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This annotated bibliography presents ten resources to inform training in Generative AI ('GenAI') literacy and critical thinking in the corporate workplace context. The sources examine the GenAI information practices of corporate knowledge workers, situate them within relevant literacy and competency frameworks, and identify practical implications. The bibliography includes peer-reviewed qualitative and quantitative research articles and conference papers,  a grey literature working paper,  public scholarship for non-academic audiences, and practical framework and LibGuide resources. All sources were published since 2023, following the November 2022 initial public release of ChatGPT that marked the start of widespread use of GenAI.  Together, these sources highlight the needs, challenges, and opportunities of GenAI users in the workplace, and how applying relevant literacy frameworks can inform GenAI literacy training that supports critical thinking and responsible use.

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

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Sarkar, A. (2025) How to stop AI from killing your critical thinking. TEDAI Vienna. Available at: https://www.ted.com/talks/advait_sarkar_how_to_stop_ai_from_killing_your_critical_thinking  (Accessed: 28 November 2025).

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Journal Articles & Conference Papers

Cox, A. (2024) ‘Algorithmic Literacy, AI Literacy and Responsible Generative AI Literacy’

Kohn, S. and Leffler-Krebs, S. (2025) ‘Building AI Literacy through Promptathons: The Deutsche Telekom Case’

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’

Liu, X., Zhang, L. and Wei, X. (2025) ‘Generative Artificial Intelligence Literacy: Scale Development and Its Effect on Job Performance’

Passlack, N., Hammerschmidt, T. and Posegga, O. (2025) ‘With Great Power Comes Great Responsibility: What Shapes AI Literacy for Responsible Interactions of Knowledge Workers With AI?’

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Calvino, F., Reijerink, J. and Samek, L. (2025) 'The effects of generative AI on productivity, innovation and entrepreneurship'

Dakan, R. and Feller, J. (2025) Framework for AI Fluency (Practical Summary Document)

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

Sarkar, A. (2025) How to stop AI from killing your critical thinking

University of Maryland (2025) Research Guides: Artificial Intelligence (AI) and Information Literacy: Home.

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This is a practitioner-focused non-fiction book, accessible to a general audience but grounded in academic research with endnotes. Two chapters are particularly relevant to the corporate workplace: Chapter 3 'Four Rules For Co-Intelligence' and Chapter 6 'AI As A  Coworker'.

The ‘jagged frontier’ is described as the uneven and unpredictable performance of GenAI in different types of tasks, requiring users to understand the abilities, nuances, and limitations of GenAI tools and output. Anecdotes and tangible examples are provided of using GenAI as a cognitive partner to humans in work-related tasks.

The author is a professor at The Wharton School of Business, University of Pennsylvania (United States) and Co-Director of the Generative AI Labs at Wharton. He has 22 indexed publications in Web of Science, cited 3,797 times. Google Scholar has ~16,000 citations, reflecting impact beyond peer-reviewed journal publications, including this book. He publishes a research-based AI newsletter ‘One Useful Thing’.

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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.

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The comprehensive literature review outlines AI literacy definitions, many of which pre-date GenAI, and highlights that defining the scope of AI and GenAI literacy is challenging and an evolving concept. It notes that AI literacies are focused on ‘users’ of AI (as distinct from designing AI) and need to be defined for different professional groups.

Building on existing literature, this article suggests the concept of ‘responsible GenAI literacy’ that is made up of four types of knowledge:

Theoretical knowledge of GenAI

Ethical knowledge related to GenAI

Pragmatic knowledge to select and use GenAI appropriately

Reflective knowledge for awareness of the user’s engagement with GenAI

Andrew Cox is a professor at the University of Sheffield Information School, with 131 indexed publications and 10 non-indexed documents in Web of Science cited 2,592 times. Journal of Web Librarianship is an international, peer-reviewed journal covering web-based ILS research and practice.

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Passlack, N., Hammerschmidt, T. and Posegga, O. (2025) ‘With Great Power Comes Great Responsibility: What Shapes AI Literacy for Responsible Interactions of Knowledge Workers With AI?’, Information Systems Frontiers. Available at: https://doi.org/10.1007/s10796-025-10648-5.

