Integration of artificial intelligence in the teaching of Social Sciences in higher education
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2024-07-31
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Abstract
The integration of artificial intelligence (AI) in the teaching of social sciences in higher education has become a crucial issue due to the rapid technological evolution and access to digital tools, which allow for more personalized and effective learning. This study aims to analyze the trends, opportunities and challenges of AI in this context. Methodologically, a comprehensive review of articles from the Scopus database of the year 2024 was conducted, selecting 436 relevant articles that were analyzed qualitatively and quantitatively. The results indicate that 58.3% of the publications are scientific articles, followed by conference proceedings and reviews, underlining the preference for validation and replicability in knowledge dissemination. The discussion reveals that, despite the potential of AI to personalize education and automate administrative tasks, there are significant barriers such as lack of adequate technological infrastructure and resistance to change among educators. Finally, it is concluded that, for effective adoption of AI in higher education, it is essential to invest in infrastructure, train teachers, and develop robust ethical and regulatory frameworks that ensure fairness and privacy of student data.
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How to Cite
Piedra-Castro, W. I., Cajamarca-Correa, M. A., Burbano-Buñay, E. S., & Moreira-Alcívar, E. F. (2024). Integration of artificial intelligence in the teaching of Social Sciences in higher education. Journal of Economic and Social Science Research, 4(3), 105-126. https://doi.org/10.55813/gaea/jessr/v4/n3/123
References
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Baker, R. S., & Inventado, P. S. (2014). Educational data mining and learning analytics. In J. A. Larusson & B. White (Eds.), Learning analytics (pp. 61-75). Springer.
Baker, T., & Smith, L. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta. https://www.nesta.org.uk/report/educ-ai-tion-rebooted/
Binns, R. (2018). Fairness in machine learning: Lessons from political philosophy. Proceedings of the 2018 Conference on Fairness Accountability and Transparency, 149-159. https://doi.org/10.1145/3287560.3287591
Bornmann, L. (2011). Scientific peer review. Annual Review of Information Science and Technology, 45(1), 197-245. https://doi.org/10.1002/aris.2011.1440450112
Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
Carter, S., McCartney, M., & Wu, J. (2018). Developing a national AI strategy: Key considerations for policymakers. Journal of AI Policy and Strategy, 2(1), 45-62. https://doi.org/10.1145/3175883.3175890
De Jong, T., Sotiriou, S., & Gillet, D. (2020). Innovations in STEM education: The go-lab federation of online labs. Smart Learning Environments, 7(1), 1-12. https://doi.org/10.1186/s40561-020-00132-1
Dede, C. (2020). The role of digital technologies in deeper learning. Journal of Research on Technology in Education, 52(3), 309-322. https://doi.org/10.1080/15391523.2020.1728445
Fernández, S. P., & Gómez, H. A. (2019). La inteligencia artificial en la formación académica: Evaluación de impactos. Estudios sobre Educación Superior, 12(3), 77-92. https://doi.org/10.2089/edu2019
García, M. E., & López, J. A. (2020). Uso de la inteligencia artificial en la educación superior: Retos y oportunidades. Revista Iberoamericana de Tecnología Educativa, 23(2), 89-104. https://doi.org/10.3390/educ2023
Garousi, V., Felderer, M., & Mäntylä, M. V. (2016). The need for multivocal literature reviews in software engineering: Complementing systematic literature reviews with grey literature. Proceedings of the 38th International Conference on Software Engineering (pp. 1000-1011). https://doi.org/10.1145/2884781.2884827
Grant, M. J., & Booth, A. (2009). A typology of reviews: An analysis of 14 review types and associated methodologies. Health Information & Libraries Journal, 26(2), 91-108. https://doi.org/10.1111/j.1471-1842.2009.00848.x
Hernández, P. M., & Morales, L. E. (2020). Implementación de tecnologías de inteligencia artificial en universidades: Un estudio de caso en Colombia. Revista Latinoamericana de Tecnología Educativa, 27(3), 108-123. https://doi.org/10.3029/edu2020
Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign.
