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New Voices RGU Student Series 2026 – Brigid Leaf

Category: RGU Student Series 2026

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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.

Brigid Leaf is currently pursuing an MSc in Information and Library Studies at Robert Gordon University. Originally from the United States, she’s now based in London. Her hobbies include all things books, reading, and travel.

How to be the ‘human in the loop’: Generative AI literacy training for the corporate workplace

From strategy slide decks to software code, GenAI tools like ChatGPT continue to change how corporate professionals work. Effective and responsible use requires GenAI literacy that integrates critical thinking, ethics, and judgment with technical proficiency.

AI Generated image of a woman sitting at a computer.

As GenAI tools were initially rolled out for use at work, employee training often focused on prompting basics and general reminders to be the ‘human in the loop’. ‘Human in the loop’ is commonly used to mean incorporating human judgment, expertise, and responsibility when using complex systems like GenAI (Mollick, 2024). However, without additional guidance, what that should look like in practice is ambiguous, and individuals and companies may overestimate their GenAI competencies (Liu et al., 2025). Busy professionals often lack the time, mindset, and structured approaches to critically engage with GenAI outputs and may use the information without fully assessing it. (This includes the phenomenon of low-effort AI-generated work that is now popularly referred to as AI workslop.) Information professionals have the expertise to help bridge this gap by designing training programmes that provide Gen AI literacy frameworks with evidence-based guidance and examples.

What is GenAI Literacy?

While there is no single definition of GenAI literacy, published literature consistently reflects that it is multi-dimensional and includes ethical considerations. Examples include the dimensions of Cox’s (2024) proposed responsible generative AI literacy and Liu, Zhang and Wei’s (2025) workplace-focused GAIL Generative Artificial Intelligence Literacy framework:

Cox (2024)

  • Theoretical knowledge
  • Ethical knowledge
  • Pragmatic knowledge
  • Reflective knowledge

Liu, Zhang and Wei (2025)

  • Basic technical proficiency
  • Prompt optimization
  • Quality evaluation
  • Innovative practice
  • Ethical and compliance awareness

GenAI literacy also requires applying aspects of related literacies, such as information literacy and digital literacy. GenAI literacy varies by context, resulting in ‘a need to define AI literacy for different professional groups’ (Cox, 2024, p. 94).

Critical thinking skills underpin several dimensions of GenAI literacy, particularly as it relates to prompting and evaluation of GenAI outputs. Research shows that workers have lower levels of critical thinking in GenAI tasks where they have high confidence in GenAI and/or low self-confidence in their ability, have less domain knowledge, perceive the task as less important, or have limited time (Lee et al., 2025).

How can this inform corporate workplace training?

  • Continuous Learning. How GenAI is understood and used can vary significantly across individuals, even more so as the capabilities of GenAI tools continue to evolve. A combination of training modules, on-demand resources, in-person events, and peer-to-peer learning opportunities can support meeting each employee where they are in their GenAI literacy needs. Ongoing learning opportunities help to keep the principles and practical guidance front of mind and applied in day-to-day work.
  • Published peer-reviewed and practitioner resources can be leveraged to identify good practices on both content and modes of delivery. A published case study highlighted Deutsche Telekom’s ‘promptathon’ experiential peer-learning, which engaged participants in a fun team competition to solve challenges using GenAI and ethical AI guidelines (Kohn and Leffler-Krebs, 2025). Curated materials can be made available online for employees to access as on-demand resources, and could leverage the library sector LibGuide approach, for example. The University of Maryland (2025) ‘Artificial Intelligence (AI) and Information Literacy’ LibGuide is one example of curated on-demand text, video, and image resources.

 

  • Role-Specific Training. Targeted training content supports employee engagement by focusing on how they work with GenAI in their day-to-day work. Incorporating real-world business scenarios that are relevant to the industry and the company provides practical experience and fosters ethical considerations and discussion of appropriate uses of AI in different situations. Providing tangible GenAI literacy framework visuals can help employees connect the overarching principles with their day-to-day work with GenAI.

 

  • Critical Thinking. Training should also support maintaining foundational skills in information practices and critical thinking (Lee et al., 2025). Guidance on assessing GenAI output, with examples of quality criteria and validation methods, supports consistent information practices.
  • GenAI itself can be used to support critical thinking. Training examples and guidance on how to prompt GenAI to have it provide input and critiques can support and improve the critical thinking of the human in the loop.
  • As GenAI tool capabilities continue to expand at the rapid pace we’ve now become accustomed to, GenAI literacy becomes even more important. Information professionals’ expertise can help organisations design the impactful GenAI literacy training that their humans in the loop need to use GenAI effectively and responsibly at work.

Want to learn more?

If you’d like to read more on this topic, please visit the related Padlet annotated bibliography, which provides a mix of peer-reviewed and practitioner resources.

AI Generated image of a group of colleagues sitting around a table in front of their computers.

References

Canva AI (2025) AI hologram coworker [Digital art]. Created for Brigid Leaf, 29 November.

Canva AI (2025) Group of employees [Digital art]. Created for Brigid Leaf, 30 November.

Cox, A. (2024) ‘Algorithmic Literacy, AI Literacy and Responsible Generative AI Literacy’, Journal of Web Librarianship, 18(3), pp. 93–110. Available at: https://doi.org/10.1080/19322909.2024.2395341.

Kohn, S. and Leffler-Krebs, S. (2025) ‘Building AI Literacy through Promptathons: The Deutsche Telekom Case’, in Proceedings of ISPIM Conferences, 2025. Available at: https://doi.org/10.5281/ZENODO.15708222.

Lee, H.-P. (Hank) et al. (2025) ‘The Impact of Generative AI on Critical Thinking: Self-Reported Reductions in Cognitive Effort and Confidence Effects From a Survey of Knowledge Workers’, in Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems. New York, NY, USA: Association for Computing Machinery (CHI ’25), pp. 1–22. Available at: https://doi.org/10.1145/3706598.3713778.

Liu, X., Zhang, L. and Wei, X. (2025) ‘Generative Artificial Intelligence Literacy: Scale Development and Its Effect on Job Performance’, Behavioral Sciences, 15(6), p. 811. Available at: https://doi.org/10.3390/bs15060811.

Mollick, E. (2024) Co-intelligence: living and working with AI. London: WH Allen.

University of Maryland (2025) Research Guides: Artificial Intelligence (AI) and Information Literacy: Home. Available at: https://lib.guides.umd.edu/AI (Accessed: 28 November 2025).

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