Evaluation of an artificial intelligence literacy course for university students with diverse study backgrounds

By Siu Cheung Kong, W. M. Cheung, Guo Zhang. Computers and Education: Artificial Intelligence, 2021

311 citations recorded by the source corpus.

Abstract A few limited efforts have been made to promote artificial intelligence (AI) literacy for citizens. The objective of this study was to design, implement and evaluate an AI literacy course for university students. One of the study's research questions was whether university students from a variety of disciplines could develop a conceptual understanding of AI through a literacy course. We promoted this course to 4000 students and recruited 120 volunteer participants to attend and complete the 7-h course. The results of our pre-course and post-course surveys indicated that the participants made significant progress in understanding AI concepts, felt empowered to work with AI. These findings indicated that the participants of diverse study backgrounds, and of both genders, could understand the concepts of machine learning, supervised learning, regression, classification, unsupervised learning, and clustering. Prior knowledge of programming was not necessary for AI concepts development, and the flipped classroom learning approach enabled more flexible learning by the participants. In the future, this AI literacy course could be extended to include AI application projects and discussions of related ethical issues regarding the wider use of AI in society. We are planning to introduce this literacy course to senior secondary school students.

Subfields: Computer Science Applications, Education, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/4b67efd17cf42b36b599f1b62fdf66b512383e4a

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Computer Science Applications

1. Jiahong Su
2. Davy Tsz Kit Ng
3. S. K. Chu
4. Jac Ka Lok Leung
5. R. Ng
6. J. Stephen Downie
7. M. Lee
8. Xiao Hu

In Education

1. S. K. Chu
2. J. Stephen Downie
3. M. Lee
4. Xiao Hu
5. Davy Tsz Kit Ng
6. Roy Jun Yi Tan

Citations in the corpus

• A review of AI teaching and learning from 2000 to 2020 (2022, background): “In recent years, there has been renewed interest in introducing AI to K-12 students (Long & Magerko, 2020), and non-computer science university students (Kong et al., 2021).”
• MAILS - Meta AI Literacy Scale: Development and Testing of an AI Literacy Questionnaire Based on Well-Founded Competency Models and Psychological Change- and Meta-Competencies (2023, background): “This fits in with the general picture that Create AI is separate [13, 23, 29] and no part of AI Literacy.”
• Factors Influencing University Students’ Behavioral Intention to Use Generative Artificial Intelligence: Integrating the Theory of Planned Behavior and AI Literacy (2024, unknown): “This subsequently facilitates the recognition and acceptance of AI’s current status as an auxiliary tool that can address intricate challenges in the learning process (Kong et al., 2021).”
• Teachers’ AI digital competencies and twenty-first century skills in the post-pandemic world (2023, background): “…programmes for students from diverse educational backgrounds (e.g., radiology, architecture majors) to develop their foundational AI knowledge, and equip themselves with skills and mindset to solve authentic problems using AI applications (Kong et al., 2021; Ng et al., 2022a, 2022b, 2022c, 2022d).”