Data Science Framework for Adaptive Expert Systems: Psychological Profiling and Knowledge Fusion in Higher Education

Background: Traditional expert systems in education rely on static knowledge bases and rule-based logic, limiting their ability to adapt to the diverse and evolving needs of students.Recent advancements in artificial intelligence and psychological profiling offer new pathways for building personalized support systems.Objective: This study presents a data science framework for developing adaptive expert systems that personalize support delivery using dynamic psychological profiling, with a focus on the higher education domain.Methods: The proposed system integrates five core components: MIND (a multimodal information orchestrator), UEX (expert system knowledge base), ULM (domain-specific large language model), PAGE (a personality-adaptive generative engine), and SYNAPSE (a dynamic profiling module).Psychological personalization is achieved using a multimodel Myers-Briggs Type Indicator (MBTI) classifier developed and validated in a separate study.In this paper, the classifier is operationalized within the system to enable real-time profiling.System behavior is driven by multimodal data fusion and continuously updated based on user interactions.The evaluation focuses on the impact of MBTI-based personalization on user satisfaction, assessed through a controlled survey comparing generic and personalized system responses.Results: In an experiment involving 70 participants with identified MBTI profiles, 79% preferred responses generated by PAGE (psychologically personalized) over generic outputs.This preference was consistent across most MBTI types, indicating the broad applicability of personalization.Minor deviations were observed for types with a preference for concise communication, suggesting variability in personalization effectiveness. Conclusion:The findings demonstrate that embedding psychological profiling into expert system workflows enhances perceived relevance and engagement of system responses.This adaptive framework enables real-time personalization through data-driven profiling, and its modular design allows for deployment across multiple domains.The system's architecture establishes a scalable and behaviorally grounded foundation for next-generation educational support systems.