Exploring the Use of ChatGPT for Learning and Research: Content Data Analysis and Concerns
Abstract: Generative Pre-trained Transformer 3 (GPT-3) is an autoregressive language model that uses deep learning to produce human-like text. Given an initial text as prompt, it will produce text that continues the prompt. Now it is known as ChatGPT in a hot spot attracting millions of people in different fields. The authors of this session have explored some usages of ChatGPT and had some first-hand experiences and “data” exhibiting the “outcomes” what ChatGPT can produce. In this session first we will share our results of the content-data analysis from the ChatGPT outcomes/answers, which reflect how the ChatGPT “learns” and gradually “generates” more meaningful outcomes/answers. Then we will discuss with the participants on some widely concerned questions or issues about using ChatGPT: (a) how does it work? (b) how should the questions be asked or the initial text be prompted? (c) what are the implications of using it for collaborative learning, research and in teacher education, and (d) how would it influence the future of student writing, or academic writing?
Presider: Timothy Pelton, University of Victoria