Redefining educational paradigms: Integrating generative AI into society 5.0 for sustainable learning outcomes

FX. Risang Baskara, Asokan Vasudevan, Zohaib Hassan Sain, Mcxin Tee, Vasumathi Arumugam, Suma Parahakaran, Rajani Balakrishnan

Article ID: 6385
Vol 8, Issue 12, 2024

VIEWS - 18 (Abstract) 5 (PDF)

Abstract


The digital era has ushered in significant advancements in Generative Artificial Intelligence (GAI), particularly through Generative Models and Large Language Models (LLMs) like ChatGPT, revolutionizing educational paradigms. This research, set against the backdrop of Society 5.0 and aimed at sustainable educational practices, utilizes qualitative analysis to explore the impact of Generative AI in various learning environments. It highlights the potential of LLMs to offer personalized learning experiences, democratize education, and enhance global educational outcomes. The study finds that Generative AI revitalizes learning methodologies and supports educational systems’ sustainability by catering to diverse learning needs and breaking down access barriers. In conclusion, the paper discusses the future educational strategies influenced by Generative AI, emphasizing the need for alignment with Society 5.0’s principles to foster adaptable and sustainable educational inclusion.


Keywords


education; artificial intelligence; large language models; personalized learning; society 5.0

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References


Abd-Alrazaq, A., AlSaad, R., Alhuwail, D., Ahmed, A., Healy, P. M., Latifi, S., ... & Sheikh, J. (2023). Large Language Models in Medical Education: Opportunities, Challenges, and Future Directions. JMIR Medical Education, 9(1), e48291.

Adıgüzel, T., Kaya, M. H., & Cansu, F. K. (2023). Revolutionising education with AI: Exploring the transformative potential of ChatGPT. Contemporary Educational Technology.

Afzal, A., Khan, S., Daud, S., Ahmad, Z., & Butt, A. (2023). Addressing the Digital Divide: Access and Use of Technology in Education. Journal of Social Sciences Review, 3(2), 883-895.

Aggarwal, D., Sharma, D., & Saxena, A. B. (2023). Adoption of Artificial Intelligence (AI) For Development of Smart Education as the Future of a Sustainable Education System. Journal of Artificial Intelligence, Machine Learning and Neural Network (JAIMLNN), 3(6).

Alam, A. (2021, November). Possibilities and apprehensions in the landscape of artificial intelligence in education. In 2021 International Conference on Computational Intelligence and Computing Applications (ICCICA) (pp. 1-8). IEEE.

AlSadrani, B., Alzyoudi, M., Alsheikh, N., & Elshazly, E. E. (2020). The digital divide in inclusive classrooms. International Journal of Learning, Teaching and Educational Research, 19(3), 69-85.

Anfara, V. A., & Mertz, N. T. (2015). Setting the stage. Theoretical frameworks in qualitative research, 1-20.

Anwar, M. R., & Ahyarudin, H. A. (2023). AI-Powered Arabic Language Education in the Era of Society 5.0. IAIC Transactions on Sustainable Digital Innovation (ITSDI), 5(1), 50-57.

Baidoo-Anu, D., & Ansah, L. O. (2023). Education in the era of generative artificial intelligence (AI): Understanding the potential benefits of ChatGPT in promoting teaching and learning. Journal of AI, 7(1), 52-62.

Baskara, F. R. (2023, June). CHATBOTS AND PLAIGIARISM IN HIGHER EDUCATION: NAVIGATING THE ETHICAL LANDSCAPE. In Proceedings International Conference on Intercultural Humanities:" Sharing the Diversity of the Humanities Across Cultures" (p. 76). Sanata Dharma University Press.

Baskara, F. R., & Mukarto, F. X. (2023). Exploring the Implications of ChatGPT for Language Learning in Higher Education. IJELTAL (Indonesian Journal of English Language Teaching and Applied Linguistics), 7(2), 343-358.

Benmamoun, M. (2023). Reinventing International Business Education: Integrating the Power of Generative AI. AIB Insights.

