A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education

Xia, Qi; Chiu, Thomas K. F.; Lee, Min; Sanusi, Ismaila Temitayo; Dai, Yun; Chai, Ching Sing

doi:10.1016/j.compedu.2022.104582

Cited by 166 publications

(45 citation statements)

References 47 publications

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“…For students, a primary result of implementing AI is increased motivation and engagement (Xia et al, 2022). It increases their learning interest (Lin & Chang, 2020), facilitates interactive learning environments via particular tools like Smart Sparrow that promote learner engagement with educational content (Karsenti, 2019).…”

Section: Potential Benefits For Studentsmentioning

confidence: 99%

Revolutionizing education with AI: Exploring the transformative potential of ChatGPT

2023

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Artificial intelligence (AI) introduces new tools to the educational environment with the potential to transform conventional teaching and learning processes. This study offers a comprehensive overview of AI technologies, their potential applications in education, and the difficulties involved. Chatbots and related algorithms that can simulate human interactions and generate human-like text based on input from natural language are discussed. In addition to the advantages of cutting-edge chatbots like ChatGPT, their use in education raises important ethical and practical challenges. The authors aim to provide insightful information on how AI may be successfully incorporated into the educational setting to benefit teachers and students, while promoting responsible and ethical use.

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Section: Potential Benefits For Studentsmentioning

confidence: 99%

Revolutionizing education with AI: Exploring the transformative potential of ChatGPT

2023

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“…The EAD approach has the potential to transform AI education by making AI learning fun, accessible and inclusive for elementary school students. Inclusive access to quality education resources has long been a concern in AI education (Xia et al, 2022). As the participating students in this study were beginning learners from nonelite backgrounds, their growth has challenged the claim that AI education is only feasible for high‐performing or gifted students in elementary schools.…”

Section: Conclusion and Discussionmentioning

confidence: 99%

“…Given the theoretical framework of conceptual change, students' preconception was a critical starting point for teaching and learning, which constituted a baseline for observing and assessing student growth in the experimental study. Meanwhile, literature review shows that gender often plays a critical role in shaping students' learning experiences and performances in engineering‐ and technology‐oriented subjects like AI (Xia et al, 2022). Therefore, the students' level of preconceptions and gender were selected as the key variables to match students to the experimental and control groups.…”

Section: Methodsmentioning

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

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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.

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“…The reasons for differences in family factors are closely related to parents’ knowledge and social-economic status. Parents considerably impact their children’s learning motivation ( Garn et al, 2010 ; Yang and Wang, 2021 ; Xia et al, 2022 ). Students become more enthusiastic about learning and enjoy learning English more when their parents support them ( Ibabe, 2016 ; Zhang and Wang, 2020 ).…”

Section: Discussionmentioning

confidence: 99%

Understanding the importance of motivational intensity in English as a foreign language context: A structural equation modeling analysis

Zhu

Zhang

2022

Front. Psychol.

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IntroductionMotivational intensity is the effort learners make in language learning. It is an essential component and a direct measurement of L2 motivation. Few studies have distinguished motivational intensity from motivation and explored its role in learning English as a foreign language (EFL).MethodsThis study examined 208 university students from Hong Kong to investigate the factors that affect motivational intensity and explored the relationship between motivational intensity and learning motivation using structural equation modeling (SEM).Results and discussionThe students’ motivational intensity was affected by personal factors (daily English-learning time and stage of English learning), family factors (monthly household income and parental attitudes), and school factors (English learning engagement and satisfaction). The differences in personal factors, school factors and monthly household income of family factors among different motivational intensity groups were significant whereas the difference in parental attitudes (family factors) between the high- and the low-motivational intensity groups was insignificant. As for the relationship between motivational intensity and motivation, motivational intensity indirectly affected students’ intrinsic interest through their attitudes toward native English speakers (β = 0.16, p = 0.041 < 0.05). The significant path coefficient from the learning situation to attitudes toward native speakers was negative (p < 0.05), indicating that attitudes toward native speakers decline even when the learning situation improves. This study enriched the theoretical study of motivation theory and provided teaching suggestions for improving EFL learning.

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A self-determination theory (SDT) design approach for inclusive and diverse artificial intelligence (AI) education

Cited by 166 publications

References 47 publications

Revolutionizing education with AI: Exploring the transformative potential of ChatGPT

Revolutionizing education with AI: Exploring the transformative potential of ChatGPT

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

Understanding the importance of motivational intensity in English as a foreign language context: A structural equation modeling analysis

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