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Eğitimde Yapay Zekâ Uygulamalarının Potansiyel Yararları ve Riskleri

Yıl 2024, Cilt: 13 Sayı: 2, 232 - 244, 16.04.2024
https://doi.org/10.14686/buefad.1416087

Öz

Yapay zekâ (AI) teknolojileri hızla gelişmekte ve yaşamın tüm alanlarında köklü dönüşümlere yol açmaktadır. Özellikle, ChatGPT gibi generative AI sistemlerinin yaygınlaşması bu dönüşümü çok daha dramatik boyutlara taşımaktadır. Bu bağlamda en kapsamlı etki eğitim sistemlerinde gerçekleşmektedir. Eğitim sistemleri bir taraftan, bu tip sistemlerin işgücü piyasasında yaygınlaşması ile mesleklerde yaşanan beceri değişikliklerine hızla cevap üreterek eğitimi yeniden yapılandırma zorunluluğuyla karşı karşıyadır. Diğer taraftan, bu sistemlerin eğitime dâhil edilip edilmeyeceği, edilecekse nasıl ve ne derece dâhil edileceği, AI sistemlerinin yol açacağı etik sorunlara nasıl cevap üretilebileceği gibi meydan okuyucu sorularla yüzleşmektedir. Bu çalışmada bu kapsamda AI sistemlerin eğitim sistemlerinde kullanılmasının potansiyel faydaları ve olası riskleri öğrenci, öğretmen ve eğitim yöneticileri açısından değerlendirilmektedir. Bu nedenle bu çalışmada, AI sistemlerinin eğitimde nasıl kullanılabileceği olası potansiyelleri ve yol açabileceği riskler ele alınmaktadır. AI sistemlerinin sağlayacağı faydayı maksimum yaparken yol açacağı etik ve diğer sorunların etkilerini hafifletmeye yönelik politika önerileri geliştirilmektedir. Ayrıca, tüm eğitim paydaşları açısından AI okuryazarlığının artırılması, AI sistemlerinin sağlayacağı faydaları kadar yol açacağı etik ve diğer sorunların da farkındalığına yol açacağı ve böylece bu sistemlerin eğitimde faydalarını artırırken zararlarının hafifletilmesinin mümkün olabileceği vurgulanmaktadır.

Kaynakça

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Potential Benefits and Risks of Artificial Intelligence in Education

Yıl 2024, Cilt: 13 Sayı: 2, 232 - 244, 16.04.2024
https://doi.org/10.14686/buefad.1416087

Öz

Artificial Intelligence (AI) technologies are rapidly advancing and causing profound transformations in all aspects of life. In particular, the widespread adoption of generative AI systems like ChatGPT is taking this transformation to even more dramatic dimensions. In this context, the most comprehensive impact is observed in educational systems. Educational systems, on one hand, are faced with the urgent need to rapidly restructure education in response to skill changes in professions caused by the proliferation of such systems in the labor market. On the other hand, challenging questions arise about whether and to what extent these systems should be integrated into education, how they should be integrated if at all, and how ethical issues arising from AI systems can be addressed. This study evaluates the potential benefits and possible risks of using AI systems in educational systems from the perspectives of students, teachers, and education administrators. Therefore, the study discusses the potential uses of AI systems in education, as well as the risks they may pose. Policy recommendations are developed to maximize the benefits of AI systems while mitigating the ethical and other issues they may cause. Additionally, the study emphasizes the importance of increasing AI literacy for all education stakeholders. It suggests that raising awareness of both the benefits and ethical issues caused by AI systems can contribute to enhancing the benefits of these systems in education while minimizing their potential harms.

