Yapay Zekâ-Eğitim İlişkisine Bütüncül Bakış: Bir Bilim Haritalama Çalışması

Yıl 2024, Cilt: 12 Sayı: 3, 1080 - 1114

Salih Bardakcı

https://doi.org/10.46778/goputeb.1522277

Öz

Araştırmada bilimsel anlamda yapay zekâ-eğitim ilişkisinin hangi ortak kavramlar ekseninde gelişmekte olduğu incelenmektedir. Bu amaçla bilim haritalama yönteminden yararlanılmaktadır. Veriler Web of Science-Core Collection’dan elde edilmiştir. Tarama terimleri içerisinde “yapay zekâ”, “eğitim”, “öğretim”, “öğretme”, ayrıca “OpenAI”, “ChatGPT” ve “Chatbot” yer almıştır. Böylece erişilen 14.682 bilimsel metne ilişkin bibliyografik bilgi araştırmanın veri setini oluşturmuştur. Analizler VOSviewer yazılım aracı üzerinde gerçekleştirilmiştir. Veri üzerinde ortak bulunuşluk analizleri yürütülmüştür. Bu analizler sonucunda hem ortak bulunuşluk haritalarına hem de detaylı dökümlere erişilmiştir. Bu dökümlerden de yararlanılarak haritadaki genel ve güncel kavramlar belirlenmiştir. Araştırma sonuçlarına göre yapay zekâ-eğitim ilişkisi teknoloji ya da araç bağlamından çok bir öğretim yöntemi bağlamı içerisinde tartışılmaktadır. Bu tartışma özellikle son yıllarda ilişkili alanlar, öğrenme ortamı/bağlamı, öğrenme-öğretmeye ilişkin konular/beceriler ve araştırma gibi boyutlarda zenginleşmekte ve derinleşmektedir. Bu zenginlik; yapay zekâ-eğitim ilişkisini pedagojik entegrasyon, uygulanabilirlik ve etik gibi açılardan desteklemektedir. Bu ilişkinin kuramsal temellerini eğitim teknolojisi alanı ile bağlantılı biçimde ve sosyopsikolojik unsurları da kapsayarak güçlendirmektedir. Bununla birlikte etki durumları ve insan-yapay zekâ iş birliği gibi açılardan gelişime açık yönler barındırmaktadır.

Anahtar Kelimeler

Eğitim, Öğretim, Yapay Zekâ, Chatgpt, Chatbot, Bilim Haritalama, Ortak Kavramlar

A Holistic Perspective on the AI-Education Nexus: A Science Mapping Study

Öz

This study examines how the relationship between artificial intelligence (AI) and education in scientific literature is evolving around common key concepts. For this purpose, the science mapping method was employed. Data were obtained from the Web of Science Core Collection. The search terms included “artificial intelligence,” “education,” “instruction,” and “teaching,” as well as "OpenAI," "ChatGPT," and "Chatbot." Bibliographic data from 14,682 scientific documents were extracted, forming the dataset for this study. Analyses were conducted using the VOSviewer software tool, and co-occurrence analyses were performed on the data. These analyses produced both co-occurrence maps and detailed outputs. With the contribution of these outputs, the general and emerging concepts in the map were identified. The results indicate that the AI-education relationship is predominantly discussed in the context of instructional methods rather than as a technology or tool. In recent years, this discourse has particularly enriched and deepened in related fields, learning environments/contexts, issues/skills related to teaching and learning, and research. This richness supports the AI-education relationship from pedagogical integration, applicability, and ethics perspectives. Additionally, it strengthens the theoretical foundations of this relationship by linking it to educational technology and incorporating socio-psychological elements. However, there remains potential for further development in areas such as impact dynamics and human-AI collaboration

Anahtar Kelimeler

Education, Instruction, Artificial Intelligence, ChatGPT, Chatbot, Science Mapping, Common Concepts

