A context-aware decision support framework for scientific experiment configurationMiri, Pouriya (Avtor) Stankovski, Vlado (Avtor) Veljković, Kristina (Avtor) Kochovski, Petar (Avtor) contextualisationdecision-makinghuman–AI interactionMarkov decision processMDPscientific experimentIntroduction: Defining an experimental configuration is a complex decision problem for early-stage researchers, who must map goals, constraints, and requirements onto datasets, algorithms, and parameter settings that directly affect experimental outcomes. Existing scientific workflow engines improve execution and reproducibility; however, they rarely capture the decision rationale behind configuration choices, which is needed to inform future selections. Method: We propose a context-aware decision-support framework that formalises experiment configuration as a structured and sequential decision problem. The framework combines three components: a semantic Knowledge Graph (KG) storing historical configurations, contextual attributes, and decision rationale; an MDP-based Option Explorer that filters the KG under user-defined constraints and ranks feasible configurations by expected cumulative reward; and a Graphical User Interface for specifying constraints, inspecting ranked alternatives, and providing structured feedback. Unlike existing workflow systems, the framework explicitly separates user-defined context from automated reasoning, producing an interpretable ranked list rather than a single opaque recommendation. We evaluated the framework in a user study with 90 MSc- and PhD-level researchers performing a model-selection task, using a synthetic dataset of one million experimental configurations under three levels of contextual detail. Results: Compared with manual search, the framework reduced decision time (up to 68%), reduced perceived difficulty (up to 36%), and increased user satisfaction (up to 43%) under the constrained condition. Conclusion: By formalising the link between experimental context and probabilistic decision ranking, the framework improves reproducibility and scalability of decision support in scientific experimentation.20262026-06-23 14:43:50Članek v reviji183995sl