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A review of AI teaching and learning from 2000 to 2020

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Abstract

In recent years, with the popularity of AI technologies in our everyday life, researchers have begun to discuss an emerging term “AI literacy”. However, there is a lack of review to understand how AI teaching and learning (AITL) research looks like over the past two decades to provide the research basis for AI literacy education. To summarize the empirical findings from the literature, this systematic literature review conducts a thematic and content analysis of 49 publications from 2000 to 2020 to pave the way for recent AI literacy education. The related pedagogical models, teaching tools and challenges identified help set the stage for today’s AI literacy. The results show that AITL focused more on computer science education at the university level before 2021. Teaching AI had not become popular in K-12 classrooms at that time due to a lack of age-appropriate teaching tools for scaffolding support. However, the pedagogies learnt from the review are valuable for educators to reflect how they should develop students’ AI literacy today. Educators have adopted collaborative project-based learning approaches, featuring activities like software development, problem-solving, tinkering with robots, and using game elements. However, most of the activities require programming prerequisites and are not ready to scaffold students’ AI understandings. With suitable teaching tools and pedagogical support in recent years, teaching AI shifts from technology-oriented to interdisciplinary design. Moreover, global initiatives have started to include AI literacy in the latest educational standards and strategic initiatives. These findings provide a research foundation to inform educators and researchers the growth of AI literacy education that can help them to design pedagogical strategies and curricula that use suitable technologies to better prepare students to become responsible educated citizens for today’s growing AI economy.

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Data availability

The authors declare that the data supporting the findings of this study are available within the article and its supplementary information files.

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Authors and Affiliations

  1. Faculty of Education, The University of Hong Kong, Hong Kong, China

    Davy Tsz Kit Ng, Xiao Hu & Samuel Kai Wah Chu

  2. Faculty of Education, Nanyang Technological University, Singapore, Singapore

    Min Lee & Roy Jun Yi Tan

  3. School of Information Sciences, University of Illinois Urbana-Champaign, Champaign, IL, USA

    J. Stephen Downie

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Appendices

Appendix 1

Table

Table 5 Lists of selected articles from Web of Science and Scopus databases (2000–2020)
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Appendix 2

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Table 6 Literature summary of AITL studies
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Appendix 3

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Table 7 Teaching tools
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Appendix 4

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Table 8 Timeline of AI teaching tools
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Ng, D.T.K., Lee, M., Tan, R.J.Y. et al. A review of AI teaching and learning from 2000 to 2020. Educ Inf Technol 28, 8445–8501 (2023). https://doi.org/10.1007/s10639-022-11491-w

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