A Fuzzy Logic Prompting Framework for Large Language Models in Adaptive and Uncertain Tasks
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arXiv
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| Format: | Preprint |
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2025
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| _version_ | 1866918120806940672 |
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| author | Figueiredo, Vanessa |
| author_facet | Figueiredo, Vanessa |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2508_06754 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A Fuzzy Logic Prompting Framework for Large Language Models in Adaptive and Uncertain Tasks Figueiredo, Vanessa Artificial Intelligence I.2.7 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. |
| title | A Fuzzy Logic Prompting Framework for Large Language Models in Adaptive and Uncertain Tasks |
| topic | Artificial Intelligence I.2.7 |
| url | https://arxiv.org/abs/2508.06754 |