New Voices RGU Student Series 2026 – Ross Wilson

Category: RGU Student Series 2026

In the 2026 New Voices Student Series, the CILIPS Students & New Professionals Community will be sharing the views of Robert Gordon University students from the MSc in Information and Library Studies.

With special thanks to Dr Konstantina Martzoukou, Teaching Excellence Fellow and Associate Professor, for organising these thought-provoking contributions.

Ross Wilson is Academic Support Librarian at Robert Gordon University (RGU). He has worked in academic libraries for over ten years, and is currently studying MSc Information and Library Studies to deepen his knowledge of the profession. Ross enjoys reading, heavy metal music, cooking (and eating) Indian cuisine, and playing the drums.

Critical, Credible, but not Credulous: Principles for developing Generative AI Training in Academic Libraries

The Importance of Academic Libraries in Generative AI (GenAI) Literacy

Academic Libraries already provide training in the responsible creation, dissemination, and use of information – which are all dimensions that pertain to the responsible use of GenAI tools. GenAI represents a new threshold in how academic library users navigate their information landscape, but the diverse approaches academic librarians already take (workshops, embedded training within university courses, and online, asynchronous learning materials) are highly adaptable to the challenge. Though the method by which the information is delivered is not unimportant, what matters more are the principles that inform the development of such training. Deepening user’s critical engagement with technologies and information is already the hallmark of effective library user training, but academic librarians must also be willing to challenge popular narratives and develop training that aligns more closely to the truth of what GenAI can (and cannot) do.

A Critical Approach

Embedding robust criticism of GenAI into training programmes is not without its risks. Academic librarians may risk conflicting with the GenAI policies of their institutions, or face accusations of seeking to impede technological progress (Baer, 2025). Some have responded to this potential conflict by incorporating GenAI literacy into existing institutional Information Literacy frameworks, thereby ensuring institutional buy-in to their chosen approach (Hauck, Moore and Wright, 2025).

The oft-cited ‘inevitability’ of GenAI is an example of a crisis narrative: a discourse related to a highly disruptive event that is socially constructed and blends emotions with facts (Burgess, 2021). Crisis narratives are usually imposed on those who receive them (i.e. the public) by powerful entities who create and seek to legitimise them (such as GenAI companies). Crisis narratives are obstacles to critical engagement with ideas, and so it is the duty of the academic librarian developing GenAI training to dismantle such narratives as a precursor to delivering advice and guidance. Refusing to treat GenAI as anything other than another option in a suite of often more effective tools for information retrieval is a place to start.

The development of any GenAI training assumes a level of technological competence by those who receive the training, and so efforts to reduce any barriers (low digital literacy, language barriers, socioeconomic factors, etc.) that affect meaningful engagement with GenAI must be accounted for (Buitrago-Ciro et al., 2025; Hauck, Moore and Wright, 2025; Springmier, 2025). Thus the training must also be foregrounded in research ethics, and foundational concepts related to critical thinking applied to information sources (Badke, 2024; Chaudhuri and Terrones, 2025).

Avoiding Credulousness

GenAI has significant potential to degrade as well as benefit the way societies navigate information (Frank, 2023; Crawford et al., 2024; Baer, 2025). Many problems, such as the ecological and health impacts of GenAI use are well-documented, but others have potential impacts on the domains of information creation, dissemination, and retrieval.

There is a significant risk that, even after investing effort into developing tools to assist users in the responsible use of GenAI, that the venture capital bubble swelling the industry bursts, causing inflated costs for access to GenAI products that may be beyond the use of many users, or even the failure and removal of some Large-Language Model (LLM) GenAI bots chatbots as they will no longer be cost-effective to maintain (Doctorow, 2026). This means that we run the risk of teaching skills for navigating information technologies that could soon be obsolete.

Furthermore, the phenomenon of ‘AI autophagy’ potentially harms the long-term viability of GenAI tools as research companions. If, as hypothesised, GenAI runs out of original material to be trained on, it must be trained on outputs of previous versions of AI programs. This leads to a phenomenon known as ‘model collapse’ where the accuracy, reliability, and overall quality of GenAI output is severely compromised due to ingesting material that is of decreasing quality (Mills, 2025).

It is the duty of academic librarians to not only prepare users to engage with GenAI tools, but to prepare themselves to revise their training offer in response to changes to AI’s relative viability.

