This dataset contains a corpus of 329 annotated conversations between Italian university students (EFL learners) and AI-based chatbots (ChatGPT and Pi.AI), collected between May and December 2024 within the PRIN 2022 project *UNITE – Universally Inclusive Technologies to Practice English*. The corpus includes interactions from three institutions (University of Bologna, University of Macerata, and University of Naples “L’Orientale”) and consists of learner-driven tasks such as small talk and role-play activities. All conversations are annotated using a custom semantic tagset (DIS-TAG). DIS-TAG identifies lexical and discourse features related to mobility, sensory perception, and instructional language, enabling the analysis of normative discourse patterns in chatbot responses.

annotated_UNITE_corpus

Valentina De Brasi;Serena Cecchini;Anna Mongibello
2026-01-01

Abstract

This dataset contains a corpus of 329 annotated conversations between Italian university students (EFL learners) and AI-based chatbots (ChatGPT and Pi.AI), collected between May and December 2024 within the PRIN 2022 project *UNITE – Universally Inclusive Technologies to Practice English*. The corpus includes interactions from three institutions (University of Bologna, University of Macerata, and University of Naples “L’Orientale”) and consists of learner-driven tasks such as small talk and role-play activities. All conversations are annotated using a custom semantic tagset (DIS-TAG). DIS-TAG identifies lexical and discourse features related to mobility, sensory perception, and instructional language, enabling the analysis of normative discourse patterns in chatbot responses.
2026
Inglese
annotated corpus, DISTAG, semantic annotation ai, chatgpt, Pi.AI,
Part of the PRIN project "Universally Inclusive Technologies to Practice English (UNITE)"
3
De Brasi, Valentina; Cecchini, Serena; Mongibello, Anna
info:eu-repo/semantics/other
295
5 Altro::5.10 Banca dati
open
File in questo prodotto:
File Dimensione Formato  
annotated_UNITE_corpus.zip

accesso aperto

Licenza: Creative commons
Dimensione 1.68 MB
Formato Zip File
1.68 MB Zip File Visualizza/Apri

I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.

Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11574/254382
Citazioni
  • ???jsp.display-item.citation.pmc??? ND
social impact