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(Passlack, Hammerschmidt and Posegga, 2025)

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The article argues that multiple literacies are required for knowledge workers to act responsibly when using AI, and that AI literacy and ethical literacy are interdependent and required for responsible human-AI interaction. The theoretical literature review and the qualitative interview research design were clearly outlined, including limitations and areas for further research. The research design consisted of 28 semi-structured interviews across 5 groups of knowledge workers in Germany. Four key findings and related recommendations for organisational strategies are presented, including considerations for ongoing, targeted, and role-specific training and learning.

The authors are affiliated with the University of Bamberg (Germany). Lead author Nina Passlack is a doctoral candidate at the University of Bamberg and has seven indexed publications in Web of Science.  Information Systems Frontiers is a peer-reviewed journal focusing on interdisciplinary approaches in Information Systems and Information Technology from academic and industry perspectives.

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Calvino, F., Reijerink, J. and Samek, L. (2025) ‘The effects of generative AI on productivity, innovation and entrepreneurship’, OECD Artificial Intelligence Papers, No. 39, OECD Publishing, Paris. Available at: https://doi.org/10.1787/b21df222-en.

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This OECD working paper is a literature review of experimental research on GenAI in business. It highlights that impact appears highly dependent on individual workers’ levels of expertise and trust in GenAI; risks include users not critically evaluating GenAI outputs, and ‘metacognitive laziness’ by overrelying on GenAI. It emphasises the importance of human expertise and oversight, and of companies providing training and guidelines on effective GenAI use and cognitive skills.

Areas noted for future study include user understanding of GenAI capabilities and limitations and how workers address incorrect AI-generated information.

The authors are economists at the Organisation for Economic Co-operation and Development, a major public policy institution. Working Papers are not OECD official views; working papers generally are not peer-reviewed. Lead author Flavio Calvino has seven indexed publications on Web of Science cited 203 times; Google Scholar has 43 publications and 1903 citations, reflecting broader impact consistent with OECD grey literature.

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This TedTalk is from the TEDAI Vienna Artificial Intelligence conference. It provides a high-level overview of research (five papers cited) on GenAI impact on knowledge workers’ critical thinking. In the research, knowledge workers reported less critical thinking effort when working with GenAI, particularly when they had lower confidence in themselves. Working with GenAI requires metacognitive reasoning, including to evaluate output, which can be negatively impacted when GenAI intermediates engagement with source materials.

It offers forward-looking approaches on GenAI as a thought partner that supports metacognition and critical thinking, with a prototype from Microsoft’s Tools For Thought project. The insights focused on GenAI tools, but the ‘metacognition scaffolding’ concept is generalisable to training and guidance on critical thinking in GenAI information practices.

Advait Sarkar is affiliated with Microsoft Research (United Kingdom) and University of Cambridge (United Kingdom). He has 72 indexed documents in Web of Science cited 661 times.

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Advait Sarkar: How to stop AI from killing your critical thinking

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Chatbots might help you get work done faster - but at what cost? When we outsource our reasoning to artificial intelligence, we reduce ourselves to "middle managers for our own thoughts," says AI and design researcher Advait Sarkar.

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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.

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The paper models critical thinking in GenAI use, analysing 936 examples from 319 knowledge workers. The Bloom’s Taxonomy definition of critical thinking is applied with a focus on finding and evaluating information. Critical thinking motivators and inhibitors are summarised with corresponding interventions. Although the insights focus more on GenAI tool design, they are generalisable to training.

The literature review, survey design, and regression modelling are clearly outlined. Noted limitations include the sample skewed to  fluent English speakers and younger, frequent GenAI users.

Hao-Ping Lee is a PhD student at Carnegie Mellon University (United States), with 26 indexed documents in Web of Science* cited 275 times. The six co-authors are affiliated with Microsoft Research (United Kingdom) where Lee interned in 2024. CHI is the Computer-Human Interaction Special Interest Group of the ACM scientific organisation. Its conference is highly regarded; submitted papers are accepted based on peer review by subcommittee members and external reviewers.

* There are two Web of Science entries (Hao-Ping Lee and Lee, Hao-Ping (Hank)); combined records reflected above, validated via web results.

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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.

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The article frames a five-dimensional model of Generative AI Literacy (GAIL) in the workplace, empirical analysis of GAIL impact on job performance, and practical recommendations. Recommendations include context-specific scenario training; ethical guidelines; emphasising the integration of multiple dimensions;  and leader communication.

