AUTHOR=Cao Lei , Abdullah Amelia 

TITLE=EBA (Engaged but Amotivated) in AI-enhanced EFL learning: a qualitative study from a Chinese higher vocational context

JOURNAL=Frontiers in Psychology

VOLUME=Volume 16 - 2025

YEAR=2025

URL=https://www.frontiersin.org/journals/psychology/articles/10.3389/fpsyg.2025.1643653

DOI=10.3389/fpsyg.2025.1643653

ISSN=1664-1078

ABSTRACT=IntroductionThe integration of generative artificial intelligence (GenAI) into English as a Foreign Language (EFL) pedagogy entails both potentials and pitfalls. This study investigates a newly observed phenomenon: the “Engaged but Amotivated” (EBA) learners, who demonstrate behavioral participation yet experience a profound lack of motivation. Grounded in Self-Determination Theory (SDT) and multidimensional engagement framework, the study investigates how GenAI tools subtly influence EFL learners’ motivation and engagement, particularly in low-proficiency vocational contexts.MethodsThis study adopted a qualitative research design within a Chinese higher vocational college, spanning two academic semesters. A rich tapestry of data was meticulously gathered through immersive classroom observations, in-depth semi-structured interviews with 39 first-year EFL students, and trace-based learning management system logs. Thematic analysis was employed to identify nuanced patterns and emergent themes, illuminating the participants’ lived experiences and their intricate interactions with GenAI-enhanced EFL instruction.ResultsThe analysis identified three core themes defining the EBA learner dynamic: ① Performative participation: engagement as institutional compliance; ② Motivational stagnation: cognitive overload as an obstacle; and ③ Identity ambivalence: GenAI as enabler and eroder.DiscussionThis study interrogates the prevailing assumption that visible engagement signifies meaningful learning, cautioning against an overreliance on behavioral indicators in AI-mediated instructional settings, particularly in low-proficiency contexts. It further challenges the widespread optimism surrounding AI’s purported motivational benefits. The findings yield critical implications for pedagogical design, AI system development, and teacher education—particularly within underexplored vocational education contexts.