Borenstein, J., & Howard, A. (2021). Emerging challenges in AI and the need for AI ethics education. AI and Ethics, 1, 61-65.

Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., ... & Amodei, D. (2020). Language models are few-shot learners. Advances in neural information processing systems, 33, 1877-1901.

Caines, A., Benedetto, L., Taslimipoor, S., Davis, C., Gao, Y., Andersen, O., ... & Buttery, P. (2023). On the application of large language models for language teaching and assessment technology. arXiv preprint arXiv:2307.08393.

Dada, S., Dalkin, S., Gilmore, B., Hunter, R., & Mukumbang, F. C. (2023). Applying and reporting relevance, richness and rigour in realist evidence appraisals: Advancing key concepts in realist reviews. Research synthesis methods, 14(3), 504-514.

Daniel, B. K. (2019). Big Data and data science: A critical review of issues for educational research. British Journal of Educational Technology, 50(1), 101-113.

Deguchi, A., Hirai, C., Matsuoka, H., Nakano, T., Oshima, K., Tai, M., & Tani, S. (2020). What is society 5.0. Society, 5(0), 1-24.

Devlin, J., Chang, M. W., Lee, K., & Toutanova, K. (2018). Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.

Dingli, A., & Caruana Montaldo, L. (2019). Human Computer Interaction in Education. In HCI International 2019-Posters: 21st International Conference, HCII 2019, Orlando, FL, USA, July 26–31, 2019, Proceedings, Part III 21 (pp. 226-229). Springer International Publishing.

Divito, C. B., Katchikian, B. M., Gruenwald, J. E., & Burgoon, J. M. (2023). The tools of the future are the challenges of today: The use of ChatGPT in problem-based learning medical education. Medical Teacher, 1-3.

Draxler, F., Buschek, D., Tavast, M., Hämäläinen, P., Schmidt, A., Kulshrestha, J., & Welsch, R. (2023). Gender, age, and technology education influence the adoption and appropriation of LLMs. arXiv preprint arXiv:2310.06556.

Eager, B., & Brunton, R. (2023). Prompting higher education towards AI-augmented teaching and learning practice. Journal of University Teaching & Learning Practice, 20(5), 02.

Ean Heng, L., Pei Voon, W., A. Jalil, N., Lee Kwun, C., Chee Chieh, T., & Fatiha Subri, N. (2021, February). Personalisation of Learning Content in Learning Management System. In 2021 10th International Conference on Software and Computer Applications (pp. 219-223).

Eynon, R., & Young, E. (2021). Methodology, legend, and rhetoric: The constructions of AI by academia, industry, and policy groups for lifelong learning. Science, Technology, & Human Values, 46(1), 166-191.

Fink, A. (2019). Conducting research literature reviews: From the internet to paper. Sage publications.

Floridi, L., Cowls, J., Beltrametti, M., Chatila, R., Chazerand, P., Dignum, V., ... & Vayena, E. (2021). An ethical framework for a good AI society: Opportunities, risks, principles, and recommendations. Ethics, governance, and policies in artificial intelligence, 19-39.

Floridi, L., Cowls, J., King, T. C., & Taddeo, M. (2021). How to design AI for social good: seven essential factors. Ethics, Governance, and Policies in Artificial Intelligence, 125-151.

Fuchs, K. (2023, May). Exploring the opportunities and challenges of NLP models in higher education: is Chat GPT a blessing or a curse? In Frontiers in Education (Vol. 8, p. 1166682). Frontiers.

Fukuyama, M. (2018). Society 5.0: Aiming for a new human-centered society. Japan Spotlight, 27(5), 47-50.

Gan, W., Qi, Z., Wu, J., & Lin, J. C. W. (2023, December). Large language models in education: Vision and opportunities. In 2023 IEEE International Conference on Big Data (BigData) (pp. 4776-4785). IEEE.

