A Competency-Based Curriculum for Fostering Artificial Intelligence Skills in Thai Children and Youth
A Competency-Based Curriculum for Fostering Artificial Intelligence Skills in Thai Children and Youth |
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© 2024 by IJETT Journal | ||
Volume-72 Issue-10 |
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Year of Publication : 2024 | ||
Author : Sirachet Phodhiran, Pichate Kunakornvong, Pongpon Nilaphruek, Jaturapith Krohkaew, Niti Witthayawiroj, Padma Nyoman Crisnapati, Yamin Thwe |
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DOI : 10.14445/22315381/IJETT-V72I10P135 |
How to Cite?
Sirachet Phodhiran, Pichate Kunakornvong, Pongpon Nilaphruek, Jaturapith Krohkaew, Niti Witthayawiroj, Padma Nyoman Crisnapati, Yamin Thwe,"A Competency-Based Curriculum for Fostering Artificial Intelligence Skills in Thai Children and Youth," International Journal of Engineering Trends and Technology, vol. 72, no. 10, pp. 357-372, 2024. Crossref, https://doi.org/10.14445/22315381/IJETT-V72I10P135
Abstract
This study introduces a carefully constructed curriculum based on competencies, with the objective of fostering artificial intelligence skills in children and young individuals. It addresses the pressing requirement for a complete AI education throughout different educational stages. The curriculum has been carefully structured into three unique tiers, namely Basic, Intermediate, and Advanced, in order to accommodate the varied educational backgrounds and developmental stages of learners. A comprehensive training package was created, encompassing a diverse range of educational materials such as video lectures, motion graphics, quizzes, and other resources, which were disseminated through an online assessment platform. The training approach aligns with the UNESCO competence curriculum development model, which encompasses the essential components of knowledge, abilities, and attitudes pertaining to artificial intelligence. Following this, the curriculum was put into effect, and further evaluation endeavors showed noteworthy accomplishments. The initiative provided benefits to a total of 2,700 pupils and 184 newly appointed instructors, constantly surpassing the established pass requirements. Moreover, the engagement of children and young people from 23 schools in an innovation contest, resulting in the receipt of 76 ideas, highlights the broad popularity of the curriculum. The results highlight the significant influence of the curriculum in fostering knowledge and skills related to Artificial Intelligence (AI) among children and young individuals. This curriculum plays a crucial role in reducing the educational disparity in this rapidly evolving domain and equipping the upcoming generation with the necessary ability to succeed in a society heavily influenced by AI.
Keywords
Artificial intelligence, Literacy, Competency, Youth, Curriculum.
References
[1] Jose Sanchez Gracias et al., “Smart Cities—A Structured Literature Review,” Smart Cities, vol. 6, no. 4, pp. 1719-1743, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[2] Arpan Kumar Kar, and P.S. Varsha, “Unravelling the Techno-Functional Building Blocks of Metaverse Ecosystems – A Review and Research Agenda,” International Journal of Information Management Data Insights, vol. 3, no. 2, pp. 1-15, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[3] Michael Chui, Roger Roberts, and Lareina Yee, McKinsey Technology Trends Outlook 2022, 2022. [Online]. Available: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-top-trends-in-tech-2022
[4] Davy Tsz Kit Ng et al., “Conceptualizing AI Literacy: An Exploratory Review,” Computers and Education: Artificial Intelligence, vol. 2, pp. 1-11, 2021.
[CrossRef] [Google Scholar] [Publisher Link]
[5] Harald Burgsteiner, Martin Kandlhofer, and Gerald Steinbauer, “IRobot: Teaching the Basics of Artificial Intelligence in High Schools,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 30, no. 1, pp. 4126-4127, 2016.
[CrossRef] [Google Scholar] [Publisher Link]
[6] Xiao Han et al., “Design of AI + Curriculum for Primary and Secondary Schools in Qingdao,” 2018 Chinese Automation Congress, Xi'an, China, pp. 4135-4140, 2018.
[CrossRef] [Google Scholar] [Publisher Link]
[7] Antonios Karampelas, “Developing and Delivering a High School Artificial Intelligence Course in Blended and Online Learning Environments,” European Distance and E-Learning Network (EDEN) Conference Proceedings, no. 1, pp. 255-261, 2020.
[Google Scholar] [Publisher Link]
[8] Michał Bednarek, Michał R. Nowicki, and Krzysztof Walas, “HAPTR2: Improved Haptic Transformer for Legged Robots’ Terrain Classification,” Robotics and Autonomous Systems, vol. 158, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[9] Manh-Tung Ho et al., “Understanding the Acceptance of Emotional Artificial Intelligence in Japanese Healthcare System: A Cross-Sectional Survey of Clinic Visitors’ Attitude,” Technology in Society, vol. 72, 2023.
[CrossRef] [Google Scholar] [Publisher Link]
[10] Ethan Fast, and Eric Horvitz, “Long-Term Trends in the Public Perception of Artificial Intelligence,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 31, no. 1, pp. 1-7, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[11] Nils J. Nilsson, The Quest for Artificial Intelligence: A History of Ideas and Achievements, Cambridge University Press, pp. 1-578, 2009.
[Google Scholar] [Publisher Link]
[12] Ingrid F Russell, Zdravko I Markov, and Todd William Neller, “Teaching AI through Machine Learning Projects,” Proceedings of the 11th Annual SIGCSE Conference on Innovation and Technology in Computer Science Education, Bologna Italy, 2006.
