Co-inference and collaboration: empowering higher education staff and students through a GenAI literacy development framework based on mind-metaphors
DOI:
https://doi.org/10.47408/jldhe.vi38.1561Keywords:
GenAI literacy, co-inference, mind-metaphors, human agency in LLM use, augmentation, GenAI in educationAbstract
Higher education staff and students are beginning to emerge from the liminal space created by the exponential rise in generative artificial intelligence technologies such as large language models. Both groups exhibit a low or developing understanding of the issues related with the use of such tools. On one hand, staff in many institutions are concerned about critical skills development, academic integrity, ethical implications and the long-term cognitive effects of the use of such tools. Equally, students are concerned about the acceptability of the use of such tools at university and the need to develop work-related GenAI skills. On the other hand, foundational large language models are being trained on larger and better data sets and have evolved into ‘reasoning’ models that can allegedly ‘think’. Furthermore, with the emergence of agentic AI, systems that can ‘act’ independently, claims of shifting agency away from humans to machines are common. This has implications for the world of work and for higher education institutions. This opinion piece presents a new framework that I have developed for enhancing the GenAI literacy of staff and students and to empower and augment themselves with GenAI tools in a balanced, meaningful and collaborative way, whilst maintaining their agency. The mind-metaphors framework includes techniques such as mind-surfing, mind-mending, mind-bending, mind-gaming, mind-storming and mind-stretching, which can help staff and students to develop trust in their chosen GenAI models and master co-inference and collaborative GenAI use.
References
Abels, C. M., Lopez-Lopez, E., Burton, J.W., Holford, D.L., Brinkmann, L., Herzog, S.M. and Lewandowsky, S. (2025) ‘The governance & behavioral challenges of generative artificial intelligence’s hypercustomization capabilities’, Behavioral Science & Policy, 11(1), pp.22-32. Available at: https://doi.org/10.1177/23794607251347020
Achiam, J., Adler, S., Agarwal, S., Ahmad, L., Akkaya, I., Aleman, F.L., Almeida, D., Altenschmidt, J. et al. (2023) 'Gpt-4 technical report', arXiv preprint arXiv:2303.08774. Available at: https://doi.org/10.1177/23794607251347020 (Accessed: 30 November 2025).
Akbulut, C., Weidinger, L., Manzini, A., Gabriel, I. and Rieser, V. (2024) 'All too human? Mapping and mitigating the risk from anthropomorphic AI', Proceedings of the AAAI/ACM Conference on AI, Ethics, and Society, 7(1), pp.13-26. Available at: https://doi.org/10.1609/aies.v7i1.31613
Andersen, J.P., Degn, L., Fishberg, R., Graversen, E.K., Horbach, S.P., Schmidt, E.K., Schneider, J.W. and Sørensen, M.P. (2025) 'Generative artificial intelligence (GenAI) in the research process – a survey of researchers’ practices and perceptions', Technology in Society, 81, p. 102813. Available at: https://doi.org/10.1016/j.techsoc.2025.102813
Cascella, M., Semeraro, F., Montomoli, J., Bellini, V., Piazza, O. and Bignami, E. (2024) 'The breakthrough of large language models release for medical applications: 1-year timeline and perspectives', Journal of Medical Systems, 48, p. 22. Available at: https://doi.org/10.1007/s10916-024-02045-3
Concannon, F., Costello, E., Farrell, O., Farrelly, T. and Wolf, L.G. (2023) 'There's an AI for that: rhetoric, reality, and reflections on EdTech in the dawn of GenAI', Irish Journal of Technology Enhanced Learning, 7(1). Available at: https://doi.org/10.22554/ijtel.v7i1.116
Coulardeau, J. (2018) 'No cognition without communication', Journal of Psychology Research, 8(9), pp. 443–477. Available at: https://api.semanticscholar.org/CorpusID:149655973 (Accessed: 30 November 2025).
Demirkan, H. and Spohrer, J. (2015) 'T-shaped innovators: identifying the right talent to support service innovation', Research-Technology Management, 58(5), pp.12–15. Available at: https://doi.org/10.5437/08956308X5805007
Dubey, S.S., Astvansh, V. and Kopalle, P.K. (2024) 'Generative AI solutions to empower financial firms', Journal of Public Policy & Marketing. Available at: https://dx.doi.org/10.2139/ssrn.5050951
Faruqui, N., Raju, N.V.D.S.S.V.P., Sivakumar, S., Patel, N., Bhaskaran, S.V., Khanam, S. and Bhuiyan, T. (2025) 'Gen-Optimizer: a generative AI framework for strategic business cost optimization', Computers, 14(2), p.59. Available at: https://doi.org/10.3390/computers14020059
Freeman, J. (2025) 'HEPI/Kortext AI survey shows explosive increase in the use of generative AI tools by students', Higher Education Policy Institute (HEPI), 26 February. Available at: https://www.hepi.ac.uk/2025/02/26/hepi-kortext-ai-survey-shows-explosive-increase-in-the-use-of-generative-ai-tools-by-students/ (Accessed: 5 March 2025).
Hua, H. and Yao, C.J. (2024) 'Investigating generative AI models and detection techniques: impacts of tokenization and dataset size on identification of AI-generated text', Frontiers in Artificial Intelligence, 7, article 1469197. https://doi.org/10.3389/frai.2024.1469197
Hua, W. and Zhang, Y. (2022) 'System 1+ system 2= better world: neural-symbolic chain of logic reasoning', Findings of the Association for Computational Linguistics: EMNLP, Abu Dhabi December, pp.601–612. Available at: https://doi.org/10.18653/v1/2022.findings-emnlp.42
Kahneman, D. (2011) Thinking, fast and slow. Allen Lane and Penguin Books, New York.
