Large Language Model-Driven Adaptive Learning Path Planning Systems for Language Arts: Advances in Intelligent Engineering Frameworks

Abstract

The emergence of a wide range of language models enables a new generation of adaptive learning path planning system to dynamically customize the trajectory of language art education. This paper uses the ideas of system engineering, network physical system theory and information theory to analyze these systems, and regards them as intelligent and complex engineering objects. Core technology components-such as digital twins for representing learners’ state, reinforcement learning for planning dynamic activities, computer vision for real-time performance detection, and edge computing for low-delay control-are studying whether they can close the feedback loop between learners’ physical interaction and network planning layers. The concrete examples of K-12 China language course and literature analysis academic course show that the participation, mastery time and skill transfer are significantly improved. Nevertheless, there are still significant risks. For example, LLM illusion that causes language errors, cultural bias in training data, privacy issues in continuous CPS data, and the risk of learners and teachers losing their skills. A new idea is to propose a path planning goal to reduce. It measures the fitness value and expected information gain, and extends the classical knowledge tracking to the generation situation. Looking forward to the future, we are considering a mixed architecture with multiple agents, federal updates to protect privacy, and standardized interpretable protocols. The analysis shows that serious engineering disciplines are needed to balance moral and educational protection for sustainable development.