Probabilistic Human-Computer Interaction- Leveraging Bayesian Inference and Causal Models for Adaptive User Interfaces

Human–computer interaction (HCI) increasingly relies on adaptive interfaces that anticipate user intent, reduce cognitive load, and personalize experiences. Traditional machine learning methods for user modeling often focus on predictive accuracy, but they lack the ability to capture uncertainty and causal relationships inherent in human behavior. This paper proposes a probabilistic framework for HCI, integrating Bayesian inference and causal modeling to design adaptive user interfaces that are both robust and interpretable. Bayesian methods allow systems to explicitly quantify uncertainty in user input, while causal models provide insight into the underlying mechanisms driving user decisions (e.g., task complexity, environmental context, emotional state). The proposed framework combines three components: (1) Bayesian user modeling to infer intent from multimodal input signals (keystrokes, gaze, speech); (2) Causal inference to disentangle correlation from causation in user interaction data; and (3) Adaptive interface generation that tailors layout, content, and feedback to maximize usability and efficiency. Preliminary simulations using synthetic interaction logs and real-world datasets (GazeBase, DEAP physiological data, and Keystroke2019) show that Bayesian–causal models outperform baseline deep learning models by 8–12% in intent prediction under noisy conditions, while providing interpretable explanations for adaptive changes. Contributions include: (1) advancing probabilistic HCI by uniting Bayesian and causal reasoning; (2) demonstrating improved robustness in uncertain and multimodal environments; and (3) proposing evaluation metrics for user trust in probabilistic adaptive systems. Future research includes large-scale user studies, integration with AR/VR systems, and exploring ethical concerns in personalized adaptation.