Kimya Öğretimi ve Öğrenimi İçin Yapay Zekâ Dil Modeli GPT-4: Fırsatları ve Zorlukları Üzerine Bir İnceleme
Özet
AI tabanlı Chatbot “GPT-4”, kimya öğretiminde kolay ve anında erişim sunması nedeniyle büyük fırsatlar sunmaktadır. Model, geniş bir bilgi tabanına erişebilmesi ve karmaşık kimya konularını anlamasıyla öğrencilere önemli bir kaynak sağlayabilir. Kimya öğretiminde, GPT-4 interaktif bir öğretmen veya rehber rolü üstlenebilir ve öğrencilere anlaşılır açıklamalar sunarak konuların anlaşılmasını kolaylaştırabilir. Bununla birlikte, modelin kullanımında bazı zorlukları vardır. Kimya, matematiksel hesaplamaları ve laboratuvar deneylerini içeren pratik bir bilim olduğundan, bu konularda sınırlamaları olabilir. Ayrıca, modelin verdiği bilgilerin güvenirliği için dikkatlice doğrulanmalıdır. GPT-4 gibi yapay zekâ dil modelleri, kimya öğretimi ve öğrenimi için büyük bir potansiyele sahip olsa da, doğru kullanımı ve içeriklerin dikkatli bir şekilde yönetilmesi önemlidir.
Referanslar
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Referanslar
Abdelghani, R., Wang, Y.-H., Yuan, X. (2022). GPT-3-driven pedagogical agents for training children’s curious question-asking skills. arXiv. preprint arXiv:2211.14228.
Ayanwale, M. A., Sanusi, I. T., Adelana, O. P., Aruleba, K. D., & Oyelere, S. S. (2022). Teachers’ readiness and intention to teach artificial intelligence in schools. Computers and Education: Artificial Intelligence, 3, Article 100099.
Bhat, S., Nguyen, H. A., Moore, S., Stamper, J., Sakr, M., & Nyberg, E. (2022). Towards automated generation and evaluation of questions in educational domains. In Proceedings of the 15th international conference on educational data mining (pp. 701–704). Durham, United Kingdom: International Educational Data Mining Society.
Brants, T., Popat , A. C. (2007). Large Language Models in Machine Translation. Proceedings of the 2007 Joint Conference on Empirical Methods in Natural Language Processing and Computational Natural Language Learning, 858–867.
Chocarro, R., Cortinas, M., & Marcos-Matas, G. (2021). Teachers’ attitudes towards chatbots in education: A technology acceptance model approach considering the effect of social language, bot proactiveness, and users’ characteristics. Educational Studies, 1–19.
Choi, S., Jang, Y., & Kim, H. (2023). Influence of pedagogical beliefs and perceived trust on teachers’ acceptance of educational artificial intelligence tools. International Journal of Human-Computer Interaction, 39(4), 910–922.
Cotton, D. R., Cotton, P. A., & Shipway, J. (2023). Chatting and cheating. In Ensuring academic integrity in the era of GPT-4. EdArXiv.
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.Floridi & Chiriatti, 2020.
Dijkstra, R., Genç, Z., Kayal, S., & Kamps, J. (2022). Reading comprehension quiz generation using generative pre-trained transformers. https://e.humanities.uva.nl/publ ications/2022/dijk_read22.pdf.
Floridi, L., & Chiriatti, M. (2020). GPT-3: Its nature, scope, limits, and consequences. Minds and Machines, 30(4), 681–694.
Gabajiwala, E., Mehta, P., Singh, R., & Koshy, R. (2022). Quiz maker: Automatic quiz generation from text using NLP. In Futuristic trends in networks and computing technologies (pp. 523–533). Singapore: Springer.
Gu, C., Huang, C., Zheng, X., Chang, K.-W., & Hsieh, C.-J. (2022). Watermarking pretrained language models with backdooring. arXiv. preprint arXiv:2210.07543.
Ji, H., Han, I., & Ko, Y. (2022). A systematic review of conversational ai in language education: Focusing on the collaboration with human teachers. Journal of Research on Technology in Education, 1–16.
Johnstone, A. H. (1991).Why is Science Difficult to Learn? Things are Seldom what they Seem. Journal of Computer Assisted Learning, 7/2, 75–83.
Jurafsky, D., Martin, J. H. (2021). Speech and Language Processing (3rd ed.). https://web.stanford.edu/~jurafsky/slp3/3.pdf.
