Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review
There is an emerging interest in Artificial Intelligence (AI) teaching and learning in the K-12 setting. While some work has explored the educational content and resources used for this purpose, there is limited empirical evidence on the effectiveness of such AI education interventions. The primary...
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| Format: | Article |
| Language: | English |
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Elsevier
2023-01-01
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| Series: | Computers and Education: Artificial Intelligence |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2666920X23000243 |
| _version_ | 1827921398024110080 |
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| author | Saman Rizvi Jane Waite Sue Sentance |
| author_facet | Saman Rizvi Jane Waite Sue Sentance |
| author_sort | Saman Rizvi |
| collection | DOAJ |
| description | There is an emerging interest in Artificial Intelligence (AI) teaching and learning in the K-12 setting. While some work has explored the educational content and resources used for this purpose, there is limited empirical evidence on the effectiveness of such AI education interventions. The primary objective of the systematic literature review presented in this paper was to examine research with empirical evidence reporting learning outcomes for teaching and learning AI in K-12 between 2019 and 2022. Through a rigorous selection process, a total of 28 studies were included in the final analysis out of 8,175 papers identified from five research databases using specific search terms. A content analysis method was used to synthesise the data. This paper outlines the focus on learners' context, the extent of empirical support for the pedagogical approaches, and the theoretical coverage of AI topics included in the studies. The majority of studies reported an improvement in both cognitive and affective learning outcomes. The paper concludes by highlighting key areas where additional research is needed in the future as well as the challenges associated with them. Although the findings are based on limited empirical studies, they suggest that a more learner-centred approach, context-aware pedagogical practices, and consistent constructs to measure AI learning outcomes could benefit teaching and learning AI in K-12 schools. Further research is needed to build on these insights. |
| first_indexed | 2024-03-13T04:26:57Z |
| format | Article |
| id | doaj.art-7b4eac7d7b54494883a9bc898b35a658 |
| institution | Directory Open Access Journal |
| issn | 2666-920X |
| language | English |
| last_indexed | 2024-03-13T04:26:57Z |
| publishDate | 2023-01-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Computers and Education: Artificial Intelligence |
| spelling | doaj.art-7b4eac7d7b54494883a9bc898b35a6582023-06-20T04:21:19ZengElsevierComputers and Education: Artificial Intelligence2666-920X2023-01-014100145Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature reviewSaman Rizvi0Jane Waite1Sue Sentance2Raspberry Pi Foundation, Cambridge, UK; Raspberry Pi Computing Education Research Centre, Department of Computer Science and Technology, University of Cambridge, UKRaspberry Pi Foundation, Cambridge, UK; Raspberry Pi Computing Education Research Centre, Department of Computer Science and Technology, University of Cambridge, UKRaspberry Pi Computing Education Research Centre, Department of Computer Science and Technology, University of Cambridge, UK; Corresponding author. Department of Computer Science and Technology, University of Cambridge, UK.There is an emerging interest in Artificial Intelligence (AI) teaching and learning in the K-12 setting. While some work has explored the educational content and resources used for this purpose, there is limited empirical evidence on the effectiveness of such AI education interventions. The primary objective of the systematic literature review presented in this paper was to examine research with empirical evidence reporting learning outcomes for teaching and learning AI in K-12 between 2019 and 2022. Through a rigorous selection process, a total of 28 studies were included in the final analysis out of 8,175 papers identified from five research databases using specific search terms. A content analysis method was used to synthesise the data. This paper outlines the focus on learners' context, the extent of empirical support for the pedagogical approaches, and the theoretical coverage of AI topics included in the studies. The majority of studies reported an improvement in both cognitive and affective learning outcomes. The paper concludes by highlighting key areas where additional research is needed in the future as well as the challenges associated with them. Although the findings are based on limited empirical studies, they suggest that a more learner-centred approach, context-aware pedagogical practices, and consistent constructs to measure AI learning outcomes could benefit teaching and learning AI in K-12 schools. Further research is needed to build on these insights.http://www.sciencedirect.com/science/article/pii/S2666920X23000243Artificial intelligenceMachine learningK-12 educationSystematic literature review |
| spellingShingle | Saman Rizvi Jane Waite Sue Sentance Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review Computers and Education: Artificial Intelligence Artificial intelligence Machine learning K-12 education Systematic literature review |
| title | Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review |
| title_full | Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review |
| title_fullStr | Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review |
| title_full_unstemmed | Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review |
| title_short | Artificial Intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review |
| title_sort | artificial intelligence teaching and learning in k 12 from 2019 to 2022 a systematic literature review |
| topic | Artificial intelligence Machine learning K-12 education Systematic literature review |
| url | http://www.sciencedirect.com/science/article/pii/S2666920X23000243 |
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