ARTIFICIAL INTELLIGENCE FOR LANGUAGES

The aim of the course is to provide students with the theoretical and practial knowledge needed to understand and critically use artificial intelligence technologies applied to language.

Particular attention will be devoted to generative AI systems, their use in language-related contexts (ranging from text simplification to language teaching support), as well as the ethical and social issues associated with their use.

The course combines general sessions, devoted to the fundamental concepts of AI for language, with topic-specific sessions focusing on particular areas of application, including ethics, language teaching, prompt engineering, text simplification, and the evaluation of outputs generated by these systems.

1. Knowledge and Understanding

- familiarity with the main approaches to AI applied to natural language
- knowledge of the basic terminology and the ability to understand texts that make use of it
- knowledge of the general principles underlying the operation of contemporary language models
- knowledge of the main application areas of AI for language-related tasks
- familiarity with the principal ethical and regulatory issues associated with the use of AI for processing texts and linguistic data

2. Applying Knowledge and Understanding

- ability to use, in an informed and appropriate manner, AI tools for the processing of texts and linguistic data
- ability to design effective prompts for specific linguistic tasks
- ability to assess the linguistic quality of texts generated or modified by AI systems
- ability to integrate AI tools into simple linguistic workflows
- ability to identify the limitations and risks associated with the use of these systems

3. Making Judgements

- ability to critically evaluate texts generated or modified by AI systems
- awareness of the various forms of implicit bias that may be present in texts generated by these systems
- awareness of the risks related to privacy protection, intellectual property, and the opacity of artificial intelligence systems

4. Communication Skills

- ability to describe a use case or an applied project in a clear and well-structured manner
- ability to produce critical analyses of texts generated by artificial intelligence tools
- ability to communicate results, evaluations, and operational proposals to audiences with different backgrounds, including linguists, teachers, students, and communication professionals.

5. Learning Skills

- ability to independently keep up to date with new AI tools for language-related applications
- ability to adapt existing tools to new linguistic tasks

Students are expected to possess:
- basic knowledge of general linguistics
- basic digital skills
- a working knowledge of English sufficient to read scientific materials

The course is organized into general sessions and topic-specific sessions.

GENERAL SESSIONS:

1. Introduction: AI, Applied Linguistics, and Language Technologies
2. From Natural Language Processing to Large Language Models
3. How Generative Language Models Work
4. AI for Text Analysis, Production, and Revision
5. Evaluating AI Outputs

TOPIC-SPECIFIC SESSIONS:

6. Prompt Engineering for Linguistic Tasks
7. AI and Language Teaching
8. AI, Language Assessment, and Feedback
9. AI for Linguistic Simplification
10. AI for Terminology, Lexicon, and Corpora
11. Ethics of AI

Course materials will be provided by the instructor through the University's e-learning platform and will consist of resources, readings, and other supporting documents relevant to the course.

Learning assessment will be based on an oral exam and a final applied project.

THE ORAL EXAM

The oral exam consists of a set of questions aimed to verify students' knowledge of the theoretical issues discussed in class, as well as exercises aimed at testing their mastery of the fundamental methodological concepts presented in class. It also evaluates students' ability to discuss the limitations, risks, and benefits of the technologies covered in the course, as well as their command of basic technical terminology.

FINAL PROJECT

The final project consists of a 3000+ words paper or an applied presentation on a topic agreed upon with the instructor.

The project must include:
- a research question or an applied objective;
- a description of the selected linguistic task;
- a justified choice of one or more AI tools;
- the design of prompts or the workflow;
- examples of inputs and outputs;
- a critical evaluation of the results;
- a reflection on linguistic, methodological, and ethical limitations.

The project will be graded as follows:
- understanding of the problem and critical use of the literature: 30%;
- methodological quality of the workflow or experiment: 25%;
- critical analysis of outputs: 20%;
- ethical awareness and reflection on limitations: 15%;
- linguistic quality and clarity of presentation: 10%.

GRADE BREAKDOWN:

The final grade will be calculated as follows:
- Oral examination: 50% of the final grade
- Final project: 50% of the final grade

oral

The lecturer has a duty to ensure that the rules regarding the authenticity and originality of exam tests and papers are respected. Therefore, if there is suspicion of irregular conduct, an additional assessment may be conducted, which could differ from the original exam description.

A. Scores in the 18–22 range will be awarded in the presence of:
- sufficient knowledge and understanding of the course content;
- limited ability to interpret data and formulate independent judgments;
- sufficient communication skills;

B. Scores in the 23–26 range will be awarded in the presence of:
- fair knowledge and understanding of the course content;
- fair ability to interpret data and formulate independent judgments;
- fair communication skills;

C. Scores in the 27–30 range will be awarded in the presence of:
- good or excellent knowledge and understanding of the course content;
- good or excellent ability to interpret data and formulate independent judgments;
- good or excellent communication skills;

D. Honors ("cum laude") will be awarded in the presence of outstanding knowledge of the course content, judgment, and communication skills.

The course combines introductory lectures, classroom practical exercises, and group activities.

This subject deals with topics related to the macro-area "Human capital, health, education" and contributes to the achievement of one or more goals of U. N. Agenda for Sustainable Development