Kaynakça

  • Acemoğlu, D., & Restrepo, P. (2018). Artificial intelligence, automation and work. NBER Working Paper 24196. National Bureau of Economic Research, Cambridge.
  • Aghion, P., & Howitt, P. (1990). A model of growth through creative destruction. NBER Working Paper 3223. National Bureau of Economic Research, Cambridge.
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  • Al-Worafi, Y. M., Hermansyah, A., Goh, K.W., & Ming, L.C. (2023). Artificial intelligence use in university: Should we ban ChatGPT? Preprints.org 2023.
  • Aquino, Y. S. J. (2023). Making decisions: Bias in artificial intelligence and data-driven diagnostic tools. Australian Journal of General Practice, 52(7), 439-442. https://doi.org/10.31128/ajgp-12-22-6630
  • Arntz, M., Gregory, T., & Zierahn, U. (2016). The risk of automation for jobs in OECD countries: A comparative analysis. Social, Employment and Migration Working Paper 189. OECD Publishing, Paris. https://doi.org/10.1787/1815199X
  • Atlas, S. (2023). ChatGPT for higher education and professional development: A guide to conversational AI. Available online: https://digitalcommons.uri.edu/cba_facpubs/548.
  • Babitha, M. M., & Sushma, C. (2022). Trends of artificial intelligence for online exams in education. International Journal of Early Childhood Special Education, 14, 2457–2463. doi: 10.9756/INT-JECSE/V14I1.290
  • Bartelsman, E., Haltiwanger, J., & Scarpetta, S. (2004). Microeconomic evidence of creative destruction in industrial and developing countries. World Bank, Washington DC.
  • Bašic, Ž., Banovac, A., Kružic, I., & Jerkovic, I. (2023). Better by you, better than me, ChatGPT3 as writing assistance in students essays. arXiv: 2302.04536.
  • Bhutoria, A. (2022). Personalized education and Artificial Intelligence in the United States, China, and India: A systematic review using a Human-In-The-Loop model. Computers and Education: Artificial Intelligence, 3, 100068.
  • Boyd, D., & Crawford, K. (2012). Critical questions for big data: Provocations for a cultural, technological, and scholarly phenomenon. Information, Communication & Society, 15(5), 662–679. https://doi.org/10.1080/1369118X.2012.678878
  • Brinkmann, L., Baumann, F., Bonnefon, J. F. et al. (2023). Machine culture. Nature Human Behavior, 7(11), 1855-1868. https://doi.org/10.1038/s41562-023-01742-2
  • Bulathwela, S., Pérez-Ortiz, M., Holloway, C., Cukurova, M., & Shawe-Taylor, J. (2024). Artificial intelligence alone will not democratise education: On educational inequality, techno-solutionism and inclusive tools. Sustainability. 16(2), 781.
  • Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264-75278. https://doi.org/10.1109/ACCESS.2020.2988510
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  • Mehrabanian, M., & Zariat, Y. (2023). ChatGPT passes anatomy exam. Br Dent J, 235, 295.
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  • Mogali, S. R. (2023). Initial impressions of ChatGPT for anatomy education. Anatomical Sciences Education, 1–4. https://doi.org/10.1002/ase.2261
  • Morgan, H. (2021). Conducting a qualitative document analysis. The Qualitative Report, 27(1), 64-77. https://doi.org/10.46743/2160-3715/2022.5044
  • Naik, R. R., Landge, M. B., & Mahender, C. N. (2015). A review on plagiarism detection tools. International Journal of Computer Applications, 125, 16–22.
  • Nazari, N., Shabbir, M. S., & Setiawan, R. (2021). Application of artificial intelligence powered digital writing assistant in higher education: Randomized controlled trial. Heliyon, 7(5), e07014. https://doi.org/10.1016/j.heliyon.2021.e07014
  • Newman, M., Gough, D. (2020). Systematic reviews in educational research: Methodology, perspectives and application. In: Zawacki-Richter, O., Kerres, M., Bedenlier, S., Bond, M., Buntins, K. (eds) Systematic reviews in educational research. Springer VS, Wiesbaden.
  • O’Neil, C. (2016). Weapons of math destruction: How big data increases inequality and threatens democracy. Crown Books.
  • Özer, M., Suna, H. E., Çelik, Z., & Aşkar, P. (2020). Covid-19 salgını dolayısıyla okulların kapanmasının eğitimde eşitsizlikler üzerine etkisi. İnsan ve Toplum, 10(4), 217-246. https://doi.org/10.12658/M0611
  • Özer, M., & Suna, H. E. (2020). Covid-19 salgını ve eğitim. In M. Şeker, A. Özer & C. Korkut (Eds.), Küresel salgının anatomisi: İnsan ve toplumun geleceği (pp. 171-192). TÜBA.
  • Özer, M., Suna, H. E., Perc, M., Şensoy, S., & İlikhan, S. U. (2022). Turkey’s transition to face-to-face schooling during the COVID-19 pandemic. Turkish Journal of Medical Sciences, 52, 529-540. https://doi.org/10.55730/1300-0144.5343
  • Özer, M. (2023a). The Matthew effect in Turkish education system. Bartın University Journal of Faculty of Education, 12(4), 704-712. https://doi.org/10.14686/buefad.1359312
  • Özer, M. (2023b). Matta etkisi. Uluslararası Yönetim İktisat ve İşletme Dergisi, 19(4), 974-984. https://doi.org/10.17130/ijmeb.1374798
  • Pajarinen, M., Rouvinen, P., & Ekeland, A. (2015). Computerization threatens one-third of Finnish and Norwegian employment. ETLA Brief, 34.
