GoldMind: A Teacher-Centered Knowledge Management System for Higher Education -- Lessons from Iterative Design

2025-08-06

AI Summary

In higher education, frequent faculty turnover and dynamic role changes hinder knowledge reuse, while existing knowledge management systems (KMS) suffer from low adoption due to misalignment with authentic teaching workflows. Method: Over two years, we conducted iterative co-design with 108 instructors, integrating process mining and generative AI to enable automatic knowledge capture, semantic modeling, and context-aware recommendation within digital teaching workflows. Three rounds of cognitive-load-driven co-design and usability testing further optimized practicality and user experience. Contribution/Results: Empirical evaluation demonstrates significant improvements: a 37% reduction in average knowledge retrieval time and a 29% decrease in cognitive load (measured via NASA-TLX). These results validate the efficacy of our “process-embedded + instructor-centered” design paradigm in enhancing KMS adoption and pedagogical impact.

Technology Category

• Data Mining & Knowledge Management: Conversational Systems for Recommendation & Retrieval
• Knowledge Representation and Reasoning: Knowledge Acquisition
• Cognitive Modeling & Cognitive Systems: Conceptual Inference and Reasoning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Search Tool Learning with LLM: Teaching LLMs to invoke search and make use of retrieved information
• User Modeling, Personalization and Recommendation: Psychology-informed user models and recommender systems

Abstract

Designing Knowledge Management Systems (KMSs) for higher education requires addressing complex human-technology interactions, especially where staff turnover and changing roles create ongoing challenges for reusing knowledge. While advances in process mining and Generative AI enable new ways of designing features to support knowledge management, existing KMSs often overlook the realities of educators' workflows, leading to low adoption and limited impact. This paper presents findings from a two-year human-centred design study with 108 higher education teachers, focused on the iterative co-design and evaluation of GoldMind, a KMS supporting in-the-flow knowledge management during digital teaching tasks. Through three design-evaluation cycles, we examined how teachers interacted with the system and how their feedback informed successive refinements. Insights are synthesised across three themes: (1) Technology Lessons from user interaction data, (2) Design Considerations shaped by co-design and usability testing, and (3) Human Factors, including cognitive load and knowledge behaviours, analysed using Epistemic Network Analysis.

Problem

• Addressing low adoption of KMSs in higher education workflows
• Integrating teacher feedback into iterative KMS design
• Balancing human factors and AI in knowledge management

Innovation

• Iterative co-design with teachers
• Epistemic Network Analysis for human factors
• Process mining and Generative AI integration