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All Journal Journal of Electrical Engineering and Computer (JEECOM)
Gede Aristana, I Dewa
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Control & Systems Engineering Electrical & Electronics Engineering Energy

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A Local-First Course-Grounded Learning System Using Parent-Child Retrieval and Quantized Large Language Models Mahajaya, Nyoman Sarasuartha; Gede Aristana, I Dewa; Gede Oka Kessawa Adnyana, Anak Agung; Darma Kotama, I Nyoman; Ngurah Desnanjaya, I Gusti Made
Journal of Electrical Engineering and Computer (JEECOM) Vol 8, No 3 (2026)
Publisher : Universitas Nurul Jadid

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.33650/jeecom.v8i3.17200

Abstract

Conventional Learning Management Systems (LMSs) mainly provide fixed materials and predefined activities, while cloud-based generative artificial intelligence may require external data transmission and produce responses that are not aligned with lecturer-approved course materials. This paper presents a local-first, course-grounded learning system using structured document processing, parent-child Retrieval-Augmented Generation (RAG), semantic retrieval, and quantized Large Language Models (LLMs). The system provides cited conversational responses, source-scoped quiz generation, lecturer moderation, image annotation, and interaction-history recording. Five quantized 7B and 14B models were evaluated with 20 course-grounded questions. The 7B models reached 57.14-58.95 tokens/s using approximately 5 GB of VRAM, whereas Qwen3 14B achieved the highest Faithfulness and Answer Relevancy scores of 0.882 and 0.906. The results demonstrate a trade-off between local inference performance and course-grounded response quality.
Co-Authors Darma Kotama, I Nyoman Gede Oka Kessawa Adnyana, Anak Agung I Gusti Made Ngurah Desnanjaya Nyoman Sarasuartha Mahajaya