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 notesWhat 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.

Effect of an Analogy-Based Approach of Artificial Intelligence Pedagogy in Upper Primary Schools

“…The research offers insights into the position and conceptualization of AI education in the K-12 context. The significant outperformance brought about by the analogy-based approach emphasizes the importance of fostering students’ conceptual understanding of AI terms, mechanisms, and principles before delving into the technical, practical details of building AI systems; that is, AI education in the K-12 context should prioritize teaching AI for student understanding (Dai et al, 2023b; Su et al, 2022). A solid comprehension of AI terms and processes can establish a strong foundation for students’ subsequent development of skills and ethical awareness (Fast & Horvitz, 2017; UNESCO, 2022).…”

“…For example, CS instructors often analogize the central processing unit (CPU) to the human brain and the algorithm to the cognitive schema. AI educators use activities like comparing AI devices and AI versus human abilities to enhance primary students’ understanding of the nature of intelligence (Dai et al, 2023b; Heinze et al, 2010; Zimmerman, 2018). With humans as an analogy, students are guided to transform something already known (their understanding of humans) into understanding something new (the intended knowledge).…”

Active Inference Goes to School. The Importance of Active Learning in the Age of Large Language Models

“…Manufactured learning environments which leverage exploratory behaviours and facilitate manageable prediction errors are then fundamental when we turn to imagine the implementation of different, by definition disembodied artefacts such as LLMs. LLMs' potential impact on education has generated a variety of responses, from fear to enthusiasm to immediate applicative attempts in a variety of contexts (e.g., Williamson et al, 2023;Devlin, 2023;Milano et al, 2023;Yan et al, 2023;Prihar et al, 2023;Dai et al, 2023;Sanusi et al, 2023; for a summarising commentary, see: Kasneci et al, 2023). If it seems clear that LLMs are here to stay and their integration in education is not challenge-free, then "a clear pedagogical approach with a strong focus on critical thinking and strategies for fact checking are required" (Kasneci et al, 2023.…”

The Influence of Artificial Intelligence Technology on the Optimization of the Teaching Model of Higher Education in the Context of the Pandemic

“…The sole objective of AI is to make machines independent and enable them to process information, analyze it, and present it. Instead, it is an attempt by researchers to perform previously feasible tasks only by human cognition (Dai et al, 2024). There are many applications based on AI.…”