An AI-Assisted Knowledge Management System Prototype for Fisheries Extension Officers: A Usability Evaluation

Authors

  • Dani Saepuloh IPB University, Indonesia
  • Irman Hermadi IPB University, Indonesia
  • Yani Nurhadryani IPB University, Indonesia
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DOI:

https://doi.org/10.63158/journalisi.v8i4.1817

Keywords:

AI-Assisted Knowledge Management System (KMS), Knowledge Mapping, Large Language Model (LLM), STAR Framework, Tacit Knowledge Externalization

Abstract

Fisheries extension officers accumulate tacit knowledge that is rarely documented and at risk of loss as senior officers retire, while existing systems lack structured capture and motivation mechanisms. This study develops and evaluates a Knowledge Management System (KMS) prototype integrating AI-guided narrative acquisition, knowledge mapping, and gamification to support tacit-knowledge externalization. Using PHP, MySQL, and Google Gemini 1.5 Flash, spoken field narratives are structured via the STAR framework under human review, visualized through a weighted expertise graph, and reinforced through a Self-Determination-Theory-based motivational mechanism. Ten fisheries extension officers (five senior, five junior) evaluated the prototype through scenario-based testing, the System Usability Scale, and interviews. Functional black-box testing confirmed that all six core scenarios (authentication, voice-based STAR extraction, moderation, knowledge mapping, gamification, and messaging) executed correctly (6/6, 100% pass rate), and the prototype achieved a mean SUS score of 83.5 (excellent), with qualitative findings indicating reduced perceived documentation burden and increased motivation, alongside dialect-recognition and internet-dependency limitations. This integrated prototype architecture demonstrates preliminary perceived effectiveness rather than validated organizational impact, given the small sample and short-term evaluation.

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References

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Published

2026-08-22

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