A Study on the Curriculum Design for K-12 AI Education Using Knowledge Graph

  • Dong Hwa Kim
  • Rojina Shakya
Keywords: 4KW, AI education, 4th industrial revolution, S/W Education, ChatGPT


The 4th industrial revolution, AI, and ChatGPT have been advancing up as key topics in industrial areas and educational systems. This paper deals with the educational curriculum for the effective nurturing of manpower including the ChatGPT. ChatGPT and AI are highly impacting on educational systems as well as in many industrial areas because ChatGPT can be easily used by everyone to ask questions and get responses about any field. In particular, it helps young students for composing various written content, including articles, and essays, as well as debugging and fixing code. In the coming future, millions of jobs are speculated to be replaced by AI as AI can effectively perform jobs that need automation, such as data collection and repetitive tasks. Therefore, revising education methods and curriculum systems has become a high necessity in today’s time.  However, the government and its representatives do not fully realize how important the change is for education. Thus, this paper provides education methods and curriculum for AI education including ChatGPT by analyzing many papers, reports, and previous experiences.


Aliyu, I., Kana, A., & Aliyu, S. (2020). Development of knowledge graph for university courses management. International Journal of Education and Management Engineering, 10(2), 1-10.
András, B. (2016). Educatio Public Services Nonprofit LLC – director of development, National Ministry of Human Resources. ICT in Education Policies Hungary, Budapest.
Bellomarini, L., Benedetti, M., Gentili, A., Laurendi, R., Magnanimi, D., Muci, A., & Sallinger, E. (2020). COVID-19 and company knowledge graphs: assessing golden powers and economic impact of selective lockdown via AI reasoning. arXiv preprint arXiv:2004.10119.
Chen, Y., Li, H., Li, H., Liu, W., Wu, Y., Huang, Q., & Wan, S. (2022). An overview of knowledge graph reasoning: key technologies and applications. Journal of Sensor and Actuator Networks, 11(4), 78. https://doi.org/10.3390/jsan11040078
Chistruga, B. et al. (2016). European integration and competitiveness of EU new member states. European Journal of Economics and Business Studies, 6(1), 175–185.
Dasgupta, P., & Weale, M. (1992). On measuring the quality of life. World Development, 20(1), 119–131.
Filippidis, I., & Katrakilidis, C. (2015). Finance, institutions and human development: Evidence from developing countries. Economic Research-Ekonomska Istraživanja, 28(1), 1018–1033.
Goldsmith, A. (1995). Economic rights and government in developing countries: Cross national evidence on growth and development. Studies in Comparative International Development, 32(2), 29–44.
Hamada, R. (2014). Vybrané spôsoby a metódy merania a hodnotenia regionálnych disparít. Regionální rozvoj mezi teorií a praxí, 3(1), 21–34.
Ji, S., Pan, S., Cambria, E., Marttinen, P., & Philip, S. Y. (2020). A survey on knowledge graphs: Representation, acquisition, and applications. arXiv preprint arXiv:2002.00388v1
Ke, Q., & Lin, J. (2022). Dynamic Generation of Knowledge Graph Supporting STEAM Learning Theme Design. Applied Sciences, 12(21), 11001.
Kordos, M. (2012). US-EU bilateral trade relations – Transatlantic economic issues. ICEI 2012: Proceedings of the 1st International Conference on European Integration (pp. 131–139). VSB: Ostrava.
Noy, N., Gao, Y., Jain, A., Narayanan, A., Patterson, A., & Taylor, J. (2019). Industry-scale Knowledge Graphs: Lessons and Challenges: Five diverse technology companies show how it’s done. Queue, 17(2), 48-75.
Peng, C., Xia, F., Naseriparsa, M., & Osborne, F. (2023). Knowledge graphs: Opportunities and challenges. Artificial Intelligence Review, 56, 13071–13102. https://doi.org/10.1007/s10462-023-10465-9
Python Awesome. (2019). Python library for knowledge graph embedding and representation learning. Python Awesome. Retrieved from: https://pythonawesome.com/python-library-for-knowledge-graph-embedding-and-representation-learning/?__cf_chl_captcha_tk__=d71298ff975e0814d43f9de
Sheth, A., Padhee, S., & Gyrard, A. (2019). Knowledge graphs and knowledge networks: the story in brief. IEEE Internet Computing, 23(4), 67-75.
Simperl, E., Corcho, O., Grobelnik, M., Roman, D., Soylu, A., Ruíz, M. J. F., ... & Lech, T. C. (2019). Towards a knowledge graph based platform for public procurement. In Metadata and Semantic Research: 12th International Conference, MTSR 2018. Limassol, Cyprus, October 23-26, 2018, Revised Selected Papers 12 (pp. 317-323). Springer International Publishing.
Sousa, R. T., Silva, S., & Pesquita, C. (2020). Evolving knowledge graph similarity for supervised learning in complex biomedical domains. BMC bioinformatics, 21, 1-19.
Tabacof, P., & Costabello, L. (2019). Probability calibration for knowledge graph embedding models. In International Conference on Learning Representations,
Tuomi, I. et al. (2018). The Impact of Artificial Intelligence on Learning, Teaching, and Education (M. Cabrera, R. Vuorikari, Y. Punie, Eds.). Luxembourg: Publications Office of the European Union.
Yu, S. Y. et al. (2019). A python library for knowledge graph embbedding. Retrieved from https://github.com/Sujit-O/pykg2vec
How to Cite
Kim, D. H., & Shakya, R. (2023). A Study on the Curriculum Design for K-12 AI Education Using Knowledge Graph. European Journal of Science, Innovation and Technology, 3(4), 515-531. Retrieved from https://ejsit-journal.com/index.php/ejsit/article/view/277