AI-Driven Listening Systems in Language Acquisition Redefining Auditory Cognition in the Intelligent Era

Abstract This study addresses a critical gap in second language acquisition (SLA) research: how artificial intelligence (AI) can reconcile the tension between implicit input theories (e.g., Nation, 2009) and explicit strategy frameworks (e.g., Richards, 2015) in EFL listening instruction. Using a mixed-methods design, we conducted a 16-week randomized controlled trial (RCT) with 120 Chinese EFL learners (Mage = 19.2), comparing an AI-driven listening system (experimental group, n = 60) with traditional classroom instruction (control group, n = 60). The AI system integrated adaptive content curation (aligning with Nation's "comprehensible input" principle) and real-time strategy prompts (scaffolding Richards' metacognitive model).Key findings include: (1) The experimental group outperformed the control group in post-test listening scores by 11.2 points (M = 79.5 vs. 68.3, d = 1.02, p < 0.001); (2) Anxiety levels decreased by 5.2 points in the AI group (M = 24.5 vs. 29.7, d=-1.05, p < 0.001); (3) Qualitative analysis revealed AI-facilitated learners doubled their use of top-down strategies (e.g., schema activation) over the intervention period. These results validate AI as a "cognitive mediator" that operationalizes both implicit environmental design and explicit strategic training, challenging the traditional dichotomy between input-focused and strategy-focused approaches. The study advances SLA theory by proposing a technology-mediated acquisition framework, with implications for scalable personalized language instruction.