C9856 - ARTIFICIAL INTELLIGENCE FOR LANGUAGES

Learning outcomes

By the end of this course, students will be familiar with the core principles governing how Artificial Intelligence (AI) systems work, especially as regards large language models, and with the principal types of AI-based tools. They will also be able to assess the advantages and disadvantages of such tools with respect to several use cases, including writing, translation, revision and language analysis, and to use them mindfully. They will be able to critically analyze the role of AI in multilingual and intercultural communication, as well as its ethical, social and professional implications.

Course contents

The course introduces the fundamental principles of Artificial Intelligence (AI), with particular emphasis on Large Language Models (LLMs) and their applications in multilingual communication.

Topics include:

• the history and evolution of AI and language technologies, including Machine Translation (MT);
• the basic principles underlying machine learning, neural MT and LLMs;
  
• effective interaction with AI systems, including prompt design;
• AI-assisted writing, translation, revision and language analysis through practical activities comparing human and AI performance;
• critical assessment of AI output, including issues of accuracy, reliability, bias and hallucinations;
• ethical, legal and societal implications of AI, including copyright, privacy, transparency and environmental sustainability.

Rather than focusing on specific AI tools, the course adopts an AI literacy perspective, equipping students with the conceptual knowledge and critical skills needed to understand, evaluate and use current and future AI technologies responsibly in language-related contexts.

Readings/Bibliography

Getting started with Artificial Intelligence for language-related tasks:

• Pantcheva, Marina. 2024. Compete, Collaborate, or Take Control: The Role of Linguists in an AI Future. Keynote presentation at the Translating Europe Forum 2024, Brussels, Belgium, November 8. YouTube video. https://www.youtube.com/watch?v=6n-Byk1QXEg

Readings on LLMs:

• Jurafsky, Daniel, and James H. Martin. 2026. Speech and Language Processing: An Introduction to Natural Language Processing, Computational Linguistics, and Speech Recognition with Language Models. 3rd ed. Online manuscript released January 6, 2026. Stanford University. https://web.stanford.edu/~jurafsky/slp3/. [Chapter 7]
• Raaijmakers, Stephan. 2025. Large Language Models. Cambridge, MA: The MIT Press. https://doi.org/10.7551/mitpress/15517.001.0001 [Chapters 2, 5, 6, 8, 9]
  

Readings on digital literacy for the language professions:

• Krüger, Ralph. 2024. "Outline of an Artificial Intelligence Literacy Framework for Translation, Interpreting and Specialised Communication." Lublin Studies in Modern Languages and Literature 48 (3): 11–23. https://doi.org/10.17951/lsmll.2024.48.3.11-23.
• Massey, Gary, and Maureen Ehrensberger-Dow. 2026. "Translation Competence in the Age of Generative AI: Debates, Dilemmas, Directions." In Teaching Translation in the Age of Generative AI: New Paradigm, New Learning?, edited by JC Penet, Joss Moorkens, and Masaru Yamada, 3–26. Berlin: Language Science Press. https://doi.org/10.5281/zenodo.17641064.
  
• Secară, Alina, Isabel Rivas Ginel, Antonio Toral, Ana Guerberof, Dragoș Ciobanu, Justus Brockmann, Claudia Plieseis, Raluca Chereji, and Caroline Rossi. 2025. LT-LiDER Language Technology Map: Technologies in Translation Practice and Their Impact on the Skills Needed. Activity Report 2.4. Language and Translation Literacy in Digital Environments and Resources (LT-LiDER). https://doi.org/10.25365/phaidra.641.

Teaching methods

The course combines lectures with practical workshops.

Lectures introduce the theoretical foundations of AI and its role in multilingual communication. Workshops are based on guided hands-on activities in which students perform language-related tasks both independently and with AI tools, compare different solutions, critically evaluate AI-generated output, and reflect on appropriate use of AI in academic and professional contexts. Class discussions and collaborative activities are used throughout the course to encourage critical thinking and informed use of AI.

Assessment methods

Assessment is by written examination held in a computer laboratory. The examination consists of two parts:

• questions assessing students’ understanding of the theoretical foundations of AI/LLMs, as well as their ability to discuss the technical, ethical and societal issues associated with their use (e.g. bias, hallucinations, copyright, privacy, etc.);
• practical tasks requiring students to use AI tools to complete language-related activities (e.g. writing, translation, revision or language analysis), critically evaluate the quality and appropriateness of the AI-generated output, identify its strengths and limitations, and justify the choices made.
  

For this examination, assessment is based on the extent to which the learning outcomes have been achieved. Grades correspond to the following levels of achievement:

• 30L–30, excellent (comprehensive knowledge and understanding, critical awareness, and consistently effective application with no or only negligible inaccuracies);
• 29–28, very good (very good knowledge and understanding, with only minor inaccuracies or omissions);
• 27–25, good (sound knowledge and understanding, with some inaccuracies or gaps);
• 24–21, adequate (adequate achievement of the learning outcomes despite several inaccuracies or gaps); 2
• 0–18, sufficient (minimum acceptable achievement of the learning outcomes, with significant inaccuracies or gaps);
• below 18, insufficient (failure to demonstrate the minimum knowledge and skills required to achieve the learning outcomes).

The use of AI is not permitted in Part 1 of the exam, which assesses students' individual understanding of the course contents. In Part 2, the use of AI is required, as the examination assesses their ability to use AI tools effectively and critically. Students remain fully responsible for the quality of the final output and for the critical evaluation accompanying it.

Students with specific learning difficulties (SpLD) or with disabilities that can affect their ability to attend courses are invited to contact the University service for students with disabilities and SLD at the earliest opportunity – ideally before the start of the course). The University service will suggest possible adjustments to the course work and/or exam, which must then be submitted to the course leader so they can assess their feasibility, in line with the learning objectives of the course. Please note that adjustments to the exam must be requested at least two weeks in advance.

Teaching tools

All course materials are made available through the University’s Virtual Learning Environment (Virtuale), including lecture slides, reading materials, practical exercises, and links to online resources.

Practical sessions require students to have access to one or more general-purpose AI systems (e.g. ChatGPT, Gemini, Claude or DeepSeek), which will be used throughout the course for hands-on exercises and classroom activities.

Office hours

See the website of Adriano Ferraresi