Huang, T., Zhu, X., & Wu, C. (2019). Global trends in artificial intelligence research: A bibliometric analysis. Journal of Artificial Intelligence Research, 65(1), 189-210. https://doi.org/10.1613/jair.1.11384
Kitchin, R. (2014). The data revolution: Big data, open data, data infrastructures and their consequences. SAGE Publications.
Li, Y., Chen, Y., & Xu, Z. (2020). China's AI development: Current status and future trends. AI & Society, 35(3), 459-478. https://doi.org/10.1007/s00146-019-00927-8
Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson.
Luthra, P., & Mahajan, R. (2020). Artificial intelligence in education: An overview. International Journal of Advanced Science and Technology, 29(3), 143-148. https://doi.org/10.2139/ssrn.3569304
Mariani, M., Baggio, R., & Fuchs, M. (2019). Business intelligence and big data in hospitality and tourism: A systematic literature review. International Journal of Contemporary Hospitality Management, 31(2), 447-474. https://doi.org/10.1108/IJCHM-07-2017-0461
Martínez, R. J., & Pérez, L. C. (2021). Aplicaciones de la IA en la enseñanza universitaria: Un enfoque práctico. Educación y Tecnología, 15(4), 132-145. https://doi.org/10.1037/edu2021
Means, B., Bakia, M., & Murphy, R. (2014). Learning online: What research tells us about whether, when and how. Routledge.
Piech, C., Bassen, J., Huang, J., Ganguli, S., Sahami, M., Guibas, L. J., & Hanrahan, P. (2016). Deep knowledge tracing. In Advances in Neural Information Processing Systems (pp. 505-513). https://papers.nips.cc/paper/2015/file/bac9162b47c1358b2ff512c7e66c9933-Paper.pdf
Rodríguez, T. C., & Rivera, F. A. (2022). Desafíos de la inteligencia artificial en la educación superior en América Latina. Revista de Innovación Educativa, 18(1), 45-59. https://doi.org/10.3094/edu2022
Russell, S., & Norvig, P. (2020). Artificial intelligence: A modern approach (4th ed.). Pearson.
Russell, S., Dewey, D., & Tegmark, M. (2015). Research priorities for robust and beneficial artificial intelligence. AI Magazine, 36(4), 105-114. https://doi.org/10.1609/aimag.v36i4.2577
Selbst, A. D., Boyd, D., Friedler, S. A., Venkatasubramanian, S., & Vertesi, J. (2019). Fairness and abstraction in sociotechnical systems. Proceedings of the Conference on Fairness Accountability and Transparency, 59-68. https://doi.org/10.1145/3287560.3287598
Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Polity.
Sun, T. Q., & Medaglia, R. (2019). Mapping the challenges of artificial intelligence in the public sector: Evidence from public healthcare. Government Information Quarterly, 36(2), 368-383. https://doi.org/10.1016/j.giq.2018.09.008
Topol, E. J. (2019). Deep medicine: How artificial intelligence can make healthcare human again. Basic Books.
Vincent-Lancrin, S., Urgel, J., Kar, S., & Jacotin, G. (2019). Measuring innovation in education: A journey to the future. OECD Education Working Papers No. 255. OECD Publishing. https://doi.org/10.1787/20769679
Vincenzo, R. (2018). The role of university research in the development of AI. Journal of AI Research & Development, 4(2), 98-112. https://doi.org/10.1016/j.jaird.2018.02.001
Williamson, B., & Eynon, R. (2020). Historical threads, missing links, and future directions in AI in education. Learning Media and Technology, 45(3), 223-235.
Woolf, B. P. (2020). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e-learning. Morgan Kaufmann.
Xiao, Y., & Watson, M. (2019). Guidance on conducting a systematic literature review. Journal of Planning Education and Research, 39(1), 93-112. https://doi.org/10.1177/0739456X17723971
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(39). https://doi.org/10.1186/s41239-019-0173-8
Zhang, Q., Zhao, Y., & Huang, C. (2021). Trends and research foci in artificial intelligence in education: A bibliometric review. Journal of Educational Technology Development and Exchange, 14(1), 39-60. https://doi.org/10.18785/jetde.1401.03