Latent Learning State Estimation (LLSE): A Multimodal Conceptual Framework for Inferring Learner States in AI-Supported English Language Education

Latent Learning State Estimation (LLSE) is introduced as a multimodal conceptual framework designed to address a persistent limitation in AI-supported English language education: the overreliance on observable performance metrics such as accuracy, test scores, and task completion. While these indicators provide valuable information, they often fail to capture the underlying cognitive, affective, and behavioral conditions that shape learner development, leading to incomplete interpretations of learning progress. Inspired by the observation that experienced language educators naturally infer hidden learner states through subtle behavioral cues, LLSE seeks to computationally model this inference process through the estimation of learners' evolving latent states from multimodal behavioral evidence. The framework integrates multimodal behavioral signals, temporal learning patterns, and learner–environment interactions to estimate dynamic representations of learners' cognitive engagement, affective responses, and behavioral tendencies beyond observable outcomes. The principal contribution of LLSE is the establishment of a conceptual foundation for intelligent educational systems capable of understanding not only what learners achieve but also how they engage, adapt, and progress throughout the learning process. By shifting the focus from outcome-centered evaluation to process-centered inference, LLSE provides a research direction for developing more adaptive, context-aware, and learner-centered AI systems for English language education, thereby contributing to the broader field of multimodal learning analytics.

کلیدواژه ها:

Artificial Intelligence ، English Language Education ، Latent Learning State ، Multimodal Learning Analytics ، Adaptive Learning ، Affective Computing