With Great Power Comes Great Responsibility: What Shapes AI Literacy for Responsible Interactions of Knowledge Workers With AI?
Citation. Passlack, N., Hammerschmidt, T., & Posegga, O. (2025). With great power comes great responsibility: What shapes AI literacy for responsible interactions of knowledge workers with AI? Information Systems Frontiers, 28, 11–46. https://doi.org/10.1007/s10796-025-10648-5
Summary. This qualitative study reframes AI literacy for knowledge workers as a condition for responsible human–AI interaction, not merely efficient tool use. It argues that responsible interaction requires a context-sensitive combination of technical, domain, social, ethical, and learning-related literacies, alongside personal dispositions and organizational conditions that support shared accountability. The paper introduces role-ability fit: responsibility assigned to a role should align with that role holder’s capacity to act responsibly and be held accountable.
Key claims
- Across 28 interviews with HR professionals, managers, AI designers/data scientists, educators/researchers, and AI-provider product/sales managers, participants described responsible AI interaction as depending on personal dispositions, human-centered literacy aspects, ethical literacy, and micro-, meso-, and macro-level influences on learning.
- Personal dispositions accounted for 58.16% of coded building-block references, versus knowledge (25.52%), skills (11.22%), capabilities (4.08%), and competencies (1.02%); participants emphasized continual learning, patience, moral values, and healthy skepticism rather than technical ability alone.
- Required literacy is use-case and role contingent: interviewees linked more complex AI systems to deeper technical understanding, but linked more usable systems to greater need for domain knowledge and communication skills.
- The authors argue that responsible interaction requires multi-literacy and shared responsibility, because unclear role boundaries can create a gap between the responsibilities assigned to workers and their ability to act responsibly.
- The paper proposes that targeted, role-specific AI-literacy development should assess the abilities required for distinct professional roles rather than deliver generic hard-skills training alone.
Evidence & limitations
- Peer-reviewed qualitative study based on 28 semi-structured interviews conducted in German from March 2023 to September 2024; participants were purposively selected across five knowledge-worker stakeholder groups and interviews were translated into English for reporting.
- The findings are propositions and participant accounts rather than causal estimates or validated measurement evidence; the authors explicitly do not claim an exhaustive component model of AI literacy.
- Public provenance verified through the Crossref record and Springer version-of-record PDF; the public PDF matches the ingested Drive PDF exactly by SHA-256.