[CrossRef] [Google Scholar] [Publisher Link]
[13] Jie Chen, Jian Sun, and Gang Wang, “From Unmanned Systems to Autonomous Intelligent Systems,” Engineering, vol. 12. pp. 16-19, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[14] Alexei V. Samsonovich, “Socially Emotional Brain-Inspired Cognitive Architecture Framework for Artificial Intelligence,” Cognitive Systems Research, vol. 60, pp. 57-76, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[15] Gülay Canbaloğlu, Jan Treur, and Peter H.M.P. Roelofsma, “Computational Modeling of Organisational Learning by Self-Modeling Networks,” Cognitive Systems Research, vol. 73, pp. 51-64, 2022.
[CrossRef] [Google Scholar] [Publisher Link]
[16] Ray Kurzweil, The Age of Intelligent Machines, MIT Press, pp. 1-565, 1990.
[Google Scholar] [Publisher Link]
[17] Elaine Rich, and Kevin Knight, Artificial Intelligence, Tata Mcgraw Hill Education Private Limited, pp. 1-567, 2011.
[Google Scholar] [Publisher Link]
[18] David Lynton Poole, Alan K. Mackworth, and Randy Goebel, Computational Intelligence: A Logical Approach, Oxford University Press, pp. 1-558, 1998.
[Google Scholar] [Publisher Link]
[19] Nils J. Nilsson, Artificial Intelligence: A New Synthesis, Morgan Kaufmann Publishers, pp. 1-513, 1998.
[Google Scholar] [Publisher Link]
[20] Stefania Druga, “Growing up with AI: Cognimates: From Coding to Teaching Machines,” Graduate Theses, Massachusetts Institute of Technology, pp. 1-204, 2018.
[Google Scholar] [Publisher Link]
[21] Safinah Ali et al., “Constructionism, Ethics, and Creativity: Developing Primary and Middle School Artificial Intelligence Education,” International Workshop on Education in Artificial Intelligence K-12 (Eduai’19), California, vol. 2, pp. 1-4, 2019.
[Google Scholar] [Publisher Link]
[22] Orasa Patsadu, Yanee Muchchimwong, and Nattaburud Narudkun, “The Development of Game to Develop the Cognitive Skill for Autistic Children via Virtual Reality,” Information Technology Journal, vol. 15, no. 2, pp. 12-22, 2019.
[Google Scholar] [Publisher Link]
[23] Mojtaba Dadashzadeh et al., “Weed Classification for Site-Specific Weed Management Using an Automated Stereo Computer-Vision Machine-Learning System in Rice Fields,” Plants, vol. 9, no. 5, pp. 1-19, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[24] Daisuke Komura, and Shumpei Ishikawa, “Machine Learning Approaches for Pathologic Diagnosis,” Virchows Archiv, vol. 475, pp. 131-138, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[25] David Stuart Touretzky, “Seven Big Ideas in Robotics, and How to Teach Them,” Proceedings of the 43rd ACM Technical Symposium on Computer Science Education, Raleigh North Carolina USA, pp. 39-44, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[26] Abhishek Kumar Kashyap et al., “A Hybrid Technique for Path Planning of Humanoid Robot NAO in Static and Dynamic Terrains,” Applied Soft Computing Journal, vol. 96, 2020.
[CrossRef] [Google Scholar] [Publisher Link]
[27] A.P. Okunev et al., “Digital Modeling and Testing of Tractor Characteristics,” Russian Engineering Research, vol. 39, pp. 453-458, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[28] Matthew Smith et al., “Big Data Privacy Issues in Public Social Media,” 2012 6th IEEE International Conference on Digital Ecosystems and Technologies, Campione d'Italia, Italy, pp. 1-6, 2012.
[CrossRef] [Google Scholar] [Publisher Link]
[29] Hunt Allcott, and Matthew Gentzkow, “Social Media and Fake News in the 2016 Election,” Journal of Economic Perspectives, vol. 31, no. 2, pp. 211-236, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[30] Joy Buolamwini, and Timnit Gebru, “Gender Shades: Intersectional Accuracy Disparities in Commercial Gender Classification,” Proceedings of the 1st Conference on Fairness, Accountability and Transparency, vol. 81, pp. 77-91, 2018.
[Google Scholar] [Publisher Link]
[31] David S. Touretzky, “Computational Thinking and Mental Models: From Kodu to Calypso,” 2017 IEEE Blocks and Beyond Workshop (B&B), Raleigh, NC, USA, pp. 71-78, 2017.
[CrossRef] [Google Scholar] [Publisher Link]
[32] Michael Anderson, and Susan Leigh Anderson, Machine Ethics, Cambridge University Press, 2011.
[Google Scholar] [Publisher Link]
[33] Michael J. Quinn, Ethics for the Information Age, Pearson, pp. 1-522, 2015.
[Google Scholar] [Publisher Link]
[34] David Touretzky et al., “Envisioning AI for K-12: What Should Every Child Know About AI?,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 33, no. 1, pp. 9795-9799, 2019.
[CrossRef] [Google Scholar] [Publisher Link]
[35] Kanit Sriklaub, Suwimon Wongwanich, and Nonglak Wiratchai, “Development of the Classroom Climate Measurement Model,” Procedia - Social and Behavioral Sciences, vol. 171, pp. 1353-1359, 2015.
[CrossRef] [Google Scholar] [Publisher Link]
[36] Seonghun Kim et al., “Why and What to Teach: AI Curriculum for Elementary School,” Proceedings of the AAAI Conference on Artificial Intelligence, vol. 35, no. 17, pp. 15569-15576, 2021.
[CrossRef] [Google Scholar] [Publisher Link]