Credible Examples

An excellent recent example of online, asynchronous guidance in the use of GenAI is provided by the University of Sheffield Libraries (2025). The guide’s developers challenge users to reach their own conclusions about GenAI, while refusing to sugarcoat the technology’s downsides as much as emphasise its potential benefits. In its mature consideration of GenAI’s strengths and weaknesses, it is the perfect counter to a crisis narrative. Another approach with much potential, is the provision by Maastricht University of a library of GenAI prompts (Kaminska and Maastricht University Library, 2024). It treats a knowledge of prompt engineering (designing a context-rich prompt to maximise chances of eliciting a desired response) as a prerequisite to engaging with GenAI, and encourages users to develop their competence in this regard as they use these tools.

Recommendations for AI training development

• Consciously critique and dismantle the GenAI crisis narrative in whatever training you develop.
• Prepare users to navigate the current information landscape in a holistic manner, and to avoid overreliance on GenAI tools alone.
• Avoid simply teaching people how to use GenAI tools, and encourage them to critically engage with their use.
• Accept that the GenAI bubble may burst, and prepare to revise or pivot what you offer in response.
• Do explore ways to innovate (à la Maastricht University) but remember to make your training accessible to a wide range of digital competencies.
• Adherence to such principles is vital for not only designing effective training schemes, but to develop teaching that is worthwhile and impactful.

A selected subject bibliography has also been compiled on this topic.

References

Badke, W. (2025) ‘The Great AI Rubbish Heap’, Computers in Libraries (May 2025). Available at: http://www.infotoday.com/cilmag/may25/Badke–The-Great-AI-Rubbish-Heap.shtml (Accessed: 23 November 2025).

Baer, A. (2025) ‘Investigating the “feeling rules” of generative AI and imagining alternative futures’, In The Library With The Lead Pipe, 16 July. Available at: https://www.inthelibrarywiththeleadpipe.org/2025/ai-feeling-rules/ (Accessed: 23 November 2025).

Buitrago-Ciro, J. et al. (2025) ‘Bridging the AI gap: Comparative analysis of AI integration, education, and outreach in academic libraries’, IFLA Journal, 51(3), pp. 682–702. Available at: https://doi.org/10.1177/03400352251325274

Burgess, J. (2021) ‘Towards an Ethics of Unity: An Ecological Approach to Overcoming Dispossession in Academic Libraries’, Canadian Journal of Academic Librarianship, 7, pp. 1–15. Available at: https://doi.org/10.33137/cjalrcbu.v7.36443

Chaudhuri, J. and Terrones, L. (2025) ‘Reshaping Academic Library Information Literacy Programs in the Advent of ChatGPT and Other Generative AI Technologies’, Internet Reference Services Quarterly, 29(1), pp. 1–25. Available at: https://doi.org/10.1080/10875301.2024.2400132

Crawford, J. et al. (2024) ‘When artificial intelligence substitutes humans in higher education: the cost of loneliness, student success, and retention’, Studies in Higher Education, 49(5), pp. 883–897. Available at: https://doi.org/10.1080/03075079.2024.2326956

Doctorow, C. (2026) ‘AI companies will fail. We can salvage something from the wreckage’, The Guardian, 18 January. Available at: https://www.theguardian.com/us-news/ng-interactive/2026/jan/18/tech-ai-bubble-burst-reverse-centaur

Frank, S. (2023) ‘The fall of creativity: A librarian’s role in the world of AI’, College & Research Libraries News, 84(11), pp. 439–441. Available at: https://doi.org/10.5860/crln.84.11.439

Hauck, M., Moore, E. and Wright, C. (2025) A framework for the learning and teaching of critical AI literacy skills. Available at: https://www.open.ac.uk/blogs/learning-design/wp-content/uploads/2025/01/OU-Critical-AI-Literacy-framework-2025-external-sharing.pdf (Accessed: 23 November 2025).

Kaminska, M. and Maastricht University Library (2024) AI Prompt Library. Available at: https://library.maastrichtuniversity.nl/apps-tools/ai-prompt-library/ (Accessed: 23 November 2025).

Mills, M. (2025) ‘Replaced by bots? No, AI needs academics more than we need it’ Times Higher Education, 28 August. Available at: https://www.timeshighereducation.com/depth/replaced-bots-no-ai-needs-academics-more-we-need-it (Accessed: 28 September 2025).

Springmier, K. (2025) ‘An Exploration of Faculty and Student Perceptions of Generative AI’, Library Trends, 73(4), pp. 426–442. Available at: https://doi.org/10.1353/lib.2025.a968490

University of Sheffield Library (2025) Critical GenAI Literacy, Articulate Rise Tutorial. Available at: https://rise.articulate.com/share/ckJWIit1oGFbVpNkjQwGaA4_lrFSEawS#/ (Accessed: 23 November 2025).