Poster Session Generative AI for Computational Skill Development among English Learners more

Interdisciplinary learning is widely acknowledged as a crucial educational strategy, allowing students to integrate knowledge from different domains. Natural Language Processing (NLP), for example, is a highly interdisciplinary field involving computer science and linguistics, often requiring learners to develop computational skills such as programming and data analysis (Joshi, 1991). The growth of generative AI (GenAI) has further catalyzed this integration and expanded the possibilities for humanities students to learn computational skills traditionally considered outside their domain. However, limited research has examined how humanities students use AI tools in interdisciplinary learning and their attitudes toward the approach. Therefore, applying questionnaires, semi-structured interviews, and reflections from 29 undergraduate English majors taking an Introduction to NLP course in a Taiwanese university, this study explores how these students leverage GenAI to support computational competencies acquisition and their attitudes regarding AI-assisted interdisciplinary learning. Preliminary findings suggest that while students found GenAI useful for debugging, they struggled when its responses failed to meet their expectations or task requirements. Despite challenges, they generally view GenAI as an empowering tool that facilitates their computational tasks. By leveraging AI tools to navigate NLP-related tasks, this study highlights the potential for integrating AI into humanities curricula.