- Credits: 3
- Language: English
- Teaching Mode: Traditional lectures
- Campus: Forli
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Corso:
Second cycle degree programme (LM) in
Specialized translation (cod. 9174)
Also valid for Second cycle degree programme (LM) in Specialized translation (cod. 9174)
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from Feb 21, 2025 to May 16, 2025
Learning outcomes
The student knows the advanced techniques for language service provision in one or more professional settings; s/he is able to analyze and critically assess language services in one or more professional settings, and suggest improvement strategies; s/he is able to acquire further skills related to linguistics, translation and technology, as well as other disciplines of relevance to her/his studies, through interaction with professionals from a range of fields.
Course contents
The activities of this seminar revolve around the research carried out as part of the PRIN project "UNITE –UNiversally Inclusive Technologies to practice English".
The aim of UNITE is to explore the applications of Dialogue Systems (DS) as agents to practice English as a Foreign Language (EFL). DS include, among others, chatbots based on large language model such as ChatGPT e Pi.ai. The ultimate objective of UNITE is to produce teaching and learning materials which favour the uptake of DS as tools for autonomous language learning by university students, including those with disability and specific learning disorders.
At present, the project has led to the creation of a learner corpus of over 300 interactions between learners and DS, collected with the help of SpecTra and TraTec students from the 2023/24 academic year (more information here).
Course participants will be actively involved in ongoing activities carried out within the project, including:
- testing of chatbots for language learning;
- design of an annotation scheme for learner errors;
- annotation of the UNITE corpus data, following the annotation scheme;
- linguistic analysis of learner-DS interactions based on the annotated corpus.
The course is taught jointly with Giada Palmieri and Daniele Polizzi.
Readings/Bibliography
- Bibauw, S., W. Van Den Noortgate, T. François and P. Desmet (2022). Dialogue systems for language learning: a meta-analysis. Language Learning & Technology, 26(1). https://doi.org/10.4324/9781351117586-12
- Huang, W., K. Foon Hew and L. Fryer (2022). Chatbots for language learning — Are they really useful?. Journal of Computer Assisted Learning, 38(1). https://doi.org/10.1111/jcal.12610
- Gilquin, G. (2020). Learner corpora. In M. Paquot and S.Th. Gries (eds.), A Practical Handbook of Corpus Linguistics. Springer, 283-304.
- Lüdeling, A. and H. Hirschmann (2015). Error annotation systems. In S. Granger, G. Gilquin and F. Meunier (eds.), The Cambridge Handbook of Learner Corpus Research. Cambridge University Press, 135-158.
Teaching methods
The seminar combines frontal lectures and practice-based sessions.
Theoretical and methodological contents (e.g. on language learning through the use DS, corpus annotation and learner language) are delivered through presentations by the lecturer.
The practical sessions consist of hands-on activities, which are carried out collaboratively in class or individually as assignments, and are followed by group discussion. The activities are aimed at constantly monitoring progress in the development of the research and technological skills that make the object of the seminar.
All students must attend Module 1 and 2 on Health and Safety online.
Assessment methods
Assessment will be partly in itinere and partly based on a final project.
The in itinere assessment is based on observation of students' participation in the course activities (including assignments to be handed in during the semester). For those who do not hand in assignments, the final mark is entirely based on the final project.
The final project, whose contents and format will be agreed upon with the teachers, will focus on a project of data annotation, accompanied by a brief technical report or oral presentation with slides).
Assessment criteria:
- 30-30L: excellent project work, displaying very high technical quality, presented in a very clear and detailed way.
- 28-29: excellent project work, displaying high technical quality, presented in a clear and detailed way.
- 26-27: good project work, displaying adequate technical quality; some aspects of the presentation could be improved.
- 23-25: fair project work, displaying technical shortcomings; some aspects of the presentation could be improved.
- 20-22: project work displays technical shortcomings; presentation has gaps or is unclear at several points.
- 18-20: project work displays several major technical shortcomings; presentation has many gaps and/or is mostly unclear.
- <18: insufficient or severely insufficient project work.
Teaching tools
Hardware: PC and overhead projector.
Software: Web-based and mobile Dialogue Systems; text editors to process textual data; software for learner error annotation; corpus consultation software.
SDGs


This teaching activity contributes to the achievement of the Sustainable Development Goals of the UN 2030 Agenda.