ArticleFrontiers in psychology2026
Towards sustainable AI knowledge-base assistants in computer science education: on-premise deployment and optimization with open educational resources.
Article in Frontiers in psychology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
This study investigates the deployment of artificial intelligence (AI) knowledge-base assistants using open educational resources (OER) for computer science education, demonstrating the feasibility of providing AI-assisted technical support in resource-constrained environments. Our methodology combines structured knowledge extraction from 82 open-licensed Markdown documents, systematic benchmarking of Qwen-7B and DeepSeek-MoE on consumer-grade GPUs (NVIDIA RTX 3060, 12 GB VRAM), and a multi-criteria evaluation framework incorporating response accuracy, computational efficiency, retrieval-augmented generation (RAG)-specific metrics, and energy sustainability. Key results show that Qwen-7B (FP16) achieves 71.5% overall accuracy with 1.4 s average latency, while DeepSeek-MoE (FP16) achieves 79.8% overall accuracy and 82.3% accuracy on multi-hop reasoning tasks. NF4 4-bit quantization reduces VRAM usage by 38.7% for Qwen-7B (6.2 GB → 3.8 GB) and 37.9% for DeepSeek-MoE (5.8 GB → 3.6 GB). Our most energy-efficient configuration consumes 1.8 mWh per query (1.8 Wh per 1,000 queries), measured at the GPU device rail with idle power subtracted. This work makes four contributions: (1) systematic multi-dimensional benchmarks for on-premise, OER-aligned AI knowledge-base assistants in introductory computer science education; (2) hardware-specific deployment guidelines for consumer-grade GPUs without cloud dependency; (3) quantitative evidence that NF4 4-bit quantization-aware fine-tuning preserves educational response utility within 2 percentage points of accuracy degradation while reducing VRAM by 38.7%; and (4) a reproducible evaluation protocol serving as a reference design for future research. The system is characterized as a knowledge-base assistant rather than a validated intelligent tutor; comprehensive pedagogical validation with controlled learner studies remains a priority for future work.
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