Exploring the Impact of AI on Go Education: A Teacher Survey

Daniela Trinks & Chi-min Oh /December 7, 2023

How to cite this article:
Trinks, D., Oh, C. (2023). Exploring the Impact of AI on Go Education: A Teacher Survey. Journal of Go Studies, 17(2), 107-150.doi: 10.62578/689093

Abstract
        In 2016, AlphaGo’s advent transformed the world of Go as AI-powered tools began to surpass the world’s top professional players. The rapid growth in AI’s influence raises questions about the potential replacement of human players. This paper examines recent trends in Go education in light of the AI revolution and its future implications. To investigate these trends, we conducted a survey among Go educators, focusing on three key aspects: (1) the perceived benefits of learning Go, (2) the impact of AI on Go education, and (3) educators’ satisfaction with Go AI tools. Data was collected through online questionnaires in English, Korean, and Chinese.
        Survey results indicate that Go teachers believe learning Go equips students with valuable skills, including critical thinking, resilience, and perseverance, fostering character and cognitive development. However, educators’ opinions on AI-based tools in the classroom are mixed. Approximately 41% of respondents have refrained from using AI tools, citing concerns about their suitability for lower-level and younger learners, as well as perceived difficulties in their implementation. Additionally, there are concerns about over-reliance on AI and its limitations in Go education. Conversely, educators who have integrated AI tools report overall satisfaction and optimism for further developments. This study highlights the growing acceptance of AI programs and their positive impact on Go education. While practical demands remain partially unmet, many educators, in general, express satisfaction with the available programs. The findings of this study shed light on areas for potential improvement in AI to further enhance Go education.

Keywords: Go, Baduk, Weiqi, Education, Artificial Intelligence, Educational Technology, Instructional Media, Teacher Survey


References

An, Y. (2021). A history of instructional media, instructional design, and theories. International Journal of Technology in Education ( IJTE), 4(1), 1-21. doi: 10.46328/ijte.35

Azoulay, A. (2018). Making the most of artificial intelligence. The UNESCO Courier, 3, 36-39.

Baudiš, P., & Gailly, J. L. (2011). Pachi: State of the art open source Go program. Advances in computer games, 24-38.
doi: 10.1007/978-3-642-31866-5_3

Binder, W. (2022). Technology as ( Dis-) Enchantment. AlphaGo and the Meaning-Making of Artificial Intelligence. Cultural Sociology. 1-24. doi: 10.1177/17499755221138720

Bouzy, B., & Cazenave, T. (2001). Computer Go: an AI oriented survey. Artificial Intelligence, 132(1), 39-103. doi: 10.1016/S0004-3702(01)00127-8

Chatterjee, S., & Bhattacharjee, K. K. (2020). Adoption of artificial intelligence in higher education: A quantitative analysis using structural equation modelling. Education and Information Technologies, 25, 3443-3463. doi: 10.1007/s10639-020-10159-7

Chen, J. X. (2016). The evolution of computing: AlphaGo. Computing in Science & Engineering, 18(4), 4-7. doi: 10.1109/MCSE.2016.74

Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. Ieee Access, 8, 75264-75278. doi: 10.1109/ACCESS.2020.2988510

Chen, X., Zou, D., Xie, H., Cheng, G., & Liu, C. (2022). Two decades of artificial intelligence in education. Educational Technology & Society, 25(1), 28-47.

Chen, Y., Huang, A., Wang, Z., Antonoglou, I., Schrittwieser, J., Silver, D., & de Freitas, N. (2018). Bayesian optimization in alphago. https://arxiv.org/pdf/1812.06855.pdf.

Cruz-Jesus, F., Castelli, M., Oliveira, T., Mendes, R., Nunes, C., Sa-Velho, M., & Rosa-Louro, A. (2020). Using artificial intelligence methods to assess academic achievement in public high schools of a European Union country. Heliyon, 6(6). doi: 10.1016/j.heliyon.2020.e04081

Doshay, D. G., & McDowell, C. (2005) Slugo: A Computer Baduk Program, The 3rd International Conference on Baduk, 33-49.

