A Fuzzy Logic Prompting Framework for Large Language Models in Adaptive and Uncertain Tasks
This work addresses the challenge of enabling LLMs to modulate behavior adaptively in uncertain contexts, such as education and games, without requiring fine-tuning, though it appears incremental as it builds on existing prompting methods.
The paper tackles the problem of making large language models (LLMs) safer and more adaptive for dynamic, user-centered tasks by introducing a modular prompting framework grounded in human learning theory, which improves scaffolding quality, adaptivity, and instructional alignment in a simulated intelligent tutoring setting, outperforming standard prompting baselines.
We introduce a modular prompting framework that supports safer and more adaptive use of large language models (LLMs) across dynamic, user-centered tasks. Grounded in human learning theory, particularly the Zone of Proximal Development (ZPD), our method combines a natural language boundary prompt with a control schema encoded with fuzzy scaffolding logic and adaptation rules. This architecture enables LLMs to modulate behavior in response to user state without requiring fine-tuning or external orchestration. In a simulated intelligent tutoring setting, the framework improves scaffolding quality, adaptivity, and instructional alignment across multiple models, outperforming standard prompting baselines. Evaluation is conducted using rubric-based LLM graders at scale. While initially developed for education, the framework has shown promise in other interaction-heavy domains, such as procedural content generation for games. Designed for safe deployment, it provides a reusable methodology for structuring interpretable, goal-aligned LLM behavior in uncertain or evolving contexts.