Design and Effectiveness Evaluation of Personalized Learning Paths for Ancient Chinese Literature Driven by Intelligent Inference Algorithms

Abstract

Current learning of ancient Chinese literature is constrained by complex content structures and insufficient intelligent guidance, resulting in limited learning efficiency and weak learner engagement. To address individual differences in reading ability, literary background, and learning interest, this paper constructs a personalized learning path generation model based on an intelligent recommendation algorithm. The method first collects learners’ reading records, assessment scores, and behavioral characteristics, and calculates text similarity through a semantic vector model. An improved Bayesian inference algorithm is then used to optimize relevance weights and dynamically recommend learning resources. Reinforcement learning strategies are further introduced to iteratively optimize path effectiveness, while a learning curve prediction model is used to evaluate learning efficiency improvement. The experiment is conducted in university ancient Chinese literature courses with 120 students. The control group follows a conventional teaching path, while the experimental group uses the intelligent recommendation model. Results show that the experimental group improves learning efficiency by 31.6%, increases self-directed learning enthusiasm by 28.4%, achieves an average score 9.2 points higher than the control group, and shows a significant increase in the learning interest index (p < 0.01).