Fine‐Tuning Lightweight LLMs With Human‐Curated Data on Electrical Circuit Fundamentals for E‐Learning

This study examines whether Parameter‐Efficient Fine‐Tuning (PEFT) of lightweight, free, and open‐licensed Large Language Models (LLMs) can yield tutoring assistants for introductory circuit analysis methods, while fitting the students' needs. We analyzed 260 Electrical and Computer Engineering (ECE) exam responses to classify and quantify frequent students' mistakes when applying the Loop Current Method (LCM). Only 28.5% solved the target problem without error, and most difficulties were conceptual (e.g., miscounting the number of independent Kirchhoff's Voltage Law (KVL) equations). Driven by this taxonomy, we assembled official course materials and curated a bilingual (Portuguese–English) pedagogical dataset. Using GTP‐4o for distillation, we generated question‐answer (QA) pairs for fine‐tuning smaller models (Meta Llama 3.2 1B and 3.1 8B), via Quantized Low‐Rank Adaptation (QLoRA) on a single commodity GPU, with an end‐to‐end pipeline completing in under 7 min. A blind study involving 77 first‐year ECE students evaluated responses to (never seen) questions from both our tuned models and GPT‐4.5, rating correctness, clarity, educational value, task coverage, and style. The 8B model scored within one point (5‐point Likert) of GPT‐4.5 model and both 1B and 8B were consistently preferred over untuned baseline versions for clarity and task coverage. As a complementary cross‐check, 12 higher education senior professors independently evaluated model responses, largely corroborating the student‐based rankings. These results provide evidence that carefully curated documents introducing electrical circuit theory, combined with smaller models optimized with PEFT, namely QLoRA, can be used in the construction of a always‐available tutoring application. The proposed system features modest cost, runs on consumer‐grade hardware, and paves the way for deployable front‐end applications that do not involve possibly expensive, resource‐hungry, remote machines.