Acta Informatica Pragensia X:X | DOI: 10.18267/j.aip.28283

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

Tomislav Mesic ORCID...1,2, Miloslav Hub ORCID...1
1 Faculty of Economics and Administration, University of Pardubice, Pardubice, Czech Republic
2 Algebra Bernays University, Zagreb, Croatia

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.

Keywords: Data science; Psychological data mining; Adaptive user modeling; Expert recommendation systems; Multimodal data processing; Myers-Briggs Type Indicator.

Received: May 22, 2025; Revised: July 8, 2025; Accepted: July 10, 2025; Prepublished online: September 12, 2025 

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