Exploring learning motivation in the era of generative artificial intelligence among Chinese students majoring in...

Generative artificial intelligence (GenAI) has brought significant while unpredictable changes to the learning motivation of students from various majors including translation and interpreting (T&I). Therefore, this study aims to explore learning motivation in the era of GenAI among T&I majors, shifting from traditional top-down quantitative approaches to constructionist bottom-up mixed methods. Data were collected using Q methodology from 30 students pursuing the master of T&I at 12 universities in China. Findings show 5 motivational typologies, which are cross-border explorers, pragmatist practitioners, passive receivers, employment seekers, and dream pursuers. The 5 motivational typologies overlap while each maintain peculiar characteristics, indicating that learning motivation is a complex system with various orientations interact within the whole and emerge into different typologies that distinguish from one another while share motivational grounds in some degree. This study also proposes pedagogical implications tailored to the 5 motivations, hoping to offer support for the enhancement of T&I training, the improvement of teaching strategy, and the adjustment of learning psychology in the era of GenAI. STRUCTURED ABSTRACT: Introduction Generative artificial intelligence (GenAI) has brought significant while unpredictable changes to the learning motivation of students from various majors including translation and interpreting (T&I). Objectives This study aims to explore learning motivation in the era of GenAI among T&I majors by identifying and interpreting motivational typologies and their characteristics, and to propose pedagogical implications tailored to the identified typologies. Methods Shifting from traditional top-down quantitative approaches to constructionist bottom-up mixed methods, this study adopted Q methodology to collect data from 30 students pursuing the master of T&I at 12 universities in China. Data were processed by means of by-person statistical analysis. Results and discussion Findings show 5 motivational typologies, which are cross-border explorers, pragmatist practitioners, passive receivers, employment seekers, and dream pursuers. The 5 motivational typologies overlap while each maintain peculiar characteristics, indicating that learning motivation is a complex system with various orientations interact within the whole and emerge into different typologies that distinguish from one another while share motivational grounds in some degree. This study also proposes pedagogical implications tailored to the 5 motivations, hoping to offer support for the enhancement of T&I training, the improvement of teaching strategy, and the adjustment of learning psychology in the era of GenAI. Conclusion This study has demonstrated by constructionist and holistic approaches that learning motivation is a complex and dynamic psychological construct. As current GenAI technologies mature and new shifts inevitably arise, learning motivation requires re-examination within new contexts, necessitating further research.