An embodied, analogical and disruptive approach of AI pedagogy in upper elementary education: An experimental study

Abstract While AI has become more prevalent in our society than ever, many young learners are found holding various naive, erroneous conceptions of AI due to the influence of their technology and media environments. To address this issue, this study seeks to propose a novel pedagogical solution to improve upper‐elementary school students' scientific understanding of AI. Following a theory‐informed design convention, we propose an embodied, analogical and disruptive (EAD) approach which is centred on a human–AI comparison through analogical teaching and embodied interaction. To evaluate the impact of this approach, a matched‐group experimental study with pre‐ and posttest interviews was conducted among 77 Grade 6 elementary students in China. The statistical analysis showed that the experimental group, learning via the EAD approach, significantly outperformed their counterparts receiving direct instruction in terms of student growth in understanding. Qualitative analyses revealed the strengths of the EAD approach in supporting student engagement, abstract thinking and system thinking, and its limitations in cognitive overload and communication issues. Patterns of student learning were identified, including their mental schemas and strategies. The EAD approach, as an evidence‐based, age‐appropriate pedagogical solution, demonstrates the value of embodied cognition and human–AI analogy in AI education in elementary education. Practitioner notes What is already known about this topic Many young students hold naive, erroneous conceptions about AI. Pedagogical design has long been a critical challenge in teaching AI in K‐12 schools. Human–AI analogies are often used to teach AI, but with obvious pitfalls to be fixed. What this paper adds An embodied, analogical and disruptive (EAD) approach is proposed to improve students’ understandings of AI. An experimental study shows statistically significant greater gains in students’ understanding with the EAD approach than with the conventional AI‐only direct instruction approach. Qualitative analysis shows the strength of the EAD approach in supporting student engagement, abstract thinking and systems thinking, and its limitations in cognitive overload and communication issues. Implications for practice and/or policy The EAD approach offers a novel pedagogical solution to promote inclusive and quality AI education for young learners. The EAD approach highlights the pedagogical value of embodied cognition and human–AI analogy in AI education. The EAD approach can complement programming and other plugged activities in AI, and more broadly technology and engineering education.