AI Literacy - Towards Measuring Human Competency in Artificial Intelligence
Open accessAbstract
Artificial intelligence (AI) has gained significant traction in information systems (IS) research in recent years. While past studies have identified many effects of AI technology on human-AI collaborations, there is a paucity in IS literature on the competencies of humans that affect this relationship. In this study, we set out to develop a measurement instrument (scale) for general AI literacy, that is humans’ socio-technical competencies regarding AI. We conducted a systematic literature review followed by five expert interviews to define and conceptualize the construct of general AI literacy and to generate an initial set of items. Furthermore, we performed two rounds of card sorting with six and five judges and a pre-test study with 50 participants to evaluate the developed scale. The validated measurement instrument contains five dimensions and 13 items. We provide empirical support for the measurement model and conclude with future research directions.
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It was not possible to examine how respondents would have responded to other adapted questionnaires.
“For reasons of evaluation efficiency, it was not possible to examine how respondents would have responded to other adapted questionnaires (e.g., [23-25]).”
From Evaluating AI Courses: A Valid and Reliable Instrument for Assessing Artificial-Intelligence Learning through Comparative Self-Assessment (2023) · cites this paper as [23]
Pinski & Belian's instrument showed at least some evidence for content validity.
“Pinski & Belian’s instrument44 also showed at least some evidence for content validity, but it has been validated on a limited sample, requiring revalidation on a bigger sample in the future.”
From A systematic review of AI literacy scales (2024) · cites this paper as [44]
Pinski & Belian's instrument has been validated on a limited sample.
“Pinski & Belian’s instrument44 also showed at least some evidence for content validity, but it has been validated on a limited sample, requiring revalidation on a bigger sample in the future.”
From A systematic review of AI literacy scales (2024) · cites this paper as [44]
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References 35 · Cited by 94
Research works printed but not linked: 1
- Wiesche, M., Joseph, D., Thatcher, J., Gu, B., & Krcmar, H. (2020). "IT Workforce" in MIS Quarterly Research Curations (A. Bush & A. Rai, Eds.). http://misq.org/research-curations
- Authors
- Pinski, M., & Benlian, A.
- Affiliations
- Technische Universität Darmstadt (DE)
- Venue
- Proceedings of the Annual Hawaii International Conference on System Sciences
- Published
- 2023
- ISSN
- 1530-1605 (ISSN-L)
- 2572-6862
- OpenAlex
- W4391903960
- Cited by
- 94
- References
- 35
- Citations by year
- 2026: 34 · 2025: 44 · 2024: 15 · 2023: 1
- FWCI
- 24.14
- Type
- conference paper
- Language
- en
- Open access
- hybrid
- doi.org
- Licence
- cc-by-nc-nd
- Locations
- Hawaii International Conference on System Sciences (HICSS) (publishedVersion, cc-by-nc-nd)
- Journal of the Association for Information Systems (submittedVersion)
- ScholarSpace (University of Hawaii at Manoa) (submittedVersion, cc-by-nc-nd)
- TUbilio (Technical University of Darmstadt) (submittedVersion, cc-by-nc-nd)
- Topics
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Social Sciences › Safety Research › Ethics and Social Impacts of AI
- Keywords
- AI literacy, human-AI collaboration, scale development, construct definition, card sorting, scale validation
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