과제정보
이 연구는 2023학년도 단국대학교 대학연구비 지원으로 연구되었음.
참고문헌
- Bailey, J. O., Patel, B., & Gurari, D. (2021). A perspective on building ethical datasets for children's conversational agents. Frontiers in Artificial Intelligence, 4, 637532.
- Boden, M. A. (2018). Artificial intelligence: A very short introduction. Oxford: Oxford University Press.
- Cameron, L. (2002). Metaphors in the learning of science: A discourse focus. British Education Research Journal, 28(5), 673-688. https://doi.org/10.1080/0141192022000015534
- Cao, Q., Lin, L., Shi, Y., Liang, X., & Li, G. (2017). Attention-aware face hallucination via deep reinforcement learning. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 690-698.
- Chiang, T. T. C., Liao, C.-S., & Wang, W.-C. (2022a). Impact of artificial intelligence news source credibility identification system on effectiveness of media literacy education. Sustainability, 14, 4830.
- Chiang, T. T. C., Liao, C.-S., & Wang, W.-C. (2022b). Investigating the difference of fake news source credibility recognition between ANN and BERT algorithms in artificial intelligence. Applied Sciences, 12, 7725.
- Chowdhary, K. R. (2020). Fundamentals of artificial intelligence. Dordrecht: Springer.
- Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32, 444-452. https://doi.org/10.1007/s10956-023-10039-y
- Copeland, J. (2015). Artificial intelligence: A philosophical introduction. Oxford: Blackwell.
- Devlin, J., Chang, M.-W., Lee, K., & Toutanova, K. (2018). BERT: Pre-training of deep bidirectional transformers for language understanding. Retrieved April 29, 2023 from https://arxiv.org/abs/1810.04805
- Duit, R. (1991). On the role of analogies and metaphors in learning science. Science Education, 75(6), 649-672. https://doi.org/10.1002/sce.3730750606
- Eisenstein, J. (2019). Introduction to natural language processing. Cambridge, MA: The MIT Press.
- Fergus, S., Botha, M., & Ostovar, M. (2023). Evaluating academic answers generated using chatGPT. Journal of Chemical Education, 100, 1672-1675. https://doi.org/10.1021/acs.jchemed.3c00087
- Fiori, A. (2019). Trends and applications of text summarization techniques. Hershey, PA: IGI Global.
- Foster, D. (2023). Generative deep learning. Sebastopol, CA: O'Reilly.
- Goldenthal, E., Park, J., Liu, S. X., Mieczkowski, H., & Hancock, J. T. (2021). Not all AI are equal: Exploring the accessiblity of AI-mediated communication technology. Computers in Human Behavior, 125, 106975.
- Goldie, J. G. S. (2016) Connectivism: A knowledge learning theory for the digital age? Medical Teacher, 38(10), 1064-1069. https://doi.org/10.3109/0142159X.2016.1173661
- Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning: Adaptive computation and machine learning series. Cambridge, MA: The MIT Press.
- Han, S. (2023). Evolution of large language models and cloud services such as ChatGPT. Digital Service Issue Report, 3, 3-12.
- Hinton, G. E., Osindero, S., & Teh, Y. (2006). A fast learning algorithm for deep belief nets. Neural Computation, 18, 1527-1554. https://doi.org/10.1162/neco.2006.18.7.1527
- Hu, P., Lu, Y., Gong, Y. (2021). Dual humanness and trust in conversational AI: A person-centered approach. Computers in Human Behavior, 119, 106727.
- Humphry, T., & Fuller, A. L. (2023). Potential ChatGPT use in undergraduate chemistry laboratories. Journal of chemical Education, 100, 1434-1436. https://doi.org/10.1021/acs.jchemed.3c00006
- Jho, H. (2020). Discussion how to apply artificial intelligence to physics education. New Physics: Sae Mulli, 70(11), 974-984. https://doi.org/10.3938/NPSM.70.974
- Jho, H., & Lee, B. (2022). Clustering science gifted students' graduation thesis based on machine learning. Journal of Science Education for the Gifted, 14(1), 13-22.
- Kabudi, T., Pappas, I., & Olsen, D. H. (2021). AI-enabled adaptive learning system: A systematic mapping of the literature. Computers and Education: Artificial Intelligence, 2, 100017.
- Kang, D. (2023). The advent of ChatGPT and the response of Korean language education. Korean Language and Literature, 82, 469-496.
- Kim, S., Kim, S., Lee, M., & Kim, H. (2020). Review on artificial intelligence education for K-12 students and teachers. The Journal of Korean Association of Computer Education, 23(4), 1-11. https://doi.org/10.32431/kace.2020.23.1.001
- Kizito, R. N. (2016). Connectivism in learning activity design: Implications for pedagogically-based technology adoption in African higher education contexts. International Review of Research in Open and Distributed Learning, 17(2), 19-39. https://doi.org/10.19173/irrodl.v17i2.2217
- Ko, B., & Han, S. (2021). Achievements in AI education of elementary school teachers and awareness of AI education training. Korean Association of Artificial Intelligence Education Transaction, 2(1), 29-43.
- Langr, J., & Bok, V. (2019). GANs in action: Deep learning with generative adversarial networks. New York: Manning.
- Lee, J. (2023). Exploring the possibility of automatic scoring for graphical responses using a convolutional neural network. New Physics: Sae Mulli, 73(2), 138-149. https://doi.org/10.3938/NPSM.73.138
- Lee, S., & Jeon, S. (2023). Issues about copyright of ChatGPT. Korean Copyright Commission.
