This study evaluates the effectiveness of pretrained language models in identifying argument structure constructions, important for modeling both first and second language learning.We examine three methodologies: (1) supervised training with RoBERTa using a gold-standard ASC treebank, including by-tag accuracy evaluation for sentences from both native and non-native English speakers, (2) prompt-guided annotation with and (3) generating training data through prompts with GPT-4, followed by RoBERTa training.Our findings indicate that RoBERTa trained on gold-standard data shows the best performance.While data generated through GPT-4 enhances training, it does not exceed the benchmarks set by gold-standard data.