Kaynakça

• Aiken R. M., & Epstein R. G. (2000). Ethical guidelines for AI in education: starting a conversation, International Journal of Artificial Intelligence in Education, 11, 163-76.
• Allen, B., McGough, A. S., & Devlin, M. (2022). Toward a framework for teaching artificial intelligence to a higher education audience. Acm Transactions on Computing Education, 22(2). https://doi.org/10.1145/3485062
• Arslan, K. (2020). Artificial intelligence and applications in education. Western Anatolia Journal of Educational Sciences, 11(1), 71-88.
• Baker M. J. (2000). The roles of models in artificial intelligence and education research: a prospective view. Journal of Artificial Intelligence and Education, 11, 122-143.
• Baker T., Smith L., & Anissa N. (2019). Educ-AI-tion rebooted? Exploring the future of artificial intelligence in schools and colleges. NESTA. Retrieved July 15, 2024, https://media.nesta.org.uk/documents/Future_of_AI_and_education_v5_WEB.pdf
• Bardakcı, S., Yılmaz-Soylu, M., Deryakulu, D., & Akkoyunlu, B. (2019). Bilim haritalama yöntemi ve eğitim teknolojisi alanında yürütülen eğilim araştırmalarına katkıları [Science mapping method and its contributions of trend studies in the field of educational technology]. In A. İşman, H.F. Odabaşı, & B. Akkounlu (Eds.), Eğitim Teknolojisi Okumaları (pp. 17-38). Pegem Akademi.
• Becker B. (2017). Artificial intelligence in education: what is it, where is it now, where is it going. In Mooney B. (Ed.), Ireland’s Yearbook of Education 2017-2018 (pp. 42-48). Education Matters. https://irelandseducationyearbook.ie/irelands-yearbook-of-education-2017-2018/
• Benjamin Jr., L. T. (1988). A history of teaching machines. American Psychologist, 43(9), 703-712.
• Center for Information Technology and Society. (2023). Why we fall for fake news. University of California Santa Barbara. https://cits.ucsb.edu/fake-news/why-we-fall
• Chassignol, M., Khoroshavin, A., Klimova, A., & Bilyatdinova, A. (2018). Artificial intelligence trends in education: a narrative overview. Procedia Computer Science, 136 (2018), 16-24.
• Chatterjee, J., & Dethlefs, N. (2023). This new conversational AI model can be your friend, philosopher, and guide … and even your worst enemy. Pattern, 4(1), Article 100676. https://doi.org/10.1016/j.patter.2022.100676
• Cobo, M. J., López-Herrera, A.G., Herrera-Viedma, E., & Herrera F. (2011). Science mapping software tools: Review, analysis, and cooperative study among tools. Journal of The American Society for Information Science and Technology, 62(7), 1382–1402. https://doi.org/10.1002/asi.21525
• Couldry, N., & Mejias, U. (2019). The costs of connection: how data is colonizing human life and appropriating it for capitalism. Stanford, CA: Stanford University Press.
• Davies H. C., Eynon R., & Salveson C. (2020), The mobilisation of AI in education: a bourdieusean field analysis. Sociology, 55(3), 539-560. https://doi. org/10.1177/0038038520967888
• Holmes W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: promises and implications for teaching and learning. USA: Center for Curriculum Redesign. https://curriculumredesign.org/wp-content/uploads/AIED-Book-Excerpt-CCR.pdf
• Huang, A. Y. Q., Lu, O. H. T., & Yang, S. J. H. (2023). Effects of artificial Intelligence–Enabled personalized recommendations on learners’ learning engagement, motivation, and outcomes in a flipped classroom, Computers & Education, 194(2023) 104684. https://doi.org/10.1016/j.compedu.2022.104684
• Huh, J., Nelson, M. R., & Russell, C. A. (2023). ChatGPT, AI advertising, and advertising research and education. Journal of Advertising, 52(4), 477–482. https://doi.org/10.1080/00913367.2023.2227013
• Jobin A., Ienca M., & Vayena E. (2019). Artificial intelligence: the global landscape of ethics guidelines. Nature Machine Intelligence, 1(9), 389-99. https://doi.org/10.1038/s42256-019-0088-2
• Kay, J. (2023). Foundations for human-AI teaming for self-regulated learning with explainable AI (XAI). Computers in Human Behavior, 147 (2023), 107848. https://doi.org/10.1016/j.chb.2023.107848
