Academic Knowledge Engineering in the Age of AI
Academic Knowledge Engineering in the Age of AI

Summary/Abstract: A decade ago, the acquisition of expert knowledge relied heavily on structured interviews, cognitive task analysis, conceptual modeling, and specialized tools, requiring extensive involvement of domain experts and knowledge engineers. These classical approaches, although methodologically rigorous, faced limitations related to the tacit nature of expertise, the difficulty of coordinating multiple experts, and the challenges of building, validating, and maintaining large knowledge bases over time. Today, artificial intelligence – particularly large language models (LLMs) – redefines this landscape by enabling automated, scalable, and context-aware extraction, synthesis, and representation of expert knowledge.This paper proposes a methodological framework for knowledge extraction from university educators, combining insights from classical knowledge engineering (e.g., multi-expert validation, rule-based representations, semantic networks, and case-based reasoning) with the capabilities of generative AI. The framework emphasizes structured modeling, intelligent feedback loops, and continuous improvement, ensuring that institutional expertise actively informs teaching and learning. This study contributes a hybrid approach that integrates classical KE rigor with AI-driven automation to achieve scalability, interpretability, and institutional trustworthiness - creating a dynamic academic knowledge ecosystem that evolves with pedagogical needs.

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