THE FUTURE OF EDUCATION: IMPORTANCE OF ETHICAL FRAMEWORKS AND CULTURAL SENSITIVITY
Abstract
With the advent of artificial intelligence (AI) and advanced platforms such as ChatGPT, we are witnessing profound changes in the educational context that are increasingly becoming the focus of current scientific research and discourse. These technologies have the potential to completely redefine the future of education, introducing the possibility of personalized learning and the adaptation of teaching materials to the specific needs and abilities of each student. It also opens up the possibility of overcoming barriers in education in cases where there is no access to traditional education. However, in order to fully exploit the possibilities of these innovations, it is necessary to improve the level of technical literacy and develop the corresponding skills and competencies. Cultural contexts can significantly influence how students from different backgrounds understand and use AI systems. Existing biases in AI, stemming from predominantly Western-centric datasets, may indirectly promote stereotypes and lead to bias in learning outcomes. The use of artificial intelligence in education opens up a wide range of possibilities for improving the teaching process, but its application requires detailed analysis and consideration of ethical aspects, as well as issues of user security and privacy, as well as checking for bias that may be embedded in (AI) algorithms. A strategy that includes careful adoption and ethical use of artificial intelligence can bring significant progress in training and learning methods, and positively influence the development of educational standards and increase the quality of education. Only through informed and carefully monitored development, based on diversity, cultural sensitivity and equal global opportunities, is there hope for a true evolution of education with the help of new technologies.
References
Banks: 2016. Banks, James A. Cultural diversity and education. Routledge.
Boyd, 2014. Boyd, Danah. It's complicated: The social lives of networked teens. Yale University Press.
Brown, et al.: 2020. Brown, Tom et al. Language models are few-shot learners. arXiv preprint arXiv:2005.14165.
Castells: 2013. Castells, Manuel. Communication power. Oxford University Press.
Daniels: 2013. Daniels, Jessie. Race and racism in internet studies: A review and critique. New Media & Society, 15(5), 695-719.
Fadel-Trilling: 2017. Fadel, Charles-Bernie Trilling. 21st Century Skills: Learning for life in our times. John Wiley & Sons.
Firnando: 2024. Firnando, Hero Gefthi. Tech Revolution with a Soul: Navigating Digital Transformation through Islamic Ethics. Proceedings of International Conference on Education. http://icon.ftk.uinjambi.ac.id/index.php/icon/article/view/84
Henze, et al.: 2011, September. Henze Niels, Enrico Rukzio, and Susanne Boll. 100,000,000 taps: analysis and improvement of touch performance in the large. In Proceedings of the 13th international conference on Human computer interaction with mobile devices and services (pp. 133-142).
Jenkins, Mizuko: 2015. Jenkins, Henry, and Mizuko Ito. Participatory culture in a networked era: A conversation on youth, learning, commerce, and politics. John Wiley & Sons.
Kukulska-Hulme: 2019. Agnes Kukulska-Hulme. Mobile-assisted language learning. The Encyclopedia of Applied Linguistics.
Liu, Zhong: 2024. Liu, Xiaofan, and Baichang Zhong. What to consider before incorporating generative AI into schools? AI & Society. https://link.springer.com/article/10.1007/s00146-024-01872-9
Милутиновић-Балтезаревић Б. 2021. Оливера Милутиновић, Боривоје Балтезаревић. Развој технологије и интегративни приступ истраживању онлајн образовања, Баштина, св. 53, 477-485.
Mitchell: 1997. Mitchell, Tom M. Machine learning. McGraw Hill.
O'Neil: 2016. O'neil, Cathy. Weapons of math destruction: How big data increases inequality and threatens democracy. Broadway Books.
Rainie-Wellman: 2012. Rainie, Harrison, and Barry Wellman. Networked: The new social operating system. MIT press.
Selwyn: 2016. Selwyn, Neil. Education and technology: Key issues and debates. Bloomsbury Publishing.
Siemens: 2004. Siemens, George. Connectivism: A learning theory for the digital age. International Journal of Instructional Technology and Distance Learning, 2. http://www.itdl.org/Journal/Jan_05/article01.htm
Smith: 2013. Smith, Linda Tuhiwai. Decolonizing methodologies: Research and indigenous peoples. Zed Books Ltd.
Vaswani, et al.: 2017. Vaswani, Ashish, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N. Gomez, Łukasz Kaiser, and Illia Polosukhin. Attention is all you need. Advances in neural information processing systems, 30.
Warschauer: 2004. Warschauer, Mark. Technology and social inclusion: Rethinking the digital divide. MIT press.
Weller: 2011. Weller, Martin. The digital scholar: How technology is transforming scholarly practice. Bloomsbury Publishing.
West-Whittaker, Crawford: 2019. West, Sarah Myers, Meredith Whittaker, and Kate Crawford. Discriminating systems: Gender, race, and power in AI. AI Now Institute.
Zhang-Wallace: 2017. Zhang, Ye, and Byron Wallace. A Sensitivity Analysis of (and Practitioners' Guide to) Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 1: Long Papers) Convolutional Neural Networks for Sentence Classification.