Essay from the year 2026 in the subject Didactics - Common Didactics, Educational Objectives, Methods, grade: 1.0, , course: Master of Arts in Education major in Language Teaching, language: English, abstract: This paper examines AI-powered Speech Recognition Technology (AI-SRT) as a reconceptualized “new language lab” that reshapes pronunciation pedagogy beyond traditional drill-based models. Historically, language laboratories emphasized repetitive listening and imitation; however, AI-SRT transforms this space into an adaptive digital environment capable of analyzing learner speech, detecting phonological deviations, and delivering immediate, individualized feedback. Drawing on Second Language Acquisition theories, including the Interaction Hypothesis, the Noticing Hypothesis, the Affective Filter Hypothesis, and sociocultural perspectives, the paper argues that AI-SRT enhances pronunciation learning through iterative feedback cycles, heightened phonological awareness, and reduced anxiety in oral practice. The analysis highlights three interrelated dimensions of transformation. First, AI-SRT strengthens phonological development by enabling rapid production–evaluation–adjustment sequences that support both segmental and suprasegmental accuracy. Visual and acoustic feedback tools promote metacognitive monitoring, encouraging learners to regulate their own speech production. Second, AI-mediated practice lowers affective barriers by providing a private, low-stakes rehearsal environment, fostering confidence, autonomy, and sustained engagement. Third, the integration of AI in pronunciation learning raises sociocultural considerations concerning intelligibility, identity, and linguistic diversity. While AI systems offer precision and scalability, they may also reinforce standardized norms if not critically mediated. The paper contends that AI-SRT’s pedagogical value depends not on technological novelty but on principled instructional integration. Rather than replacing teachers, AI functions most effectively as a scaffold within communicative, task-based frameworks, where automated feedback informs human-guided reflection and meaningful interaction. By situating AI within intelligibility-oriented pronunciation pedagogy, this study contributes a learner-centered perspective that connects technological affordances with classroom practice. Ultimately, AI-SRT is positioned as a catalyst for holistic speaking development—phonologically robust, psychologically supportive, and socially responsive—when embedded within ethically informed and communicatively grounded teaching contexts.