Gilson, A., Safranek, C. W., Huang, T., Socrates, V., Chi, L., Taylor, R. A., & Chartash, D. (2023). How does ChatGPT perform on the United States medical licensing examination? The implications of large language models for medical education and knowledge assessment. JMIR Medical Education, 9(1), e45312.

Guan, C., Mou, J., & Jiang, Z. (2020). Artificial intelligence innovation in education: A twenty-year data-driven historical analysis. International Journal of Innovation Studies, 4(4), 134-147.

Hamburg, I., & Lütgen, G. (2019). Digital divide, digital inclusion and inclusive education. Advances in Social Sciences Research Journal, 6(4).

Helbing. D. and Hausladen, C.I. (2022), Socio-Economic Implications of the Digital Revolution, in: Chen, P., Elsner, W. and Pyka, A.(eds.), Handbook of Complexity Economics, Routledge, London, New York.

Holmes, W., Porayska-Pomsta, K., Holstein, K., Sutherland, E., Baker, T., Shum, S. B., ... & Koedinger, K. R. (2021). Ethics of AI in education: Towards a community-wide framework. International Journal of Artificial Intelligence in Education, 1-23.

Holstein, K., & Aleven, V. (2022). Designing for human–AI complementarity in K-12 education. AI Magazine, 43(2), 239-248.

Hong, Q. N. (2018). Revision of the Mixed Methods Appraisal Tool (MMAT): A mixed methods study. McGill University (Canada).

Hong, Y., Nguyen, A., Dang, B., & Nguyen, B. P. T. (2022, July). Data Ethics Framework for Artificial Intelligence in Education (AIED). In 2022 International Conference on Advanced Learning Technologies (ICALT) (pp. 297-301). IEEE.

Huang, L. (2023). Ethics of artificial intelligence in education: Student privacy and data protection. Science Insights Education Frontiers, 16(2), 2577-2587.

Ifenthaler, D., & Schumacher, C. (2023). Reciprocal issues of artificial and human intelligence in education. Journal of Research on Technology in Education, 55(1), 1-6.

Jobin, A., Ienca, M., & Vayena, E. (2019). The global landscape of AI ethics guidelines. Nature machine intelligence, 1(9), 389-399.

Kadaruddin, K. (2023). Empowering education through generative AI: Innovative instructional strategies for tomorrow’s learners. International Journal of Business, Law, and Education, 4(2), 618-625.

Kaddour, J., Harris, J., Mozes, M., Bradley, H., Raileanu, R., & McHardy, R. (2023). Challenges and applications of large language models. arXiv preprint arXiv:2307.10169.

Kaplan, A., & Haenlein, M. (2019). Siri, Siri, in my hand: Who’s the fairest in the land? On the interpretations, illustrations, and implications of artificial intelligence. Business horizons, 62(1), 15-25.

Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.

Krishna, R., Lee, D., Fei-Fei, L., & Bernstein, M. S. (2022). Socially situated artificial intelligence enables learning from human interaction. Proceedings of the National Academy of Sciences, 119(39), e2115730119.

Kung, T. H., Cheatham, M., Medenilla, A., Sillos, C., De Leon, L., Elepaño, C., ... & Tseng, V. (2023). Performance of ChatGPT on USMLE: Potential for AI-assisted medical education using large language models. PLoS digital health, 2(2), e0000198.

Lacey, F. M., Matheson, L., & Jesson, J. (2011). Doing your literature review: Traditional and systematic techniques. Doing Your Literature Review, 1-192.

Li, C., Zhang, M., Mei, Q., Wang, Y., Hombaiah, S. A., Liang, Y., & Bendersky, M. (2023). Teach LLMs to Personalise--An Approach inspired by Writing Education. arXiv preprint arXiv:2308.07968.

Limo, F. A. F., Tiza, D. R. H., Roque, M. M., Herrera, E. E., Murillo, J. P. M., Huallpa, J. J., ... & Gonzáles, J. L. A. (2023). Personalised tutoring: ChatGPT as a virtual tutor for personalised learning experiences. Social Space, 23(1), 293-312.

