Reinforcement Learning-Driven Optimization of Picture Book Paths for Aesthetic Perception Enhancement

Aesthetic perception, as a core competence in art education, fosters students’ cultural sensibility, emotional expression, and critical thinking. However, existing approaches to cultivating aesthetic perception often lack systematic guidance and personalized developmental pathways, limiting their capacity to support sustained and individualized growth. Two central challenges remain unresolved: first, how to effectively model the dynamic, multidimensional progression of students’ aesthetic understanding, and second, how to construct coherent learning paths that guide students from basic perceptual awareness to more abstract artistic engagement. To address these issues, we propose AesthPath a reinforcement learning-based recommendation model that constructs personalized picture book learning paths to enhance aesthetic perception. Specifically, the model introduces a Markov decision process formulation that captures the evolving states of learners’ aesthetic competence across multiple dimensions. An actor–critic algorithm is then employed to generate adaptive learning trajectories by balancing exploration of new content with the reinforcement of effective materials, based on ongoing learner feedback. Unlike traditional static or rule-based recommendation methods, AesthPath supports fine-grained, feedback-driven optimization of learning trajectories, facilitating goal-oriented and personalized development of aesthetic perception. Experimental results on a real-world dataset demonstrate the effectiveness of AesthPath in enhancing students’ aesthetic understanding. This study offers new theoretical and methodological insights for intelligent learning path design and educational recommendations, highlighting the potential of reinforcement learning in adaptive learning scenarios.