Generative artificial intelligence is transforming language education by reshaping how learners access input, interact, and receive feedback. While AI tools such as chatbots, adaptive writing assistants, and mobile speaking applications are associated with gains in motivation and performance, less attention has been given to how learners actively regulate their learning within these environments. This conceptual study integrates Proactive Language Learning Theory with a cyclical model of self-regulated learning to explain how learner agency can be intentionally designed in AI mediated contexts. The proposed framework maps proactive behaviors such as input-, interaction-, information-, and feedback-seeking onto the phases of goal setting, performance, and reflection. By translating theory into practical design principles, the study positions AI not as an autonomous tutor, but as a structured ecology that can support proactive, reflective, and self-directed language development.
Ali Mikaeili (Fri,) studied this question.