Abstract : On November 30, 2022, ChatGPT made its first appearance in the public domain and within a week had more than a million subscribers. The world was taken aback by the advanced ability of the generative AI tool ChatGPT to do impressively challenging jobs. Teaching staff have varied sentiments regarding ChatGPT’s amazing potential to conduct complete challenging activities in the realm of education because this development in AI appears to alter modern education pedagogies. In this study we offer some distinct advantages of ChatGPT in enhancing teaching and learning by synthesizing current existing studies. The promotion of individualized and interactive learning, formative evaluation procedures, and other advantages are only a few of ChatGPT’s advantages. The research also identifies numerous fundamental flaws in the ChatGPT, such as incorrect information generation, biases in data training that may exaggerate preexisting prejudices, privacy concerns, etc. The research makes suggestions for utilizing ChatGPT to enhance instruction and learning. In order to improve education and promote students’ learning, policy experts, academics, educators, and technology experts might collaborate and start discussions on how these growing generative AI technologies may be utilized securely and constructively.
Cite : S. (2023). Recognizing Chatgpt's Ability to Enhance the Teaching and Learning Process (1st ed., pp. 136-140). Noble Science Press. https://doi.org/10.52458/9789388996570.2023.eb.ch28
References :
Abukmeil, M., Ferrari, S., Genovese, A., Piuri, V., & Scotti, F. (2021). A survey of unsupervised generative models for exploratory data analysis and representation learning. Acm computing surveys (csur), 54(5), 1-40. https://doi.org/10.1145/3450963.
Alshater, M. (2022). Exploring the role of artificial intelligence in enhancing academic performance: A case study of ChatGPT (December 26, 2022). Available at SSRN: https://ssrn.com/abstract=4312358 or http://dx.doi.org/10.2139/ssrn.4312358
Altman, S. (2022, Dec. 4). Twitter. https://twitter.com/sama/status/1599668808285028353?s=20&t=j5ymf1tUeTpeQuJKlW AKaQ.
Aydin, Ö., &Karaarslan, E. (2022). OpenAI ChatGPT Generated Literature Review : Digital Twin in Healthcare. In Emerging Computer Technologies 2 (Vol. 2). ?zmir AkademiDernegi Brown, T., Mann, B., Ryder, N., Subbiah, M., Kaplan, J. D., Dhariwal, P., et al. (2020). Language models are few-shot learners. Advances in Neural Information Processing Systems, 33: 1877-1901.
Chen, Y., Chen, Y., & Heffernan, N. (2020). Personalized math tutoring with a conversational agent. arXiv preprint arXiv:2012.12121. D'Mello, S., Craig, S., Witherspoon, A., &Graesser, A. (2014).
Affective and learning-related dynamics during interactions with an intelligent tutoring system. International Journal of Human-Computer Studies, 72(6), 415-435. Elsen-Rooney, M. (2023). NYC education department blocks ChatGPT on school devices, Electronic copy available at: https://ssrn.com/abstract=4337484 17 networks. Retrieved on January 24 2023 from. Retrieved on January 24 2023 from https://ny.chalkbeat.org/2023/1/3/23537987/nyc-schools-ban-chatgpt-writing-artificialintelligence.
Gui, J., Sun, Z., Wen, Y., Tao, D., & Ye, J. (2021). A review on generative adversarial networks: Algorithms, theory, and applications. IEEE Transactions on Knowledge and Data Engineering. doi: 10.1109/TKDE.2021.3130191.
Herft, A. (2023). A Teacher's Prompt Guide to ChatGPT aligned with 'What Works Best' Guide. Retrieved on January 23 2023 from https://drive.google.com/file/d/15qAxnUzOwAPwHzoaKBJd8FAgiOZYcIxq/view. Hu, L. (2023).
Generative AI and Future. Retrieved on January 23 from https://pub.towardsai.net/generative-ai-and-future-c3b1695876f2. Jovanovi?, M. (2023). Generative Artificial Intelligence: Trends and Prospects. https://www.computer.org/csdl/magazine/co/2022/10/09903869/1H0G6xvtREk. 0.1109/MC.2022.3192720.
Johnson, M., Schuster, M., Le, Q., Krikun, M., Wu, Y., Chen, Z., ... & Chen, Y. (2016). Google's neural machine translation system: Bridging the gap between human and machine translation. arXiv pre. Kim, S., Park, J., & Lee, H. (2019).
Automated essay scoring using a deep learning model. Journal of Educational Technology Development and Exchange, 2(1), 1-17. Lucy, L., &Bamman, D. (2021, June).
Gender and representation bias in GPT-3 generated stories. In Proceedings of the Third Workshop on Narrative Understanding (pp. 48-55). http://dx.doi.org/10.18653/v1/2021.nuse-1.5.