Kasneci, E., Seßler, K., Küchemann, S., Bannert, M., Dementieva, D., Fischer, F., ... & Kasneci, G. (2023). GPT-4 for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274.
Kirchenbauer, J., Geiping, J., Wen, Y., Katz, J., Miers, I., & Goldstein, T. (2023). A watermark for large language models. arXiv. preprint arXiv:2301.10226v1.
Kubacka, T. (2022). Tweet zum Thema „GPT-4 about the topic I wrote my PhD about”. https://twitter.com/paniterka ch/status/1599893718214901760.
Kuhlthau, C. C., Maniotes, L. K., & Caspari, A. K. (2015). Guided inquiry: Learning in the 21st century: Learning in the 21st century. Abc-Clio.
Liu, Y., Ott, M., Goyal, N., Du, J., Joshi, M., Chen, D., Levy, O., Lewis, M., Zettlemoyer, L., & Stoyanov, V. (2019). Roberta: A robustly optimized bert pretraining approach. arXiv. preprint arXiv:1907.11692.
Min, B., Ross, H., Sulem, E., Veyseh, A. P. B., Nguyen, T. H., Sainz, O., Agirre, E., Heinz, I., & Roth, D. (2021). Recent advances in natural language processing via large pre-trained language models: A survey. arXiv. preprint arXiv:2111.01243.
Moore, S., Nguyen, H. A., Bier, N., Domadia, T., & Stamper, J. (2022). Assessing the quality of student-generated short answer questions using GPT-3. In Educating for a new future: Making sense of technology-enhanced learning adoption: 17th European conference on technology enhanced learning, EC-TEL 2022, Toulouse, France, September 12–16, 2022, Proceedings (pp. 243–257). Springer.
OpenAI. GPT-4: Optimizing Language Models for Dialogue. https://openai.com/blog/GPT-4/.
Pavlik, J. V. (2023). Collaborating with GPT-4: Considering the implications of generative artificial intelligence for journalism and media education. Journalism & Mass Communication Educator, 78(1), Article 10776958221149577.
Pawlak, F. (2023). ChatGPT–a revolution for teaching and learning in chemistry education?! CHEMKON, 30, Nr.1 – 6
Perels, F. ve ark. (2020). Selbstregulation und selbstreguliertes Lernen. In: E.Wild & J. Mçller (Hrsg.). Einfhrung in die Pdagogische Psychologie. Berlin, Springer, 46.
Polak, S., Schiavo, G., & Zancanaro, M. (2022). Teachers’ perspective on artificial intelligence education: An initial investigation. In Extended abstracts of the 2022 CHI conference on human factors in computing systems, CHI EA ’22, New York, NY, USA. Association for Computing Machinery.
Radford, A., Narasimhan, K., Salimans, T., Sutskever, I., et al. (2018). Improving language understanding by generative pre-training.
Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., Liu, P. J., et al. (2020). Exploring the limits of transfer learning with a unified text-to-text transformer. Journal of Machine Learning Research, 21(140), 1–67.
Reiners, Ch.S., Saborowski, J. (2022). Auf dem Weg zum Chemieunterricht. In: Ch. S. Reiners (Hrsg.). Chemie vermitteln: Fachdidaktische Grundlagen und Implikationen, Springer, Berlin & Heidelberg, 113–180.
Scao, T. L., Fan, A., Akiki, C., Pavlick, E., Ili´c, S., Hesslow, D., Castagn´e, R., Luccioni, A. S., Yvon, F., Gall´e, M., et al. (2022). BLOOM: A 176B-parameter open-access multilingual language model. arXiv. preprint arXiv:2211.05100.
Team, O. (2022). GPT-4: Optimizing language models for dialogue.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kaiser, & Polosukhin, I. (2017). Attention is all you need. Advances in Neural Information Processing Systems, 30. preprint arXiv:2212.01020.
Wambach, H., Wambach-Laicher, J. (2018). Unterrichtssteuerung und Verlaufsplan. In: K. Sommer, J. Wambach-Laicher, & P. Pfeifer (Hrsg.): Konkrete Fachdidaktik Chemie: Grundlagen fr das Lernen und Lehren im Chemieunterricht. Friedrich, Aulis, Seelze 372–397.
Yang, Z., Dai, Z., Yang, Y., Carbonell, J., Salakhutdinov, R., & Le, Q. V. (2019). XLNet: Generalized Autoregressive Pretraining for Language Understanding. Advances in neural information processing systems, 32. preprint arXiv:1810.04805
