Large language models excel at statistical pattern recognition but may lack explicit understanding of constructional form-meaning correspondences that characterize human grammatical competence. This paper presents Construction-Aware LoRA (CA-LoRA), a parameter-efficient fine-tuning method that incorporates constructional templates through specialized loss functions and targeted parameter updates. We focus on five major English construction types: ditransitive, caused-motion, resultative, way-construction, and conative. Evaluation on BLiMP, CoLA, and SyntaxGym shows selective improvements: frequent patterns like ditransitive and caused-motion show improvements of approximately 3.5 percentage points, while semi-productive constructions show minimal benefits (1.2 points). Overall performance improves by 1.8% on BLiMP and 1.6% on SyntaxGym, while maintaining competitive performance on general NLP tasks. Our approach requires only 1.72% of trainable parameters and reduces training time by 67% compared to full fine-tuning. This work demonstrates that explicit constructional knowledge can be selectively integrated into neural language models, with effectiveness dependent on construction frequency and structural regularity.