Stochastic dynamics of oxygen-hemoglobin binding: insight through facilitated exploration of an agent-based model

Background Understanding oxygen–hemoglobin binding is fundamental to physiology and clinical practice. However, most students have an exclusively deterministic view of oxygen transport, including the misconception that hemoglobin simply loads oxygen in the lungs and unloads it at the tissues. Developing a conceptual understanding requires appreciation of stochastic molecular interactions, dynamic equilibrium, and population-level averaging of microstates, concepts that are abstract, difficult to grasp, and not typically taught in medical physiology courses. Methods In this mixed methods study, we developed an agent-based model (ABM) in NetLogo to visualize oxygen–hemoglobin binding as a stochastic process. Based on a published quantitative model describing the distribution of hemoglobin bound states as a function of P O 2 , the ABM allows users to explore the effect of changing P O 2 and hemoglobin number while observing stochastic changes in molecular states. Thirty-one second-year medical students participated in a facilitated computer laboratory session and completed pre- and post-intervention quizzes comprising 18 true/false questions. Quantitative data were analyzed using the McNemar test and a paired-samples t-test. Eight days after the facilitated session and quiz completion, four focus groups comprising 28 participants were conducted, and the focus group transcripts underwent inductive and deductive thematic analysis to identify common themes. Results The ABM reproduced the expected oxyhemoglobin equilibrium curve and demonstrated stochastic fluctuations that diminished with increasing numbers of hemoglobin molecules. Quantitative analysis showed that performance improved on all 18 quiz items, with 11 showing statistically significant gains. Mean scores increased from 54.1% to 83.2% (mean difference 29.0%; t(30)=8.7, p=10 -9 ; d z =1.56). Qualitative analysis identified three themes: knowledge construction through active exploration, affective responses associated with intellectual engagement, and mechanistic understanding of oxygen-hemoglobin binding. Findings demonstrated that participants replaced static mental models with an understanding of stochastic oxygen binding and recognized oxygen saturation as a population average rather than the behavior of individual hemoglobin molecules. Conclusion Facilitated agent-based modeling substantially improved conceptual understanding of oxygen–hemoglobin binding. By interactively exploring stochastic molecular dynamics, the intervention promoted active learning, corrected persistent misconceptions, and may provide a transferable framework for understanding emergent physiological phenomena.