The Role of AI-Based Listening Tools in Blended Language Learning Environments

Abstract

Listening comprehension is one of the most challenging yet essential skills for English as a Second Language (ESL) and English as a Foreign Language (EFL) learners. Natural speech complexity and high learner anxiety often hinder the development of effective listening skills. This study examines the effectiveness of AI-based listening support tools, including YouTube Auto-Caption, ELSA Speak, and AI chatbots, in blended learning environments to improve language learning and learner engagement. A descriptive qualitative research design was adopted, involving twelve university-level ESL students selected through purposive sampling based on their experience with AI-powered listening platforms. Semi-structured interviews were conducted to gain in-depth insights into the usefulness of these tools, and the collected data were systematically analyzed and interpreted. The findings indicate that familiarity and accessibility significantly influenced students’ preference for platforms such as YouTube, which was valued for its real-time text visualization. A major advantage identified was the creation of a supportive, low-stress learning environment that reduced listening anxiety while promoting learner autonomy. Participants appreciated the immediate feedback provided by AI tools, which enhanced motivation and independent learning. However, they also highlighted notable limitations, including inaccurate pronunciation, recurring technical issues, and insufficient educational value in automated feedback. The study suggests that AI functions as an “autonomy accelerator” and cognitive scaffold, supporting learners in achieving their Zone of Proximal Development. Rather than replacing conventional teaching methods, AI-based tools should serve as complementary resources within language instruction. Therefore, effective pedagogical design should carefully consider the technical limitations of AI applications and ensure that tool selection aligns with learners’ proficiency levels, instructional objectives, and course relevance to maximize language learning outcomes and sustained learner engagement.