AI literacy for users – A comprehensive review and future research directions of learning methods, components, and effects
Open accessAbstract
The rapid advancement of artificial intelligence (AI) has brought transformative changes to various aspects of human life, leading to an exponential increase in the number of AI users. The broad access and usage of AI enable immense benefits but also give rise to significant challenges. One way for AI users to address these challenges is to develop AI literacy, referring to human proficiency in different subject areas of AI that enable purposeful, efficient, and ethical usage of AI technologies. This study aims to comprehensively understand and structure the research on AI literacy for AI users through a systematic, scoping literature review. Therefore, we synthesize the literature, provide a conceptual framework, and develop a research agenda. Our review paper holistically assesses the fragmented AI literacy research landscape (68 papers) while critically examining its specificity to different user groups and its distinction from other technology literacies, exposing that research efforts are partly not well integrated. We organize our findings in an overarching conceptual framework structured along the learning methods leading to, the components constituting, and the effects stemming from AI literacy. Our research agenda – oriented along the developed conceptual framework – sheds light on the most promising research opportunities to prepare AI users for an AI-powered future of work and society.
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Cultures with higher technological adaptation may foster greater familiarity and comfort with AI, while economically disadvantaged regions may exhibit lower AI literacy due to limited access to technology and resources.
“For instance, cultures with higher levels of technological adaptation may foster greater familiarity and comfort with AI, while economically disadvantaged regions may exhibit lower AI literacy due to limited access to technology and resources (Pinski & Benlian, 2024).”
From Measuring undergraduate students’ reliance on generative AI during problem-solving: Scale development and validation. (2025) · cites this paper as Pinski & Benlian, 2024
The field lacks a structured yet adaptable framework to translate AI literacy concepts into educational practice.
“Absence of an actionable framework for implementation: The field lacks a structured yet adaptable framework to translate AIL concepts into educational practice.”
From The AI literacy heptagon: A structured approach to AI literacy in higher education (2026) · cites this paper as Pinski and Benlian, 2024
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References 103 · Cited by 243
- Authors
- Pinski, M., & Benlian, A.
- Affiliations
- Technische Universität Darmstadt (DE)
- Corresponding author
- Pinski, Marc
- Venue
- Computers in Human Behavior: Artificial Humans
- Publisher
- Elsevier BV
- Published
- 2024-01
- Volume
- 2
- Issue
- 1
- Pages
- 100062
- ISSN
- 2949-8821 (ISSN-L)
- OpenAlex
- W4392752316
- Semantic Scholar
- 52a095d49391bc299a26c3bd34d2489e633f9dad
- Cited by
- 243
- References
- 103
- Citations by year
- 2026: 132 · 2025: 96 · 2024: 15
- FWCI
- 91.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers in Human Behavior Artificial Humans (publishedVersion, cc-by)
- RePEc: Research Papers in Economics (submittedVersion)
- Topics
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Social Sciences › Library and Information Sciences › Library Science and Information Literacy
- Keywords
- AI literacy, AI users, learning methods, technological literacy, conceptual framework, research agenda, future of work, ethical AI use
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