This paper reports on a study aimed at comparing AI vs. human performance in detecting and categorising errors in L2 Italian texts. Four LLMs were considered: ChatGPT, Copilot, Gemini and Llama3. Two groups of human annotators were involved: L1 and L2 speakers of Italian. A gold standard set of annotations was developed. A fine-grained annotation scheme was adopted, to reflect the specific traits of Italian morphosyntax, with related potential learner errors. Overall, we found that human annotation outperforms AI, with some degree of variation with respect tospecific error types. An increased attention to languages other than English in NLP may significantly improve AI performance in this pivotal task for the many domains of language-related disciplines.

We present an evaluation of three different methods for the automatic identification of candidate collocations in corpora, part of a research project focused on the development of a learner dictionary of Italian collocations. We compare the commonly used POS-based method and the syntactic dependency-based method with a hybrid method integrating both approaches. We conduct a statistical analysis on a sample corpus of written and spoken texts of different registers. Results show that the hybrid method can correctly detect more candidate collocations against a human annotated benchmark. The scores are particularly high in adjectival modifier rela- tions. A hybrid approach to candidate collocation identification seems to lead to an improvement in the quality of results.