Egri-Nagy, A., & Törmänen, A. (2020). The game is not over yet-go in the post-AlphaGo era. Philosophies, 5(4), 37. doi: 10.3390/philosophies5040037

Ezzaim, A., Kharroubi, F., Dahbi, A., Aqqal, A., & Haidine, A. (2022). Artificial intelligence in education-State of the art. International Journal of Computer Engineering and Data Science, 2(2), 1-11.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2018). AI4People-an ethical framework for a good AI society: opportunities, risks, principles, and recommendations. Minds and machines, 28, 689-707. doi: 10.1007/s11023-018-9482-5

Friedenbach, K. (2005). Computer and Mathematical Go A Personal Perspective on the First 35 Years. The 3rd International Conference on Baduk, 65-82.

Fu, M. C. (2016). AlphaGo and Monte Carlo tree search: the simulation optimization perspective. In 2016 Winter Simulation Conference (WSC), 659-670. IEEE. doi: 10.1109/WSC.2016.7822130

Gelly, S., & Silver, D. (2011). Monte-Carlo tree search and rapid action value estimation in computer Go. Artificial Intelligence, 175(11), 1856-1875. doi: 10.1016/j.artint.2011.03.007

Granter, S. R., Beck, A. H., & Papke Jr, D. J. (2017). AlphaGo, deep learning, and the future of the human microscopist. Archives of pathology & laboratory medicine, 141(5), 619-621. doi: 10.5858/arpa.2016-0471-ED

Gürbüzel, F., Sadak, T., & Özdemir, A. Investigation of the effect of Go (Baduk) education on problem solving processes and thinking styles. Journal for the Mathematics Education and Teaching Practices, 3(1), 45-55.

Holcomb, S. D., Porter, W. K., Ault, S. V., Mao, G., & Wang, J. (2018). Overview on deepmind and its alphago zero ai. In Proceedings of the 2018 international conference on big data and education, 67-71. doi: 10.1145/3206157.3206174

Hwang, G. J., Xie, H., Wah, B. W., & Gašević, D. (2020). Vision, challenges, roles and research issues of Artificial Intelligence in Education. Computers and Education: Artificial Intelligence, 1, 100001. doi: 10.1016/j.caeai.2020.100001

Kong, S. C., Cheung, W. M. Y., & Zhang, G. (2023). Evaluating an artificial intelligence literacy programme for developing university students' conceptual understanding, literacy, empowerment and ethical awareness. Educational Technology & Society, 26(1), 16-30.

Kwon, J. S., Jung, W. H., Kim, S. N., Lee, T. Y., Jang, J. H., Choi, C. H., & Kang, D. H. (2013). Exploring the brains of Baduk (Go) experts: gray matter morphometry, resting-state functional connectivity, and graph theoretical analysis. Frontiers in Human Neuroscience, 7(633), 1-17. doi: 10.3389/fnhum.2013.00633

Kwon, J. S., Lee, B., Park, J. Y., Jung, W. H., Kim, H. S., Oh, J. S., ... & Choi, C. H. (2010). White matter neuroplastic changes in long-term trained players of the game of "Baduk"(GO): a voxel-based diffusion-tensor imaging study. Neuroimage, 52(1), 9-19. doi: 10.1016/j.neuroimage.2010.04.014

Lim, C, S. (2009). Korean Baduk School Association and the Status of Youth Go Education, In A White Paper of Korean Baduk 2009, 156-167.

Lewt, Ł. (2006) Developments in computer Baduk, The 4 th International Conference on Baduk, pp. 113-129.

Mańdziuk, J. (2007). Computational intelligence in mind games. Challenges for computational intelligence, 407-442. doi: 10.1007/978-3-540-71984-7_15

Moskowitz, M. L. (2013). Go nation: Chinese masculinities and the game of weiqi in China. University of California Press. doi: 10.1525/california/9780520276314.001.0001

Ouyang, F., & Jiao, P. (2021). Artificial intelligence in education: The three paradigms. Computers and Education: Artificial Intelligence, 2, 100020. doi: 10.1016/j.caeai.2021.100020

Park, W., Kim, S., Kim, K. L., & Kim, J. (2019). Alphago's decision making. Journal of Applied Logics. IFCoLog Journal of Logics and their Applications, 6(1), 105-155.

