An AI-Enhanced Motivational Model for ESP in Aviation: Insights from Chinese Vocational Flight Attendant Training - Zhou, Yan; Soo, Ruey Shing (Bilingual Publishing Group, 2025)
This study investigates motivation in English for Specific Purposes (ESP) among Chinese vocational flight attendant students and proposes an AI-enhanced motivational model grounded in Self-Determination Theory (SDT). Adopting a convergent mixed-methods design, the study combined quantitative and qualitative data sources: a questionnaire survey with 186 students, semi-structured interviews with eight students and three ESP instructors, together with classroom observations, to triangulate findings. The questionnaire established motivational profiles, while interviews and observations provided contextual insights into instructional practices. Descriptive results indicated that students highly value English for both academic achievement and future employment, yet report competence gaps in writing, vocabulary and grammar with speaking identified as the most urgently required skill. Thematic analysis further identified five contextual levers shaping motivation: authenticity of tasks, feedback quality and timeliness, opportunities for interaction, learning environment and class size and technology integration. Interpreted through the SDT framework, these patterns reveal that limited choice and pacing constrain autonomy, lecture-heavy formats weaken relatedness and delayed or generic feedback undermines competence. In response, the study presents an SDT-aligned, AI-enhanced motivational model as a design proposal for piloting in aviation ESP classrooms. The model integrates automatic speech recognition, for instance, individualized feedback, VR-based cabin simulations for authentic scenario practice, AI passengers for interactive role-play and adaptive micro-modules for learner controlled pacing. The study’s originality lies in integrating Self-Determination Theory with AI-supported ESP course design in aviation, extending motivational theory into an under-explored domain.