Let’s Have a CHAT: On the Experience of Two Teachers’ Collaboration in Developing Localized Material Using ChatGPT

Abstract

In an era characterized by postmodern pedagogy, teachers are seeking to develop localized materials to cater to the specific learning needs of their students (Garin et al., 2017; Tajeddin& Bahrebar, 2017). Furthermore, this era is experiencing a rapidly-evolving pedagogical landscape primarily driven by technological advancement such as artificial intelligence (AI). ChatGPT, for example, is a generative AI tool that has been widely adopted by language teachers due to its perceived affordance to efficiently support the development of materials. While the effectiveness of ChatGPT on language learning has garnered a fair amount of attention in recent years (Kasneci et al., 2023), relatively little effort is made to yield deeper thinking on the adaptability of using ChatGPT to develop localized materials, especially when conflicting stakeholders are involved in the localization process.

To explore the issue, the study adopted Engeström’s (1987) Cultural-Historical Activity Theory (CHAT) framework to investigate a collaborative practice of two local high school English teachers engaging in the community of material localization. Qualitative data was elicited from field notes, on-site observations and interviews. Drawing from the framework of CHAT, the study analyzed teachers’ contextually-mediated choices and behaviors on the technology-generated texts as well as revealed conflicts that may be inherent in the system. Three key contradictions were identified: first, resistance versus flexibility in object negotiation; second, localization versus universalization in development of the “ideally” contextualized materials; and third, optimization versus innovation in the use of generative AI tools such as ChatGPT. The potential unsuitability of ChatGPT's text-based nature for localizing materials for diverse student populations was also discussed.

Keywords

Cultural Historical Activity Theory, CHAT, ChatGPT, Material-Localization, Collaborative Planning