Kosmyna, N., Hauptmann, E., Yuan, Y.T., Situ, J., Liao, X.H., Beresnitzky, A.V., Braunstein, I. and Maes, P. (2025) ‘Your brain on ChatGPT: accumulation of cognitive debt when using an AI assistant for essay writing task’, arXiv preprint arXiv:2506.08872. Available at: https://doi.org/10.48550/arXiv.2506.08872
Lakoff, G. and Johnson, M. (2008) Metaphors we live by. Chicago: University of Chicago Press.
Lee, H.P., Sarkar, A., Tankelevitch, L., Drosos, I., Rintel, S., Banks, R. and Wilson, N. (2025) ‘The impact of generative AI on critical thinking: self-reported reductions in cognitive effort and confidence effects from a survey of knowledge workers’, Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. Yokohama, Japan 26 April-1 May. Available at: https://doi.org/10.1145/3706598.3713778
Li, S., Liu, J. and Dong, Q. (2025) ‘Generative artificial intelligence-supported programming education: effects on learning performance, self-efficacy and processes’, Australasian Journal of Educational Technology, 41(3), pp.1–25. Available at: https://doi.org/10.14742/ajet.9932
Malik, M. (2025) Mind-metaphors for co-intelligence and collaboration with GenAI for teaching and learning. YouTube. Available at: https://youtu.be/SZj8qittRjQ?si=5ZzCHeKENzE3Ad17 (Accessed: 30 November 2025).
Malik, M. and Sime, J.A. (2025) ‘Teamwork, co-regulation, and socially shared regulation skills within Engineering education studies: a GenAI-assisted scoping review’, 2025 ASEE Annual Conference & Exposition. Montreal, Quebec, Canada 22-25 June. Available at: https://peer.asee.org/57199 (Accessed: 30 November 2025).
Nikolic, S., Wentworth, I., Sheridan, L., Moss, S., Duursma, E., Jones, R.A., Montserrat Ros, M. and Middleton, R. (2024) 'A systematic literature review of attitudes, intentions and behaviours of teaching academics pertaining to AI and generative AI (GenAI) in higher education: an analysis of GenAI adoption using the UTAUT framework', Australasian Journal of Educational Technology, 40(6), pp.56–75. Available at: https://doi.org/10.14742/ajet.9643
Ninan, J., Hertogh, M. and Liu, Y. (2022) 'Educating engineers of the future: T-shaped professionals for managing infrastructure projects', Project Leadership and Society, 3, article 100071. Available at: https://doi.org/10.1016/j.plas.2022.100071
Oakley, B., Johnston, M., Chen, K., Jung, E. and Sejnowski, T. (2025) ‘The memory paradox: why our brains need knowledge in an age of AI’, in The future of artificial intelligence: economics, society, risks and global policy (Springer Nature, forthcoming). Available at https://dx.doi.org/10.2139/ssrn.5250447
Opus, C. and Lawsen, A. (2025) ‘Comment on the illusion of thinking: understanding the strengths and limitations of reasoning models via the lens of problem complexity’, arXiv preprint arXiv:2506.09250. Available at: https://arxiv.org/abs/2506.09250
Roberts, A.I. and Roberts, S.G. (2020) 'Communicative roots of complex sociality and cognition', Biological Reviews, 95(1), pp.51–73. Available at: https://doi.org/10.1111/brv.12553
Senoner, J., Schallmoser, S., Kratzwald, B., Feuerriegel, S. and Netland, T. (2024) ‘Explainable AI improves task performance in human–AI collaboration’, Scientific Reports, 14(1), 31150. Available at: https://doi.org/10.1038/s41598-024-82501-9
Shojaee, P., Mirzadeh, I., Alizadeh, K., Horton, M., Bengio, S. and Farajtabar, M. (2025) ‘The illusion of thinking: understanding the strengths and limitations of reasoning models via the lens of problem complexity’, arXiv preprint arXiv:2506.06941. Available at: https://doi.org/10.48550/arXiv.2506.06941
Shardlow, M. and Przybyła, P. (2024) 'Deanthropomorphising NLP: can a language model be conscious?', PLoS One, 19(12), article e0307521. Available at: https://doi.org/10.1371/journal.pone.0307521
Singh, R.G. and Ngai, C.S.B. (2024) 'Top-ranked US and UK’s universities’ first responses to GenAI: key themes, emotions, and pedagogical implications for teaching and learning', Discover Education, 3(1), article 115. Available at: https://doi.org/10.1007/s44217-024-00211-w
Snell, C., Lee, J., Xu, K. and Kumar, A. (2024) 'Scaling LLM test-time compute optimally can be more effective than scaling model parameters', arXiv preprint arXiv:2408.03314. Available at: https://arxiv.org/abs/2408.03314 (Accessed: 30 November 2025).
Sperber, D. and Wilson, D. (1987) 'Précis of relevance: communication and cognition', Behavioral and Brain Sciences, 10(4), pp. 697–710. Available at: https://doi.org/10.1017/S0140525X00055345
Tannenbaum, R., (2025) Tokens for education. Available at: http://tokens.for.education/ (Accessed: 30 November 2025).
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L. and Polosukhin, I. (2017) 'Attention is all you need', 31st Conference on Neural Information Processing Systems (NIPS 2017). Long Beach, CA, USA. Available at: https://arxiv.org/pdf/1706.03762
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