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

• Published in: Computers in human behavior reports 2022-08, Vol.7, p.100223, Article 100223
• Main Authors: Kong, Siu-Cheung, Cheung, William Man-Yin, Zhang, Guo
• Format: Article
• Language: English
• Subjects: Artificial intelligence; Concepts; Diverse study backgrounds; Empowerment; Literacy; University students
• ISSN: 2451-9588; 2451-9588
• DOI: 10.1016/j.chbr.2022.100223

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. •The study refocuses AI literacy courses to conceptual building without introducing mathematical formulae/program codes.•Eighty-two university students with diverse study backgrounds participated in the AI literacy courses.•The courses enabled students to achieve a growth in AI concepts, self-perception of AI literacy and AI empowerment.•The courses successfully lower the barrier to entry for AI literacy and address a public need.•This study can guide future research on fostering AI literacy among educated citizens from diverse study backgrounds.