Cognitive-Level Adaptive Generation via Capability-Aware Retrieval and Style Adaptation (2025)

Abstract: Large Language Models (LLMs) have demonstrated strong performance in open-ended generation tasks. However, they often struggle to adapt content to users with differing cognitive capacities, leading to a phenomenon we term cognitive misalignment. This issue arises in two forms: knowledge-level misalignment, where content is too complex or too simplistic relative to user understanding, and presentation style misalignment, where the structure or tone hinders effective comprehension. To address these challenges, we propose the Cognitive-Level Alignment Framework (CLAF), a general-purpose generation framework that aligns both knowledge complexity and presentation style with user cognition. CLAF integrates a capability-aware retrieval module based on a hierarchical knowledge graph and a style optimization module guided by Bloom’s taxonomy and preference learning. Additionally, a knowledge-controllable generation component ensures consistency and relevance throughout the output. To support training and evaluation, we construct Scale, a cognitively annotated dataset containing responses at multiple comprehension levels per query. Empirical results show that CLAF enhances the adaptability and informativeness of LLM outputs across a range of user profiles, offering a robust solution to cognitive-level alignment in real-world applications.

## introduction large language models (llms) have shown remarkable capabilities in the field of education @xcite @xcite . building upon these strengths, llms also enable personalized education by adapting their responses to individual learners' needs and communication * corresponding author. ## dataset curation effective research on cognitively aligned text generation needs datasets with two key features: (1) clearly defined cognitive levels, and (2) responses that share the same meaning but vary in depth of explanation and language complexity. existing datasets on personalization @xcite @xcite often focus on user traits or history, but they lack structured cognitive levels and controlled variation in how the answers are written. to address this, we introduce the scale, designed to support controlled generation across different cognitive levels. each item in scale includes a question and three aligned answers: one each at the basic, intermediate, and advanced levels. these levels are based on cognitive development theory (see appendix a), and differ in how ideas are explained while keeping the core meaning intact. to check the quality of the answers, we use a multi-step human evaluation. further information about human evaluation can be found in appendix b. ## methodology this paper presents an innovative framework by aligning generated content with distinct cognitive levels. the framework adapts the scope of knowledge, language styles, and teaching strategies in response to the user's cognitive boundaries. the proposed cognitive level alignment framework (claf) consists of three components: 1) capability-aware retrieval, which delivers rele-vant knowledge tailored to various cognitive levels by retrieving content situated within the user's proximal development zone, as inspired by zpd; 2) adaptive language style optimization (also), which allows the model to employ language styles appropriate for different users by adapting tone and pedagogical strategy based on bloom's taxonomy; and 3) kcg, which dynamically adjusts the scope of knowledge and ensures the output remains faithful to the retrieved content. the overview of the framework is illustrated in figure 3 . ## conclusions we address the challenge of cognitive-level misalignment in llm-based generation by introducing scale, a dataset with tri-level cognitively aligned answers. building on scale, we propose claf, a modular framework that adapts content and style to users' cognitive capacity. experiments show that claf significantly improves cognitive alignment and controllability. this work lays the groundwork for cognitively adaptive generation across education and other user-facing domains. acknowledge:this research was supported by grants from the "pioneer" and "leading goose" r&d program of zhejiang (2025c02022), national natural science foundation of china (no.62307032) and the key research and development program of zhejiang province (no.2024c03270).