Lodge, J. M., Thompson, K., & Corrin, L. (2023). Mapping out a research agenda for generative artificial intelligence in tertiary education. Australasian Journal of Educational Technology, 39(1), 1-8.

Mantoro, T., Tarigan, W. H., & Ayu, M. A. (2022, July). Analysis of the Metaverse in Society 5.0 for Learning Systems using Meta. In 2022 IEEE 8th International Conference on Computing, Engineering and Design (ICCED) (pp. 1-5). IEEE.

Melo, P., Silveira, R. C., & de Barros, M. J. F. (2022). Might Digital Revolution be a threat for Employability?. Journal on Innovation and Sustainability RISUS, 13(4), 4-10.

Menon, J. M. L., Struijs, F., & Whaley, P. (2022). The methodological rigour of systematic reviews in environmental health. Critical Reviews in Toxicology, 52(3), 167-187.

Merriam, S. B., & Tisdell, E. J. (2015). Qualitative research: A guide to design and implementation. John Wiley & Sons.

Meyer, J. G., Urbanowicz, R. J., Martin, P. C., O’Connor, K., Li, R., Peng, P. C., ... & Moore, J. H. (2023). ChatGPT and large language models in academia: opportunities and challenges. BioData Mining, 16(1), 20.

Mozer, M. C., Wiseheart, M., & Novikoff, T. P. (2019). Artificial intelligence to support human instruction. Proceedings of the National Academy of Sciences, 116(10), 3953-3955.

Narvaez Rojas, C., Alomia Peñafiel, G. A., Loaiza Buitrago, D. F., & Tavera Romero, C. A. (2021). Society 5.0: A Japanese concept for a superintelligent society. Sustainability, 13(12), 6567.

Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221-4241.

Opara, E., Mfon-Ette Theresa, A., & Aduke, T. C. (2023). ChatGPT for teaching, learning and research: Prospects and challenges. Opara Emmanuel Chinonso, Adalikwu Mfon-Ette Theresa, Tolorunleke Caroline Aduke (2023). ChatGPT for Teaching, Learning and Research: Prospects and Challenges. Glob Acad J Humanit Soc Sci, 5.

Orozova, D., & Popchev, I. (2020, June). Cyber-Physical-Social Systems for Big Data. In 2020 21st International Symposium on Electrical Apparatus & Technologies (SIELA) (pp. 1-4). IEEE.

Osanloo, A., & Grant, C. (2016). Understanding, selecting, and integrating a theoretical framework in dissertation research: Creating the blueprint for your "house". Administrative issues journal: connecting education, practice, and research, 4(2).

Pedro, F., Subosa, M., Rivas, A., & Valverde, P. (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development.

Pencarelli, T. (2020). The digital revolution in the travel and tourism industry. Information Technology & Tourism, 22(3), 455-476.

Rahimzadeh, V., Kostick-Quenet, K., Blumenthal Barby, J., & McGuire, A. L. (2023). Ethics education for healthcare professionals in the era of chatGPT and other large language models: Do we still need it?. The American Journal of Bioethics, 23(10), 17-27.

Rao, A., Khandelwal, A., Tanmay, K., Agarwal, U., & Choudhury, M. (2023). Ethical Reasoning over Moral Alignment: A Case and Framework for In-Context Ethical Policies in LLMs. arXiv preprint arXiv:2310.07251.

Roberts, K., Dowell, A., & Nie, J. B. (2019). Attempting rigour and replicability in thematic analysis of qualitative research data; a case study of codebook development. BMC medical research methodology, 19, 1-8.

Rosak-Szyrocka, J., Apostu, S. A., Ali Turi, J., & Tanveer, A. (2022). University 4.0 Sustainable Development in the Way of Society 5.0. Sustainability, 14(23), 16043.

Rotolo, D., Hicks, D., & Martin, B. R. (2015). What is an emerging technology?. Research policy, 44(10), 1827-1843.

Sallam, M. (2023, March). ChatGPT utility in healthcare education, research, and practice: systematic review on the promising perspectives and valid concerns. In Healthcare (Vol. 11, No. 6, p. 887). MDPI.

