Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building
Open accessAbstract
Artificial intelligence (AI) applications have become prevalent in all walks of life, but efforts to promote AI literacy to educated citizens from diverse study backgrounds are limited. There is a research gap in refocussing AI literacy courses from combining conceptual learning with mathematical formulae and programming codes to emphasising conceptual building from the beginning. This study fills the knowledge gap by evaluating AI literacy courses that aim to build conceptual understanding among university students from diverse study backgrounds. Eighty-two volunteers completed two AI literacy courses comprising 7 h of machine learning and 9 h of deep learning. The results of their pre- and post-course conceptual tests, surveys and self-reflective writing tasks indicated that the courses successfully equipped the participants with a conceptual understanding of AI. The participants felt empowered by the significant gains in their literacy and conceptual understanding of AI. The AI literacy courses successfully lower the barrier to entry for AI literacy and address a public need. The courses will be expanded to incorporate the development of AI applications together with discussion of ethical issues regarding the wide use of AI in society. This study can be used to guide future research on fostering AI literacy among educated citizens from diverse study backgrounds.
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Educators with higher AI literacy are more likely to appreciate the potential enhancements that AI can bring to educational environments, thus fostering a more receptive attitude towards accepting these technologies.
“Educators with higher AI literacy are more likely to appreciate the potential enhancements that AI can bring to educational environments, thus fostering a more receptive attitude towards accepting these technologies [48].”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [48]
AI literacy is a key component in equipping teachers with the necessary skills and knowledge to integrate GenAI effectively into their teaching.
“This suggests that AI literacy is a key component in equipping teachers with the necessary skills and knowledge to integrate GenAI effectively into their teaching [45,48].”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [48]
AI literacy improves understanding of GenAI's advantages and functions, which encourages a favorable view of its acceptance and use in educational contexts.
“AI literacy improves understanding of GenAI's advantages and functions, which encourages a favorable view of its acceptance and use in educational contexts [3,47,48,64].”
From Modeling teachers’ acceptance of generative artificial intelligence use in higher education: the role of AI literacy, intelligent TPACK, and perceived trust (2024) · cites this paper as [48]
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- Authors
- Kong, S.-C., Cheung, W. M.-Y., & Zhang, G.
- Affiliations
- Education University of Hong Kong (HK)
- Corresponding author
- Kong, Siu-Cheung
- Venue
- Computers in Human Behavior Reports
- Publisher
- Elsevier BV
- Published
- 2022-08
- Volume
- 7
- Pages
- 100223
- ISSN
- 2451-9588 (ISSN-L)
- OpenAlex
- W4288988278
- Cited by
- 135
- References
- 50
- Citations by year
- 2026: 45 · 2025: 46 · 2024: 29 · 2023: 13 · 2022: 2
- FWCI
- 22.27
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Licence
- Locations
- Computers in Human Behavior Reports (publishedVersion, cc-by-nc-nd)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Funders
- University Grants Committee (EdUHK CB302)
- Research Grants Council, University Grants Committee
- Li Ka Shing Foundation
- Topics
- Physical Sciences › Computer Science › Information Systems › Digital literacy in education
- Physical Sciences › Computer Science › Information Systems › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Computer Science Applications › Teaching and Learning Programming
- Keywords
- AI literacy, conceptual understanding, university students, non-STEM students, empowerment, machine learning education, deep learning in education, AI ethics, public understanding of AI
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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