• Li, K.C., & Wong, B.T.-M. (2023). Artificial intelligence in personalised learning: a bibliometric analysis. Interactive Technology and Smart Education, 20(3), 422-445. https://doi.org/10.1108/ITSE-01-2023-0007
• Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. A., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarok ¨ or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790. https://doi.org/10.1016/j.ijme.2023.100790
• Lin, XF., Zhou, Y., Shen, W., Luo, G., Xian, X., & Pang, B. (2024). Modeling the structural relationships among Chinese secondary school students’ computational thinking efficacy in learning AI, AI literacy, and approaches to learning AI. Education and Information Technologies 29(2024), 6189–6215. https://doi.org/10.1007/s10639-023-12029-4
• Macfarlane B. (2003). Teaching with integrity: the ethics of higher education practice. Abingdon, Oxon/New York, NY: Routledge.
• Mallik, S., & Gangopadhyay, A. (2023). Proactive and reactive engagement of artificial intelligence methods for education: a review. Frontiers in Artificial Intelligence, 6, 1151391. https://doi.org/10.3389/frai.2023.1151391
• McStay, A. (2019). Emotional AI and EdTech: serving the public good? Learning, Media and Technology, 45(3), 270–283. https://doi.org/10.1080/17439884.2020.1686016
• Miao F., & Holmes W. (2021). AI and education: guidance for policy-makers. UNESCO. https://unesdoc.unesco.org/ark:/48223/pf0000376709.
• Morris, S., & Van DerVeer Martens, B. (2008). Mapping research specialties. Annual Review of Information Science and Technology, 42(1), 213–295. https://doi.org/10.1002/aris.2008.1440420113
• Nemorin, S., Vlachidis, A., Ayerakwa, H. M., & Andriotis, P. (2022). AI hyped? A horizon scan of discourse on artificial intelligence in education (AIED) and development. Learning, Media and Technology, 48(1), 38–51. https://doi.org/10.1080/17439884.2022.2095568
• OECD (2021). OECD Digital Education Outlook 2021: pushing the frontiers with artificial intelligence, blockchain and robots. OECD. https://doi.org/10.1787/589b283f-en
• Open Culture. (2023). Noam Chomsky on ChatGPT. Open Culture. https://www.openculture.com/2023/02/noam-chomsky-on-chatgpt.html
• Peters, R. S. (1970). Ethics and education. London: Allen & Unwin.
• Pritchard, A. (1969). Statistical bibliography or bibliometrics? Journal of Documentation, 25, 348-349.
• Seldon A., & Abidoye O. (2018). The fourth education revolution: will artificial intelligence liberate or infantilise humanity? London: The University of Buckingham Press.
• Sing, C. C., S., Timothy, T., Fang, H., Chiu, T. K., & Wang, X. (2022). Secondary school students’ intentions to learn AI: Testing moderation effects of readiness, social good and optimism. Educational Technology Research and Development, 70(3), 765–782. https://doi.org/10.1007/s11423-022-10111-1
• Skinner, B. F. (1958). Teaching machines. The Review of Economics and Statistics 42(3): 189–191.
• Stolpe, K., & Hallström, J. (2024). Artificial intelligence literacy for technology education, Computers and Education Open, 6(2024). 100159. https://doi.org/10.1016/j.caeo.2024.100159
• van Eck, N. J., & Waltman, L. (2022). VOSviewer Manual: Manual for VOSviewer version 1.6.18. Universiteit Leiden, CWTS Meaningful Metrics. https://www.vosviewer.com/documentation/Manual_VOSviewer_1.6.18.pdf
• Wang, B., Rau, P. L. P., & Yuan, T. (2023). Measuring user competence in using artificial intelligence: validity and reliability of artificial intelligence literacy scale. Behaviour & Information Technology, 42(9), 1324–1337. https://doi.org/10.1080/0144929X.2022.2072768
• Wang, Y. M., Wei, C. L., Lin, H. H., Wang, S. C., & Wang, Y. S. (2022). What drives students’ AI learning behavior: a perspective of AI anxiety. Interactive Learning Environments, 1–17. https://doi.org/10.1080/10494820.2022.2153147
• Watters A. (2021). Teaching machines. Cambridge, MA: MIT Press.
• 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/s41239-019-0171-0