High-quality feedback is crucial for student learning, yet scaling it across both academic and extracurricular achievements remains difficult. This study introduces an LLM-powered system that generates professional feedback from structured extracurricular records. Using a dataset of 100 secondary students with 1,067 entries, five open-source models (DeepSeek-R1, Gemma3, GPT-OSS, Llama-3.1, Qwen3) produced 500 outputs evaluated across faithfulness, coverage, fluency, coherence, and relevance. Results show that GPT (4.48) and Gemma (4.45) achieved the highest overall means, with medium-sized models (20B–27B) offering the best trade-off between accuracy and inference stability. Strengths included consistently fluent and coherent prose (>4.6), while coverage remained the weakest dimension (3.48–4.25). These findings position LLMs as drafting aids rather than replacements. They can reduce teacher workload and accelerate feedback cycles, provide human oversight to ensure factual alignment, fairness, and contextual appropriateness.