Teaching Multivariational Reasoning through AI-Guided Inquiry in Interactive Simulations

Understanding how students learn and enact multivariate quantitative inquiry strategies during science inquiry remains challenging, particularly in scalable instructional settings. Although strategies such as the extended Control of Variables Strategy (CVS++) and the General Principle Strategy (GPS) are known to support multivariate reasoning, less is known about how instruction can foster their uptake and sustained use during inquiry.We investigate this issue using a rule-based AI chatbot that scaffolds strategy-specific reasoning during simulation-based inquiry, providing context-sensitive prompts to guide students in enacting either CVS++ or GPS. In a between-groups experiment, 104 chemistry apprentices engage in a three-phase inquiry sequence: inquiry with AI-supported guidance Learning Phase, independent inquiry in the same simulation with a new variable Near Transfer, and inquiry in a novel simulation Far Transfer. Students’ inquiry actions and strategy enactment were captured through fine-grained interaction log data. Results show that CVS++ leads to higher post-instruction performance than GPS, driven primarily by single-variable reasoning items, while no group differences emerge in Near or Far Transfer. Behavioral analyses indicate that students largely enact the instructed strategies in phases close to instruction, but explicit strategy use declines in the novel simulation, particularly for GPS. Across phases, higher learning is consistently associated with more systematic inquiry behaviors, including coordinated exploration, recording, and analysis actions, rather than strategy use alone.