Managing Uncertainty in LLM-Generated Procedural Knowledge for Virtual Laboratory Planning
The paper discusses a framework for managing uncertainty in procedural knowledge generated by large language models for virtual laboratory planning. It highlights the challenges of using LLMs to create executable laboratory procedures due to potential inaccuracies in the generated content. The proposed framework aims to improve the reliability of these procedures by transforming uncertain outputs into explicit and inspectable constraints.
- ▪Educational virtual laboratories enhance experimental training accessibility.
- ▪Large language models can assist in generating detailed experimental procedures but may produce incorrect instructions.
- ▪The framework presented aims to reduce procedural uncertainty by using structured domain representations.
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Computer Science > Artificial Intelligence arXiv:2605.26333 (cs) [Submitted on 25 May 2026] Title:Managing Uncertainty in LLM-Generated Procedural Knowledge for Virtual Laboratory Planning Authors:Polychronis Karpodinis, Dimitris Kalles View a PDF of the paper titled Managing Uncertainty in LLM-Generated Procedural Knowledge for Virtual Laboratory Planning, by Polychronis Karpodinis and 1 other authors View PDF Abstract:Educational virtual laboratories can make experimental training more scala-ble, adaptive, and accessible, especially when students have limited access to physical laboratory facilities.
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