Artificial Intelligence in Language Education: Pedagogical Applications, Representative Platforms, and Human Oversight

Abstract

Artificial intelligence is reshaping language education through automated feedback, speech recognition, adaptive practice, dialogue systems, machine translation, learning analytics, and generative language models. This article critically reviews the principal pedagogical uses of these technologies and examines representative AI-assisted language learning platforms. It adopts an integrative review design that prioritizes peer-reviewed research from established indexed journals, supplemented by international guidance and current platform documentation. The synthesis shows that AI can expand opportunities for repeated practice, individualized feedback, formative assessment, and access to multilingual resources. Evidence is strongest when AI supports a clearly defined task and learners remain responsible for interpreting and revising the output. Important limitations include recognition errors, fabricated or culturally narrow responses, opaque scoring, privacy risks, unequal access, academic-integrity concerns, and excessive dependence on automated correction. A comparison of representative platforms demonstrates that products differ substantially in target skills, feedback, adaptivity, and data demands; they should not be treated as interchangeable. The article proposes a six-stage teacher-guided integration cycle covering learning outcomes, tool selection, accountable interaction, verification, assessment, and impact review. The central conclusion is that effective AI-assisted language education depends less on the novelty of a platform than on pedagogical alignment, human oversight, and learners’ developing capacity to evaluate automated output.