EBA (Engaged but Amotivated) in AI-Enhanced EFL Learning: A Qualitative Study from a Chinese Higher Vocational Context

Lei Cao^*

• Wuxi Institute of Technology, Wuxi, China

In the ever-changing landscape of English as a Foreign Language (EFL) education, the integration of generative artificial intelligence (GenAI) has brought both opportunities and challenges for learners. This qualitative study investigates the new dynamic of "Engaged but Amotivated" (EBA) learners, students who actively participate in classroom activities while experiencing a lack of motivation at a Chinese higher vocational college (HVC). Drawing on self-determination theory (SDT) and engagement frameworks, this research explores how GenAI tools shape EFL learners' motivation and engagement. Data from classroom observations, semi-structured interviews with 39 first-year students, and learning management system logs over two semesters were analyzed thematically. Findings reveal three defining characteristics of EBA: (1) performative participation driven by institutional compliance, (2) motivational stagnation compounded by cognitive overload, and (3) identity ambivalence concerning GenAI as both enabler and suppressor of agency. 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.