Collaborative construction of artificial intelligence curriculum in primary schools

Dai, Yun; Liu, Ang; Jian-jun, Qin; Guo, Yanmei; Jong, Morris Siu‐Yung; Chai, Ching Sing; Lin, Ziyan

doi:10.1002/jee.20503

Cited by 30 publications

(12 citation statements)

References 70 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This study was situated in a larger research project between the research team and a school district in China (Dai, Liu, et al, 2023). The school is situated in a middle-class school district with an average socio-economic background, and students are admitted from the school district through a fair banding system.…”

Section: Methodsmentioning

confidence: 99%

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

Dai,

Lin,

Liu

et al. 2023

Journal of Educational Computing Research

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

Dai,

Lin,

Liu

et al. 2023

Journal of Educational Computing Research

Self Cite

View full text Add to dashboard Cite

show abstract

“…It is also convenient for learning in AI to follow the computational thinking model (Shin, 2021), contextualizing the proposed curriculum (Eguchi et al, 2021;Wang et al, 2020) and providing it with the necessary resources for teachers (Eguchi et al, 2021). In this sense, emerging initiatives highlight the need to involve teachers in the process of co-creating a curriculum associated to their context (Barlex et al, 2020;Chiu et al, 2021;Dai et al, 2023;Lin & Brummelen, 2021;Yau et al, 2022).…”

Section: Ai Literacy Curriculum Designmentioning

confidence: 99%

AI literacy in K-12: a systematic literature review

et al. 2023

View full text Add to dashboard Cite

The successful irruption of AI-based technology in our daily lives has led to a growing educational, social, and political interest in training citizens in AI. Education systems now need to train students at the K-12 level to live in a society where they must interact with AI. Thus, AI literacy is a pedagogical and cognitive challenge at the K-12 level. This study aimed to understand how AI is being integrated into K-12 education worldwide. We conducted a search process following the systematic literature review method using Scopus. 179 documents were reviewed, and two broad groups of AI literacy approaches were identified, namely learning experience and theoretical perspective. The first group covered experiences in learning technical, conceptual and applied skills in a particular domain of interest. The second group revealed that significant efforts are being made to design models that frame AI literacy proposals. There were hardly any experiences that assessed whether students understood AI concepts after the learning experience. Little attention has been paid to the undesirable consequences of an indiscriminate and insufficiently thought-out application of AI. A competency framework is required to guide the didactic proposals designed by educational institutions and define a curriculum reflecting the sequence and academic continuity, which should be modular, personalized and adjusted to the conditions of the schools. Finally, AI literacy can be leveraged to enhance the learning of disciplinary core subjects by integrating AI into the teaching process of those subjects, provided the curriculum is co-designed with teachers.

show abstract

“…Nevertheless, teaching AI to young learners has been widely deemed challenging due to a lack of pedagogical experience and knowledge in K-12 schools (Dai et al, 2023). This situation is partly due to the fact that AI is a highly sophisticated subject at the intersection of science, engineering and society (Russell & Norvig, 2021).…”

Section: Practitioner Notesmentioning

confidence: 99%

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

Dai,

Lin,

Liu

et al. 2023

Brit J Educational Tech

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Collaborative construction of artificial intelligence curriculum in primary schools

Cited by 30 publications

References 70 publications

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

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

AI literacy in K-12: a systematic literature review

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

Contact Info

Product

Resources

About