Effect of an Analogy-Based Approach of Artificial Intelligence Pedagogy in Upper Primary Schools
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| Title: | Effect of an Analogy-Based Approach of Artificial Intelligence Pedagogy in Upper Primary Schools |
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| Language: | English |
| Authors: | Dai, Yun (ORCID |
| Source: | Journal of Educational Computing Research. 2024 61(8):159-186. |
| Availability: | SAGE Publications. 2455 Teller Road, Thousand Oaks, CA 91320. Tel: 800-818-7243; Tel: 805-499-9774; Fax: 800-583-2665; e-mail: journals@sagepub.com; Web site: https://sagepub.com |
| Peer Reviewed: | Y |
| Page Count: | 28 |
| Publication Date: | 2024 |
| Document Type: | Journal Articles Reports - Research |
| Education Level: | Elementary Education Grade 6 Intermediate Grades |
| Descriptors: | Foreign Countries, Grade 6, Artificial Intelligence, Logical Thinking, Elementary School Students, Instructional Effectiveness, Computer Science Education, Scaffolding (Teaching Technique), Instructional Design |
| Geographic Terms: | China |
| DOI: | 10.1177/07356331231201342 |
| ISSN: | 0735-6331 1541-4140 |
| Abstract: | Artificial intelligence (AI) has emerged as a prominent topic in K-12 education recently. However, pedagogical design has remained a major challenge, especially among young learners. Guided by the Zone of Proximal Development theory and AI education research literature, this design-based study proposes an analogy-based pedagogical approach to support AI teaching and learning in upper primary education. This pedagogical approach is centered on human-AI comparison, where humans are gradually shifted from an analogue to a contrast to make visible the attributes, mechanisms, and processes of AI. To evaluate its effectiveness, a quasi-experimental study with mixed methods was conducted. The quantitative comparison shows that the participants in the experimental group learning with the analogy-based pedagogical approach significantly outperformed their peers with the conventional direct instructional approach in all three dimensions of AI knowledge, skills, and ethical awareness. Qualitative analyses further reveal its pedagogical benefits, including demystifying AI through relatable and engaging learning, supporting student comprehension and skill mastery, and nurturing critical thinking and attitudes. The analogy-based approach contributes to the field of K-12 AI education with an age-appropriate, child-friendly pedagogical approach. Notably, AI education should prioritize teaching for student understanding, and AI should be recognized as an independent subject with interdisciplinary applications. |
| Abstractor: | As Provided |
| Entry Date: | 2023 |
| Accession Number: | EJ1402101 |
| Database: | ERIC |
| Abstract: | Artificial intelligence (AI) has emerged as a prominent topic in K-12 education recently. However, pedagogical design has remained a major challenge, especially among young learners. Guided by the Zone of Proximal Development theory and AI education research literature, this design-based study proposes an analogy-based pedagogical approach to support AI teaching and learning in upper primary education. This pedagogical approach is centered on human-AI comparison, where humans are gradually shifted from an analogue to a contrast to make visible the attributes, mechanisms, and processes of AI. To evaluate its effectiveness, a quasi-experimental study with mixed methods was conducted. The quantitative comparison shows that the participants in the experimental group learning with the analogy-based pedagogical approach significantly outperformed their peers with the conventional direct instructional approach in all three dimensions of AI knowledge, skills, and ethical awareness. Qualitative analyses further reveal its pedagogical benefits, including demystifying AI through relatable and engaging learning, supporting student comprehension and skill mastery, and nurturing critical thinking and attitudes. The analogy-based approach contributes to the field of K-12 AI education with an age-appropriate, child-friendly pedagogical approach. Notably, AI education should prioritize teaching for student understanding, and AI should be recognized as an independent subject with interdisciplinary applications. |
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| ISSN: | 0735-6331 1541-4140 |
| DOI: | 10.1177/07356331231201342 |
