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Cited by 153Digital literacy in educationArtificial Intelligence in EducationTeaching and Learning Programming

The DOI matches its Crossref record; 51 of the 51 references it cites were found in a registry or an index.

Evaluating artificial intelligence literacy courses for fostering conceptual learning, literacy and empowerment in university students: Refocusing to conceptual building

Open access
Kong, Siu-Cheung, Cheung, William Man-Yin, Zhang, Guo·2022·Computers in Human Behavior Reports
DOI 10.1016/j.chbr.2022.100223
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Abstract

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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Crossref · 10.1016/j.chbr.2022.100223

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Image 1
CC-BY-NC-ND · From the PDF
Fig. 1 Distribution of course participants from diverse study backgrounds.
Fig. 1 Distribution of course participants from diverse study backgrounds.CC-BY-NC-ND · From the PDF
Step 1: Represent athletes as data points according to their normalised height and weight. Step 1: Represent athletes as data points according to their normalised height and weight.
Step 1: Represent athletes as data points according to their normalised height and weight. Step 1: Represent athletes as data points according to their normalised height and weight.CC-BY-NC-ND · From the PDF
Image 4
CC-BY-NC-ND · From the PDF
Step 2: Randomly select two hypothetical representatives and then assign all athletes to the nearest group.
Step 2: Randomly select two hypothetical representatives and then assign all athletes to the nearest group.CC-BY-NC-ND · From the PDF
Image 6
CC-BY-NC-ND · From the PDF
Image 7
CC-BY-NC-ND · From the PDF
Fig. 5 AI Empowerment Survey results before course 1 and after courses 1 and 2.
Fig. 5 AI Empowerment Survey results before course 1 and after courses 1 and 2.CC-BY-NC-ND · From the PDF

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Research works printed but not linked: 1

  1. Darling-Hammond, 2003, Session 11: Lessons for life: Learning and transfer, The Learning Classroom, 189

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Metadata

Authors
Kong, S.-C., Cheung, W. M.-Y., & Zhang, G.
Affiliations
Education University of Hong Kong (HK)
Corresponding author
Kong, Siu-Cheung
ORCID
Venue
Computers in Human Behavior Reports
Publisher
Elsevier BV
Published
2022-08
Volume
7
Pages
100223
ISSN
2451-9588 (ISSN-L)
DOI
10.1016/j.chbr.2022.100223
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
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
  • Social Sciences › Education › Digital literacy in education
  • Social Sciences › Education › 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

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