hall. B., Mayhew, H. & Singla, A. (2022b). The state of AI in 2022-and a half decade in Review, McKinsey and Company Dec 6, 2022. Available at: https://www.mckinsey.com/capabilities/quantumblac k/our-insights/the-state-of-ai-in-2022-and-a-half- decade-in-review
- Dondi, M., Klier, J.,F Panier, F. and Schubert, J. (2021). Defining the skills citizens will need in the future world of work. McKinsey and Company. Available at: https://www.semanticscholar.org/paper/Defining-the- skills-citizens-will-need-in-the-world-Dondi- Klier/3fbbf024d28890bffa6cf4a71e84a58f37646a51
- Girdhar, N. (2022). The Role of Artificial Intelligence in Education Sector: Possibilities and Challenges. Chapter in Machine Learning, Blockchain, and Cyber Security in Smart Environments Application and Challenges. Taylor and Francis Group. DOI: 10.1201/9781003240310- 2/role-artificial-intelligence-education-sector- possibilities-challenges-riya-aggarwal-nancy-girdhar- alpana · DOI
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 · see citing works
“GenAI can dynamically adjust content delivery and assessment methods based on continuous data analysis, modifying learning activities in real-time [26, 32].”
Artificial intelligence in higher education, opportunities, and challenges: a review
AlBlooshi, S. (2026) · Frontiers in Education · doi:10.3389/feduc.2025.1683968 · Cited by 62 · see citing works
Li, Y., Ji, W., Liu, J., & Li, W. (2024) · International Conference on Interactive Intelligent Systems and Techniques (IIST) · doi:10.1109/iist62526.2024.00099 · Cited by 17 · see citing works
The Role of Generative AI in Empowering Generation Z in Higher Education
Zábojník, R., & Hromada, V. (2024) · Media & Marketing Identity · doi:10.34135/mmidentity-2024-75 · Cited by 11 · see citing works
The ETHICAL Protocol for Responsible Use of Generative AI for Research Purposes in Higher Education
Alduais, A. M. S., Qadhi, S., Chaaban, Y., & Khraisheh, M. A. M. (2025) · AI Magazine · doi:10.1002/aaai.70047 · Cited by 8 · see citing works
Artificial Intelligence and Educational Quality in Higher Education: A Systematic Review
Sir, V. M. V., Reátegui, J. A., Guevara, R. C., Revilla, A. C., & Valqui, C. V. (2025) · TEM Journal · doi:10.18421/tem144-82 · Cited by 5 · see citing works
Yeboah, A. K., Dadson, Y. A., Mohammed, Z., & Hussein, M. (2026) · Artificial Intelligence in Education · doi:10.1108/aiie-07-2025-0184 · Cited by 4 · see citing works
The Future of AI in Educational Assessment
Trajkovski, G. P., & Hayes, H. (2025) · Digital education and learning · doi:10.1007/978-3-031-88252-4_9 · Cited by 4 · see citing works
Ferrada, C., Trujillo, F. J. K., & Triviño, C. M. (2025) · Yachay - Revista Científico Cultural · doi:10.36881/yachay.v14i1.1025 · Cited by 1 · see citing works
Governance alignment and institutional reform in generative AI enabled higher education
Sunendar, D., Hardini, T. I., Mudzakir, A., Stofiana, T., Siregar, I. W., Maulana, C., & Yanti, D. (2026) · Journal of Applied Research in Higher Education · doi:10.1108/jarhe-02-2026-0299 · Cited by 1 · see citing works
Mangendre, Y., Nggolaon, D., & Nurlaela, N. (2026) · International Journal of Social Learning (IJSL) · doi:10.47134/ijsl.v6i2.546
Education 6.0: A Human-AI Co-Creation Framework for Future Higher Education
Baba, K. R. K., Neupane, T. P., Sahu, V. K., Panda, S., & Rath, S. R. (2026) · International Journal on Research and Development - A Management Review · doi:10.65521/ijrdmr.v15i2.3832
- Authors
- Bennett, L.
- Corresponding author
- Bennett, Lorraine
- Venue
- International Journal of Teaching Learning and Education
- Publisher
- AI Publications
- Published
- 2023
- Volume
- 2
- Issue
- 3
- Pages
- 12-25
- ISSN
- 2583-4371 (ISSN-L)
- OpenAlex
- W4383647139
- Cited by
- 11
- References
- 15
- Citations by year
- 2026: 4 · 2025: 4 · 2024: 2 · 2023: 1
- FWCI
- 2.58
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- International Journal of Teaching Learning and Education (publishedVersion)
- 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 › Information Systems › Digital Education and Society
- Keywords
- artificial intelligence in higher education, generative AI, ChatGPT, human intelligence, academic integrity, quality assurance, curriculum development, pedagogy, assessment design, lifelong learning, AI governance in education, future of learning
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