- Liu, B., Jiang, Y., Zhang, X., Liu, Q., Zhang, S., Biswas, J., & Stone, P. (2023). LLM+P: Empowering large language models with optimal planning proficiency. Retrieved May 1, 2023 from https://arxiv.org/abs/2304.11477
- Liu, C., Shum, H.-Y., & Freeman, W. T. (2007). Face hallucination: Theory and practice. International Journal of Computer Vision, 75, 115-134. https://doi.org/10.1007/s11263-006-0029-5
- Lu, Z., Pu, H., Wang, F., Hu, Z., & Wang, L. (2017). The expressive power of neural networks: A view from the width. Proceeding of the 31st conference on Neural Information Processing Systems, Long Beach, CA.
- Many, I., & Maybury, M. T. (1999). Advances in automatic text summarization. Cambridge, MA: The MIT Press.
- McCulloch, W. S., & Pitts, W. H. (1943). A log ical calculus of the ideas immanent in nervous activity. Bulletin of Biophysics, 5, 115-133. https://doi.org/10.1007/BF02478259
- Park, G., Hwang, S., & Lee, J. (2023). Development and validation of teaching competence scale for teachers' artificial intelligence convergence education. Journal of Education Technology, 39(1), 315-344.
- Partala, T., & Surakka, V. (2004). The effects of affective interventions in human-computer interaction. Interacting with Computers, 16(2), 295-309. https://doi.org/10.1016/j.intcom.2003.12.001
- Peters, M. E., Neumann, M., Iyyer, M., Gardner, M., Clark, C., Lee, K., & Zettlemoyer, L. (2018). Deep contextualized word representations. Retrieved April 30, 2023 from https://arxiv.org/abs/1802.05365
- Radford, A., Narasimhan, K., Salimans, T., & Sutskever, I. (2018). Improving language understanding by generative pre-training. Retrieved April 30, 2023 from https://s3-us-west-2.amazonaws.com/openai-assets/researchcovers/language-unsupervised/language_understanding_paper.pdf
- Raffel, C., Shazeer, N., Roberts, A., Lee, K., Narang, S., Matena, M., Zhou, Y., Li, W., & Liu, P. J. (2019). Exploring the limits of transfer learning with a unified text-to-text transformer. Retrieved April 25, 2023 from https://arxiv.org/abs/1910.10683
- Raghu, M., Poole, B., Kleinberg, J., Ganguli, S., & Sohl-Dickstein, J. (2016). On the expressive power of deep neural networks. Retrived May 5, 2023 from https://arxiv.org/abs/1606.05336
- Rapp, A., Curti, L., & Boldi, A. (2021). The human side of human-chatbot interaction: A systematic literature review of ten years of research on text-based chatbots. International Journal of Human-Computer Studies, 151, 102630.
- Ravichandiran, S. (2019). Hands-on deep learning algorithms with python. Bermingham: Packt.
- Rosenblatt, F. (1958). The Perceptron: A probabilistic model for information storage and organization in the brain. Psychological Review, 65(6), 386-408. https://doi.org/10.1037/h0042519
- Shin, D., Jeong, H., & Lee, Y. (2023). Exploring the potential of using ChatGPT as a content-based English learning and teaching tool. Journal of the Korea English Education Society, 22(1), 171-192.
- Shu, K., Wang, S., Lee, D., & Liu, H. (2020). Disinformation, misinformation, and fake news in social media. Dordrecht: Springer.
- Shulman, J., Wolski, F., Dhariwal, P., Radford, A., & Klimov, O. (2017). Proximal policy optimization algorithms. Retrieved May 3, 2023 from https://arxiv.org/abs/1707.06347
- Sutton, R. S., & Barto, A. G. (2018). Reinforcement learning: An introduction. Cambridge, MA: The MIT Press.
- Sutskever, I., Vinyals, O., & Le, Q. V. (2014). Sequence to sequence learning with neural networks. Proceeding of the 27th International Conference on Neural Information Processing Systems, Montreal, Canada.
- Tilli, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., & Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10, 15.
- Transue, B. M. (2013). Connectivism and information literacy: Moving from learning theory to pedagogical practice. Public Service Quarterly, 9(3), 185-195. https://doi.org/10.1080/15228959.2013.815501
- Tunstall, L., von Werra, L., & Wolf, T. (2022). Natural language processing with transformers. Sebastopol, CA: O'Reilly.
- UNESCO. (2023). ChatGPT and artificial intelligence in higher education: Quick start guide. Retrieved May 3, 2023 from https://unesdoc.unesco.org/ark:/48223/pf0000385146
- Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., Kalser, L., & Polosukhin, I. (2017). Attention is all you need. Retrieved April 3, 2023 from https://arxiv.org/abs/1706.03762
- Wang, L., Hu, Y., He, J., Xu, X., Liu, N., Liu, H., & Shen, H. T. (2023). T-SciQ: Teaching multimodal chain-of-thought reasoning via large language model signals for science question answering. Retrieved May 31, 2023 from https://arxiv.org/abs/2305.03453
- Weise, K., & Metz, C. (2023, May 9). When A.I. chatbots hallucinate. New York Times, https://www.nytimes.com/2023/05/01/business/aichatbots-hallucination.html
- Xian, Y., Lampert, C. H., Schiele, B., & Akata, Z. (2020). Zero-shot learning: A comprehensive evaluation of the good, the bad and the ugly. Retrieved May 6, 2023 from https://arxiv.org/abs/1707.00600