网络出版日期: 2023-06-25
基金资助
国家自然科学基金重点项目(62037001);上海市“科技创新行动计划”启明星项目(23QA1409000);浙江大学繁星科学基金(SN-ZJU-SIAS-0010);浙江大学研究生教育研究课题项目(20210321)
Investigation into the Transformation of Knowledge-Centered Pedagogy with ChatGPT/Generative AI
Online published: 2023-06-25
本文研究了ChatGPT/生成式人工智能在以知识点为核心的教学模式下的变革作用。作为一种语言生成模型,ChatGPT通过对海量语言数据的学习,能够挖掘单词间的共生关联关系,具备深入的语言理解和组合创新能力。然而在教育领域中,ChatGPT存在过度依赖训练数据、逻辑推理能力弱和新场景处理能力有限等局限性。为了提高ChatGPT在教学场景下生成内容的准确性和针对性,本文提出将ChatGPT与以知识点为核心的教学资源组织方式进行有机结合,通过形成知识点结构图等方式对ChatGPT进行完善,同时提供了几种具体可行的使用ChatGPT辅助教师和学生的方式。最后,本文还探讨了如何将Prompt研究范式与以知识点为核心的教学模式相结合,帮助ChatGPT建立“知识体系”,从而形成一个数据和知识双轮驱动的教育场景的语言生成模型,为教育领域提供更智能化、个性化的服务,推动教育领域的发展和变革。
关键词: ChatGPT; 生成式人工智能;大型语言模型; 知识点; 教学资源组织; 个性化教学
陈静远 , 胡丽雅 , 吴飞 . ChatGPT/生成式人工智能促进以知识点为核心的教学模式变革研究[J]. 华东师范大学学报(教育科学版), 2023 , 41(7) : 177 -186 . DOI: 10.16382/j.cnki.1000-5560.2023.07.016
This paper explores the transformative role of ChatGPT in the teaching mode centered on knowledge concepts. As a language generation model, ChatGPT is capable of in-depth language comprehension and innovative combinations by mining the symbiotic relationships between words through massive language data learning. However, in the field of education, ChatGPT faces limitations such as over-reliance on training data, weak logical reasoning ability, and limited ability to handle new scenarios. To overcome these limitations and enhance the accuracy and relevance of ChatGPT’s generated content, this paper proposes an organic combination of ChatGPT with the organization of teaching resources centered on knowledge concepts, and improve ChatGPT by creating structure diagrams of knowledge concepts. Additionally, several specific and feasible ways to assist teachers and students using ChatGPT are also proposed. Finally, this paper discusses how to combine the prompt research paradigm with the teaching mode centered on knowledge concepts to help ChatGPT establish a “knowledge system”. This will enable ChatGPT to become a language generation model driven by both data and knowledge, providing more intelligent and personalized services in the education field, and promoting its development and transformation.
null | 机器之心. (2023). 史上增速最快消费级应用, ChatGPT月活用户突破1亿. 取自: https://mp.weixin.qq.com/s/ahUJrwTgXJhc0Gc7CYG_7w. |
null | 吴飞, 陈为, 孙凌云, 肖俊 以知识点为中心建设AI+X微专业 科教发展研究 2023 3 1 吴飞, 陈为, 孙凌云, 肖俊. (2023). 以知识点为中心建设AI+X微专业. 科教发展研究,3(1). |
null | Keskar, N. S., McCann, B., Varshney, L. R., Xiong, C., & Socher, R. (2019). Ctrl: A conditional transformer language model for controllable generation. arXiv preprint arXiv: 1909.05858. |
null | Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing ACM Computing Surveys 2023 55 9 1 35 Liu, P., Yuan, W., Fu, J., Jiang, Z., Hayashi, H., & Neubig, G. (2023). Pre-train, prompt, and predict: A systematic survey of prompting methods in natural language processing. ACM Computing Surveys, 55(9), 1—35. |
null | Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J Self-supervised learning: Generative or contrastive IEEE Transactions on Knowledge and Data Engineering 2021 35 1 857 876 Liu, X., Zhang, F., Hou, Z., Mian, L., Wang, Z., Zhang, J., & Tang, J. (2021). Self-supervised learning: Generative or contrastive. IEEE Transactions on Knowledge and Data Engineering, 35(1), 857—876. |
null | Radford, A., Kim, J. W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., ... & Sutskever, I. (2021, July). Learning transferable visual models from natural language supervision. In International conference on machine learning (pp. 8748−8763). PMLR. |
null | Schulman, J., B. Zoph, C. Kim, J. Hilton, J. Menick, J. Weng, J. F. C. Uribe et al. (2022) “ChatGPT: Optimizing language models for dialogue.” |
null | Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A. N., ... & Polosukhin, I. (2017). Attention is all you need. Advances in neural information processing systems, 30. |
null | Wu, L. L., & Barsalou, L. W Perceptual simulation in conceptual combination: Evidence from property generation Acta psychologica 2009 132 2 173 189 Wu, L. L., & Barsalou, L. W. (2009). Perceptual simulation in conceptual combination: Evidence from property generation. Acta psychologica, 132(2), 173—189. |
null | Wei, J., Wang, X., Schuurmans, D., Bosma, M., Chi, E., Le, Q., & Zhou, D. (2022). Chain of thought prompting elicits reasoning in large language models. arXiv preprint arXiv: 2201.11903. |
/
〈 |
|
〉 |