Engineering a Scalable Laboratory Infrastructure for Assembly Language Scaffolding: Design and Deployment of a Locally Optimized GenAI Assistant for the CODE‐2 Educational Architecture

ABSTRACT The transition from high‐level programming to assembly language constitutes a well‐documented pedagogical bottleneck in computer engineering curricula, particularly in large‐cohort laboratory settings where individualized scaffolding cannot scale. This paper presents the design, implementation, and technical evaluation of a locally deployable generative AI assistant engineered specifically for the CODE‐2 educational processor architecture. The system is intended as laboratory infrastructure, not as a replacement for human instruction; its primary contribution is enabling scalable, privacy‐preserving syntax scaffolding without dependency on cloud services or internet connectivity. A synthetic task bank of 50,000 instruction pairs was procedurally generated to cover the full CODE‐2 curriculum. Three fine‐tuning strategies were compared on a consumer GPU: Prompt Tuning, Low‐Rank Adaptation (LoRA), and Full Fine‐Tuning of a T5‐Small encoder‐decoder model. Full Fine‐Tuning achieved 94.10% Exact Match on the held‐out evaluation set, demonstrating that rigid assembly syntax requires full parameter adaptation. Post‐training INT8 quantization via ONNX Runtime reduced inference latency by 69% (from 1689 to 526 ms) on standard laboratory hardware (Intel i5, 8 GB RAM), with a precision loss below 1%. The resulting system operates entirely offline, precluding data exfiltration by design. The system is integrated into laboratory workflows as a supervised scaffolding tool, requiring mandatory emulator‐based verification of all AI‐generated code. Pedagogical implications are discussed as plausible benefits; no controlled learning‐gains study is reported. The work demonstrates a replicable pipeline for building domain‐specific language model infrastructure tailored to CPU‐only educational environments.