  • Peng, H., Ma, S., & Spector, J. M. (2019). Personalized adaptive learning: An emerging pedagogical approach enabled by a smart learning environment. Smart Learning Environments, 6(1), 1-14.
  • Perc, M., Özer, M., & Hojnik, J. (2019). Social and juristic challenges of artificial intelligence. Palgrave Communications, 5, 61. https://doi.org/10.1057/s41599-019-0278-x
  • Piano, S. L. (2020). Ethical principles in machine learning and artificial intelligence: cases from the field and possible ways forward. Humanities & Social Sciences Communications, 7, 9. https://doi.org/10.1057/s41599-020-0501-9
  • Rahwan, I. (2018). Society-in-the-loop: Programming the algorithmic social contract. Ethics and Information Technology, 20(1), 5-14.
  • Ramesh, D., & Sanampudi, S. K. (2022). An automated essay scoring systems: a systematic literature review. Artif Intell Rev, 55, 2495–2527.
  • Rubinstein, Y. (2020). Uneasy listening: Towards a hauntology of AI-generated 835 music. Resonance: The Journal of Sound and Culture, 1, 77–93. https://doi.org/10.1525/res.2020.1.1.77
  • Rudolph, J., Tan, S., Tan, S. C. (2023). ChatGPT: Bullshit spewer or the end of traditional assessments in higher education? Journal of Applied Learning & Teaching, 6(1), 1-22. https://doi.org/10.37074/jalt.2023.6.1.9
  • Seo, K., Tang, J., Roll, I. et al. (2021). The impact of artificial intelligence on learner–instructor interaction in online learning. Int J Educ Technol High Educ, 18, 54. https://doi.org/10.1186/s41239-021-00292-9
  • Shiri, A. (2023). ChatGPT and academic integrity. Information Matters, 3(2), 1-5. https://doi.org/10.2139/ssrn.4360052
  • Skavronskaya, L., Hadinejad, A., & Cotterell, D. (2023). Reversing the threat of artificial intelligence to opportunity: A discussion of ChatGPT in tourism education. Journal of Teaching in Travel & Tourism, 23, 253–258.. https://doi.org/1080/15313220.2023.2196658
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  • Suna, H. E., Tanberkan, H., Gür, B.S., Perc, M., & Özer, M. (2020). Socioeconomic status and school type as predictors of academic achievement. Journal of Economy Culture and Society, 61, 41-64. https://doi.org/10.26650/JECS2020-0034
  • Suna, H. E., Özer, M., Şensoy, S., Gür , B. S., Gelbal , S., & Aşkar , P. (2021). Determinants of academic achievement in Türkiye. Journal of Economy Culture and Society, 64, 143-162. https://doi.org/10.26650/JECS2021-934211
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  • Trojer, L., Ambele, R. M., Kaijage, S. F., & Dida, M. A. (2022). A review of the development trend of personalized learning technologies and its applications. International Journal of Advances in Scientific Research and Engineering, 8, 75–91. https://doi.org/10.31695/IJASRE.2022.8.11.9
  • Tsai, S. C., Chen, C. H., Shiao, Y. T., Ciou, J. S., & Wu, T. N. (2020). Precision education with statistical learning and deep learning: A case study in Taiwan. International Journal of Educational Technology in Higher Education, 17, 12. https://doi.org/10.1186/s41239-020-00186-2
  • Ulnicane, I., & Aden, A. (2023). Power and politics in framing bias in artificial intelligence policy. Review of Policy Research, 40, 665-687. https://doi.org/10.1111/ropr.12567
  • UNESCO (2019). Artificial intelligence in education: Challenges and opportunities for sustainable development. UNESCO Publishing. Retrieved from https://unesdoc.unesco.org/ark:/48223/pf0000366994
  • Villegas-Ch, W., Arias-Navarrete, A., & Palacios-Pacheco, X. (2020). Proposal of an architecture for the integration of a Chatbot with artificial intelligence in a smart campus for the improvement of learning. Sustainability, 12, 1500. https://doi.org/10.3390/su12041500
  • Wang, L., Lyu, C., Ji, T., Zhang, Z., Yu, D. et al. (2023). Document-level machine translation with large language models. arXiv:2304.02210. https://doi.org/10.18653/v1/2023.emnlp-main.1036
  • Welle, D. (2023). ChatGPT is changing education, AI experts say — but how?. https://news.abs-cbn. com/spotlight/01/25/23/chatgpt-is-changing-education-ai-experts-say-but-how
  • Yau, S., Chai, C. S., Chiu, T. K. F., Meng, H., King, I., & Yam, Y. (2022). A phenomenographic approach on teacher conceptions of teaching artificial intelligence (AI) in K-12 schools. Education and Information Technologies, 28, 1041–1064. https://doi.org/10.1007/s10639-022-11161-x
  • 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. https://doi.org/10.1186/s41239-019-0171-0
Toplam 90 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Öğretim Teknolojileri, Eğitim Politikası, Öğrenme Analitiği
Bölüm Makaleler
Yazarlar

Mahmut Özer 0000-0001-8722-8670

Erken Görünüm Tarihi 25 Mart 2024
Yayımlanma Tarihi 16 Nisan 2024
Gönderilme Tarihi 7 Ocak 2024
Kabul Tarihi 7 Mart 2024
Yayımlandığı Sayı Yıl 2024 Cilt: 13 Sayı: 2

Kaynak Göster

APA Özer, M. (2024). Potential Benefits and Risks of Artificial Intelligence in Education. Bartın University Journal of Faculty of Education, 13(2), 232-244. https://doi.org/10.14686/buefad.1416087
All the articles published in the journal are open access and distributed under the conditions of CommonsAttribution-NonCommercial 4.0 International License
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