Abstract
AbstractChatGPT, a chatbot based on a Generative Pre-trained Transformer model, can be used as a teaching tool in the educational setting, providing text in an interactive way. However, concerns point out risks and disadvantages, as possible incorrect or irrelevant answers, privacy concerns, and copyright issues. This study aims to categorize the strategies used by undergraduate students completing a source-based writing task (SBW, i.e., written production based on texts previously read) with the help of ChatGPT and their relation to the quality and content of students’ written products. ChatGPT can be educationally useful in SBW tasks, which require the synthesis of information from a text in response to a prompt. SBW requires mastering writing conventions and an accurate understanding of source material. We collected 27 non-expert users of ChatGPT and writers (Mage = 20.37; SD = 2.17). We administered a sociodemographic questionnaire, an academic writing motivation scale, and a measure of perceived prior knowledge. Participants were given a source-based writing task with access to ChatGPT as external aid. They performed a retrospective think-aloud interview on ChatGPT use. Data showed limited use of ChatGPT due to limited expertise and ethical concerns. The level of integration of conflicting information showed to not be associated with the interaction with ChatGPT. However, the use of ChatGPT showed a negative association with the amount of literal source-text information that students include in their written product.
Funder
Università degli Studi di Firenze
Publisher
Springer Science and Business Media LLC
Reference51 articles.
1. Adamopoulou, E., & Moussiades, L. (2020). An overview of Chatbot technology. In: Maglogiannis, I., Iliadis, L., Pimenidis, E. (Eds.), Artificial Intelligence Applications and Innovations. AIAI 2020. IFIP Advances in Information and Communication Technology, 584. Springer. https://doi.org/10.1007/978-3-030-49186-4_31.
2. Baker, T., & Smith, L. S. (2019). Educ-AI-tion Rebooted? Exploring the future of artificial intelligence in schools and colleges. Nesta.
3. Barrot, J. S. (2023). Using automated written corrective feedback in the writing classrooms: Effects on L2 writing accuracy. Computer Assisted Language Learning, 36, 584–607. https://doi.org/10.1080/09588221.2021.1936071.
4. Baykasoğlu, A., Özbel, B. K., Dudaklı, N., Subulan, K., & Şenol, M. E. (2018). Process mining based approach to performance evaluation in computer-aided examinations. Computer Applications in Engineering Education, 26, 1841–1861. https://doi.org/10.1002/cae.21971.
5. Beach, R., Newell, G., & VanDerHeide, J. (2016). A sociocultural perspective on writing development: Toward an agenda for classroom research on students’ uses of social practices. In C. MacArthur, S. Graham, & J. Fitzgerald (Eds.), Handbook of writing research (pp. 88–101). Guilford Press.
Cited by
1 articles.
订阅此论文施引文献
订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献