Park, J., Im, J., On, S., Lee, S. J., & Lee, J. (2022). A statistical approach for detecting AI-assisted cheating in the game of Go. Journal of the Korean Physical Society, 81, 1189-1197. doi: 10.1007/s40042-022-00622-8

Popenici, S. A., & Kerr, S. (2017). Exploring the impact of artificial intelligence on teaching and learning in higher education. Research and Practice in Technology Enhanced Learning, 12(1), 1-13. doi: 10.1186/s41039-017-0062-8

Ramon, J., & Blockeel, H. (2001). A survey of the application of machine learning to the game of go. In Proceedings of the First International Conference on Baduk, 1-10.

Ramon, J., & Struyf, J. (2003). Computer science issues in Baduk. In Proceedings of the second International Conference on Baduk, 163-181. Roll, I., & Wylie, R. (2016). Evolution and revolution in artificial intelligence in education. International Journal of Artificial Intelligence in Education, 26, 582-599. doi: 10.1007/s40593-016-0110-3

Salas-Pilco, S. Z., Xiao, K., & Oshima, J. (2022). Artificial intelligence and new technologies in inclusive education for minority students: a systematic review. Sustainability, 14(20), 13572. doi: 10.3390/su142013572

Silver, D., Huang, A., Maddison, C. J., Guez, A., Sifre, L., Van Den Driessche, G., ... & Hassabis, D. (2016). Mastering the game of Go with deep neural networks and tree search. Nature, 529(7587), 484-489. doi: 10.1038/nature16961

Silver, D., Schrittwieser, J., Simonyan, K., Antonoglou, I., Huang, A., Guez, A., ... & Hassabis, D. (2017). Mastering the game of go without human knowledge. Nature, 550(7676), 354-359. doi: 10.1038/nature24270

Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., ... & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140-1144. doi: 10.1126/science.aar6404

Su, J., & Yang, W. (2022). Artificial intelligence in early childhood education: A scoping review. Computers and Education: Artificial Intelligence, 3, 100049. doi: 10.1016/j.caeai.2022.100049

Uzumcu, O., & Acilmis, H. (2023). Do Innovative Teachers Use AI-powered Tools More Interactively? A Study in the Context of Diffusion of Innovation Theory. Technology, Knowledge and Learning, 1-20. doi: 10.1007/s10758-023-09687-1

Wang, F. Y., Zhang, J. J., Zheng, X., Wang, X., Yuan, Y., Dai, X., ... & Yang, L. (2016). Where does AlphaGo go: From church-turing thesis to AlphaGo thesis and beyond. IEEE/CAA Journal of Automatica Sinica, 3(2), 113-120. doi: 10.1109/JAS.2016.7471613

Wang, X. (2023, June 14). SenseTime unveils go robot powered by AI. China Daily. https://www.chinadaily.com.cn/a/202306/14/WS6489d0b-da31033ad3f7bc425.html

Yang, S. J., Ogata, H., Matsui, T., & Chen, N. S. (2021). Human-centered artificial intelligence in education: Seeing the invisible through the visible. Computers and Education: Artificial Intelligence, 2, 100008. doi: 10.1016/j.caeai.2021.100008

Gallup Korea. (2016). Research on Baduk. In 2016 Korean Baduk White Paper, 12-89. Korea Baduk Federation.

Jeon, G. I. (2021). Exploring the Posthuman Pedagogy Spread Out on the Bansang(Go-Table). The Journal of Korean Educational Idea, 35(4), 245-287.

Jeon, H. B., Chung, H., Kang, B. O., & Lee, Y. K. (2021). Survey of Recent Research in Education based on Artificial Intelligence, Electronics and Telecommunications Trends, 36(1), 71-80.

Kim, B. R. M., & Cho, B. H. (2010). The Effect of the Baduk Play Activity Upon a Child's Intelligence, Problem-solving, and Delay of Gratification. Korean Journal of Human Ecology, 19(2), 245-256. doi: 10.5934/KJHE.2010.19.2.245

Lee H.-J., Jeong, S.-H. (2007) The Effect of Baduk Education on Children's Emotional Intelligence and Baduk Knowledge Acquisition. Korean Society for Baduk Studies 2007. 4(1), 47-64.

On, S. J., & Jeong, S. H. (2016) An Analysis of AlphaGo's Unusual Moves, Korean Society for Baduk Studies, 13(2), 11-27.

Wakabayashi, H., & Ito, T. (2020). A System to Praise Moves for Motivating Go Beginners. Transactions of the Japanese Society for Artificial Intelligence (GI), 2020(2), 1-8