The literature review, GAIL framework, and methodology are clearly outlined. GAIL dimensions are: technical, prompting, content evaluation, innovation, and ethics/compliance. The 2024 empirical study sample was of 344 public sector employees in mainland China. Limitations and areas for future research were noted, including: China’s culture and regulatory environment may limit ability to generalise internationally; potential negative consequences (e.g., over-reliance on AI; impact on critical thinking) were not explored.

The authors are affiliated with Renmin University of China. Lead author Xin Liu is a Professor at the School of Public Administration and Policy, with four indexed publications in Web of Science*, cited five times (all published 2025). Behavioral Sciences is an international, peer-reviewed, open access journal.

* Multiple records are associated with this name and institution; the additional records not linked to this paper’s author ORCID are not included above, but this could indicate this author may have additional publications.

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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.

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This case study shares Deutsche Telekom’s ‘promptathon’ format for employee upskilling in GenAI literacy, complementing formal training. Promptathons are hands-on team competitions to solve a challenge using GenAI. From September 2023 to early 2025, ~3,500 employees attended ~200 promptathons;  promptathons have been scaled to be a regular offering.  Design approach points that organisations can consider include: alignment to learning objectives and ethical practices; use cases aligned to real work contexts; communicating and applying AI guidelines and ethics; ‘tone from the top’ and recognition from leadership.

Stefan Kohn is affiliated with Hochschule Darmstadt (Darmstadt University of Applied Sciences, Germany), with 11 indexed and three non-indexed publications in Web of Science, cited 116 times. Sindy Leffler-Krebs is employed by Deutsche Telekom (Germany) in Compliance. The International Society for Professional Innovation Management (ISPIM) Innovation Conference accepts outline submissions based on a double-blind evaluation by a panel with academic and industry expertise.

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Dakan, R. and Feller, J. (2025) Framework for AI Fluency (Practical Summary Document), version 1.1. Available at: https://ringling.libguides.com/ai/framework (Accessed: 28 November 2025).

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The AI Fluency Framework describes ‘The 4Ds’, interconnected competencies for GenAI practitioners:

Delegation: deciding whether and how to engage with GenAI

Description: effectively prompting useful GenAI behaviours and outputs

Discernment: accurately assessing GenAI behaviours and outputs

Diligence: taking responsibility for GenAI work

Although the framework is aimed at higher education, it is generalisable to other sectors.

Open educational resources based on this Framework were developed by Dakan, Feller, and Anthropic (a generative AI public benefit corporation), with support from the Higher Education Authority Ireland, and made available under a Creative Commons license.

Rick Dakan is a professor at Ringling College of Art and Design (United States). There are publications on Web of Science or Scopus; Google Scholar provides an indicative 26 results but no author profile. Joseph Feller is a professor at University College Cork (Ireland), with 47 indexed publications in Web of Science cited 919 times.

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(Video copyright 2025 Rick Dakan, Joseph Feller, and Anthropic PBC. Released under the CC BY-NC-SA license.)

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AI Fluency: Framework & Foundations Course Trailer

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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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This AI and Information Literacy LibGuide is maintained by the University of Maryland (United States). LibGuides are curated content and resources managed by librarians; companies could consider a similar approach to provide  guidance to employees curated by information professionals. This LibGuide includes a section on assessing AI content, with pages on  ‘Fact-checking AI with Lateral Reading’ and ‘Beyond fact-checking’ critical thinking that provide guidance and examples in text, graphic, and video. ‘Lateral Reading’ is a method to evaluate GenAI responses by determining whether credible non-AI sources can confirm the information. An infographic summarises how to do this in practice.

The LibGuide was developed by the university’s Teaching and Learning Transformation Center in collaboration with The Institute for Trustworthy AI in Law & Society, a partnership with other American universities and private sector organisations, and funded by an award from the National Science Foundation and National Institute of Standard and Technology.

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Image generated by B. Leaf  in Canva AI to illustrate "hackathon/promptathon group of employees"

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Image generated by B. Leaf  in Canva AI to illustrate "generative ai as a coworker using hologram metaphor"

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Image generated by B. Leaf  in Canva AI to illustrate "generative ai in the corporate workplace"

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Image generated by B. Leaf  in Canva AI to illustrate "librarian training university students on ai information literacy"

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Image generated by B. Leaf  in Google Nano Banana Pro AI to illustrate "generative ai in the office"

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Image generated by B. Leaf  in Google Nano Banana Pro AI to illustrate "a visual metaphor to convey 'knowledge'"