AI Literacy at Work: Implications for Human Resource Management

Background and Objectives of the Special Issue

Artificial intelligence (AI) is being rapidly adopted across organisations, driven by promises of effectiveness and efficiency, yet accompanied by well-documented risks such as algorithmic bias and opacity (Jarrahi et al., 2021, 2025). As competitive pressures compel AI adoption regardless, AI literacy, defined as the knowledge, skills, and attitudes that allow individuals (i.e., employees and HR-professionals) to engage effectively and critically with AI technologies (Benlian & Pinski, 2025; Long & Magerko, 2020; Renkema et al., 2025), becomes crucial. This urgency is further underscored by the EU’s AI Act, which includes the requirement for a sufficient level of AI literacy in organisations (Paolini et al., 2025).

However, AI literacy research stands at a critical point. While widely acknowledged as necessary, it remains insufficiently understood in organisational and HRM contexts. Extant research on AI literacy has produced conceptual frameworks and measurement tools (Soto-Sanfiel et al., 2025), but this work is predominantly embedded in educational research and is focused on individual learners (Almatrafi et al., 2024) - leaving the organisational perspective largely under-researched and under-theorised.

This SI addresses that gap directly. We place AI literacy at the core of organisational development, learning and consequences for HRM, and invite contributors across varied disciplines and methodologies to advance our understanding of what AI literacy means as an organisational phenomenon, how it is facilitated by HRM, and the role HR managers play in AI-literate organisations. In so doing, we aim to contribute to theory and practice at a moment when the need for actionable insights into AI literacy could not be greater.

Theoretical contributions

This SI aims to advance theoretical understanding of AI literacy by moving beyond two common perspectives in the current literature: (i) uncritically optimistic framings that treat AI literacy as a solution to unlock AI’s strategic potential and neutralise AI’s risks and challenges, and (ii) dismissive pessimistic framings that treat AI literacy as little more than a legitimising instrument for AI adoption. Rather, we advocate critically engaged, practically relevant, and empirically grounded perspectives that position AI literacy between these two extremes, as a significant challenge for organisations and HRM, generating knowledge that genuinely benefits organisations and HR professionals.

We welcome theoretical perspectives that conceptualise AI literacy as a broader organisational capability than a set of individual competencies, for example, as emergent, relational, or organisationally constituted phenomenon. Our scope is thus organisational, as we focus on AI literacy within organisations and its implications for HRM, rather than on AI literacy at the national, legal policy or individual employee levels. More specifically, to make a substantial theoretical contribution, we invite manuscripts that view AI literacy across organisational levels, adopting a multilevel organisational perspective (Renkema et al., 2017).

Potential themes

The following themes and questions are illustrative rather than exhaustive:

Organizational level

• What are the implications of AI literacy for the core HRM functions, systems, policies and practices, including recruitment and selection, learning and development, performance management, and employee well-being?
• What does organisational AI literacy mean? How can HRM activities be designed, implemented, and governed for organizations to become AI literate?
• Through which emergence mechanisms does individual AI literacy compose or compile into a collective capability, and can HRM contribute to this?
• How does organisational AI literacy relate to adjacent constructs such as organisational AI readiness, and what are its antecedents and outcomes?
• How do organisations compete for AI talent in the labour market, and what are the inequality implications inter- and intra-organisationally?

Team level

• How can organisations (re)shape or create teams to enable, develop and foster AI literacy?
• How can HRM and HRD practices support the development of AI-literate teams, including through workforce planning, staffing, job design, training and allocation of AI-related responsibilities?
• What levels and combinations of AI literacy are needed across different teams and team roles, and how can HRM practices (e.g., recruitment, teamwork, performance management) help teams build and sustain these capabilities?

Individual level

• How does AI literacy manifest in and/or transform everyday work practices? What does it mean for employees (or managers) to exercise AI literacy in a specific context?
• What technical, ethical and practical dimensions of AI literacy do job seekers need to navigate in AI-mediated recruitment, and how does such literacy shape fairness perceptions and self-presentation? How does AI literacy influence employees’ AI overreliance?
• What does AI literacy of HR professionals mean, and how does it influence the adoption of AI tools?  

 

Given the current state of the literature on AI literacy, which is relatively mature in systematic reviews, we strongly encourage empirical work and welcome varied methodological approaches – qualitative, quantitative, and mixed-methods. We are open to innovative and non-traditional research designs and approaches as long as they respect the standards of rigor and relevance (e.g., interventions, action research, ethnographies, and longitudinal studies), including studies that conceptualise AI literacy as an organisational, dynamic, context-dependent phenomenon rather than a static individual attribute. We encourage all authors to aim for societal and practical impact and ethical conduct of science (e.g., Lamers, 2026). We also welcome high-quality conceptual contributions that advance extant theoretical understanding. The issue will not include (systematic) literature review papers. We further refer to the standard inclusion criteria of IJHRM for consideration at the journal, such as an explicit link to HRM theory and practice.

We welcome submissions from diverse sectors, organisation types (from small to large; public and private) and geographical contexts, including studies from the Majority World and from contexts that remain underrepresented in AI and HRM research.

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References

Almatrafi, O., Johri, A., & Lee, H. (2024). A systematic review of AI literacy conceptualization, constructs, and implementation and assessment efforts (2019–2023). Computers and Education Open, 6.

Benlian, A., & Pinski, M. (2025). The AI literacy development canvas: Assessing and building AI literacy in organizations. Business Horizons.

Jarrahi, M. H., Newlands, G., Lee, M. K., Wolf, C. T., Kinder, E., & Sutherland, W. (2021). Algorithmic management in a work context. Big Data & Society, 8(2), 1-14.

Jarrahi, M. H., Karami, A., Conway, P., Memariani, A., & Lutz, C. (2025). Navigating the muddy waters of bias in artificial intelligence research: Understanding divergent meanings and conceptions. Technology in Society, 103127.

Lamers, L. (2026). Ethical impact in HRM: Advancing knowledge production with mode E. Human Resource Management Journal, 36(1), 79-92.

Long, D., & Magerko, B. (2020, April). What is AI literacy? Competencies and design considerations. In Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems (pp. 1-16).

Paolini e Silva, M., Tamò-Larrieux, A., & Ammann, O. (2025). AI literacy under the AI Act: Tracing the evolution of a weakened norm.

Renkema, M., Meijerink, J., & Bondarouk, T. (2017). Advancing multilevel thinking in human resource management research: Applications and guidelines. Human resource management review, 27(3), 397-415.

Renkema, M., Van den Boom-Muilenburg, E., Friso-van den Bos, I., Theelen, H., Wopereis, I., & Schildkamp, K., (2025). AI-GO! A Framework for AI Literacy in Education (AI-GO Framework). Utrecht: Npuls AI- en Data geletterdheid.

Soto-Sanfiel, M. T., Angulo-Brunet, A., & Lutz, C. (2025). The scale of artificial intelligence literacy for all (SAIL4ALL): assessing knowledge of artificial intelligence in all adult populations. Humanities and Social Sciences Communications, 12(1), 1-14.