(untitled) (Eureka Journal of Education & Learning Technologies, 2026)
This article examines the pedagogical impact potential of artificial intelligence-generated authentic listening tasks on university students’ English listening comprehension competence. Since no original experimental dataset is reported, the study adopts an analytical-model design that defines authenticity, identifies task-development criteria and proposes a measurable intervention for subsequent classroom testing. The analysis integrates CEFR-oriented listening outcomes, metacognitive listening principles, UNESCO guidance on responsible AI use and recent studies of AI-generated listening materials. Findings show that AI can support situationally meaningful dialogues, academic mini-lectures, interview simulations and problem-based audio tasks that promote listening for gist, detail, inference and communicative response. Authenticity, however, cannot be reduced to realistic voice production: it also requires credible purpose, audience, discourse conventions and cultural appropriateness. A four-dimensional authenticity framework and a pre-test/post-test implementation plan are proposed. The article concludes that AI-generated tasks can strengthen listening competence when teachers validate voice quality and context, sequence tasks strategically, and combine generated recordings with human speech and reflective communication.