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

The 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.

This 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.

The 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.

This 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.