Human-Centered Artificial Intelligence in EFL Listening: Emotional Adaptivity, Gamified Feedback, and Motivational Scaffolding toward Next-Generation Learning Systems

Abstract Listening comprehension constitutes one of the most cognitively demanding yet underemphasized components of English language education, particularly within adolescent EFL classrooms. Addressing this overlooked area, the present mixed-methods research explores the influence of human-centered artificial intelligence (AI) on learners’ listening comprehension, engagement, and motivation. Two learning environments with distinct instructional architectures were compared under controlled classroom conditions: a gamified adaptive system structured around motivational feedback loops and progression tracking, and a conversational emotion-adaptive AI interface designed to foster reflective autonomy. Grounded in Schema Theory, Self-Regulation Theory, Dynamic Assessment within Sociocultural Theory, and Gamification Theory, the study integrates these perspectives through two constructivist frameworks—the AI-Enhanced Language Learning Matrix (AELLM) and the Intelligent Learning Environment Design (ILED)—and extends them by proposing Tajik’s Scaffolded Motivator Model (T-SMM). Participants consisted of 53 Iranian high-school learners (aged 14–15) engaged in a 12-week instructional program involving 24 sessions. Quantitative outcomes revealed robust gains in listening proficiency and self-efficacy, while qualitative data highlighted sustained emotional engagement and adaptive autonomy within the human-centered learning design. The findings suggest that emotionally adaptive feedback and gamified motivational scaffolding act as key mediators in supporting deeper cognitive processing and consistent learner participation. By synthesizing theoretical and empirical insights, this study redefines motivational scaffolding as a critical mechanism driving the effectiveness of next-generation, AI-supported EFL listening instruction and offers tangible implications for future intelligent learning system design.