Salmela-Aro, K., & Motti-Stefanidi, F. (2022). Digital Revolution and Youth. European Psychologist.

Schiff, D. (2021). Out of the laboratory and into the classroom: the future of artificial intelligence in education. AI & society, 36(1), 331-348.

Seo, K., Tang, J., Roll, I., Fels, S., & Yoon, D. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. International journal of educational technology in higher education, 18(1), 1-23.

Shiva, A., & Khatri, P. (2023). Digital Revolution. JOURNAL OF CONTENT COMMUNITY AND COMMUNICATION.

Short, C. R., & Shemshack, A. (2023). Personalised Learning. EdTechnica, 197-203.

Smuts, H., & Van der Merwe, A. (2022). Knowledge management in society 5.0: A sustainability perspective. Sustainability, 14(11), 6878.

Snyder, H. (2019). Literature review as a research methodology: An overview and guidelines. Journal of business research, 104, 333-339.

Soelistiono, S. (2023). Educational Technology Innovation: AI-Integrated Learning System Design in AILS-Based Education. Influence: international journal of science review, 5(2), 470-480.

T, I. (2023). Analysing the Impacts of Artificial Intelligence on Education. IAA JOURNAL OF EDUCATION.

Tanveer, M., Hassan, S., & Bhaumik, A. (2020). Academic policy regarding sustainability and artificial intelligence (AI). Sustainability, 12(22), 9435.

Tetzlaff, L., Schmiedek, F., & Brod, G. (2021). Developing personalised education: A dynamic framework. Educational Psychology Review, 33, 863-882.

Torraco, R. J. (2016). Writing integrative literature reviews: Using the past and present to explore the future. Human resource development review, 15(4), 404-428.

Vieira, R., Monteiro, P., Azevedo, G., & Oliveira, J. (2023, June). Society 5.0 and Education 5.0: A Critical Reflection. In 2023 18th Iberian Conference on Information Systems and Technologies (CISTI) (pp. 1-6). IEEE.

Wang, N., Tonko, P., Ragav, N., Chungyoun, M., & Plucker, J. (2023). A perspective on k-12 ai education. Technology & Innovation.

Wang, V. (Ed.). (2020). Handbook of research on ethical challenges in higher education leadership and administration. IGI Global.

Wang, Z., Huang, S., Liu, Y., Wang, J., Song, M., Zhang, Z., ... & Zhang, Q. (2023). Democratising reasoning ability: Tailored learning from large language model. arXiv preprint arXiv:2310.13332.

Wei Y., Hashim H., Lai S.H., Chong K., Huang Y., Ahmed A.N., Sherif M., El-Shafie A. (2024). Comparative Analysis of Artificial Intelligence Methods for Streamflow Forecasting. IEEE Access, 12, 10865-10885

Yan, L., Sha, L., Zhao, L., Li, Y., Martinez‐Maldonado, R., Chen, G., ... & Gašević, D. (2024). Practical and ethical challenges of large language models in education: A systematic scoping review. British Journal of Educational Technology, 55(1), 90-112.

Yang, H., Liu, X. Y., & Wang, C. D. (2023). FinGPT: Open-Source Financial Large Language Models. arXiv preprint arXiv:2306.06031.

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.

Yue, M., Jong, M. S. Y., & Dai, Y. (2022). Pedagogical design of K-12 artificial intelligence education: A systematic review. Sustainability, 14(23), 15620.

Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education–where are the educators? International Journal of Educational Technology in Higher Education, 16(1), 1-27.

Zhang, X. (2023). The Digital Divide: Class and Equality Education. In SHS Web of Conferences (Vol. 157, p. 04027). EDP Sciences.

Zhao, W. X., Zhou, K., Li, J., Tang, T., Wang, X., Hou, Y., ... & Wen, J. R. (2023). A survey of large language models. arXiv preprint arXiv:2303.18223.




DOI: https://doi.org/10.24294/jipd.v8i12.6385

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