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Artificial Intelligence in Communication

Code: 106672
Credits: 6
2026/2027
Degree programme Type Course
Interactive Communication OP 4

Contact lecturer

Name :
Oscar Coromina Rodríguez
Email :
oscar.coromina@uab.cat

Group languages

You can consult this information at the end of the document.

Prerequisites

The course is taught in English. This means that lectures will be delivered in English and that reading materials will be primarily in English. Interaction in the classroom with the teaching staff and among students may take place in English, Catalan, or Spanish. Assessment exercises and seminars can be completed in any of these three languages. Theoretical exams will be provided in both English and Catalan.

It is essential that students enrolled in the Interactive Communication degree have completed, passed, and acquired the core competencies of the relevant courses:


Objectives

  • Situate the state of development of Artificial Intelligence (AI) in the historical context.
  • Understand the different techniques for learning and training AIs.
  • Know the main applications of AI in the field of Communication.
  • Understand the ethical, social and economic challenges posed by AI.
  • Understand business models linked to AI applications.
 
 

Learning outcomes

  1. Distinguish the salient features in all types of documents within the subject.
  2. Cross-check information to establish its veracity, using evaluation criteria.
  3. Submit course assignments on time, showing the individual and/or group planning involved.
  4. Identify situations in which a change or improvement is needed.
  5. Propose new methods or well-founded alternative solutions.
  6. Identify the social, economic and environmental implications of academic and professional activities within one's own area of knowledge.
  7. Propose viable projects and actions to boost social, economic and environmental benefits.
  8. Propose projects and actions that are in accordance with the principles of ethical responsibility and respect for fundamental rights and obligations, diversity and democratic values.
  9. Propose projects and actions that incorporate the gender perspective.
  10. Communicate using language that is not sexist or discriminatory.
  11. Critically analyse the principles, values and procedures that govern the exercise of the profession.
  12. Explain the explicit or implicit deontological code in your area of knowledge.
  13. Evaluate the impact of problems, prejudices and discrimination that could be included in actions and projects in the short or medium term in relation to certain people or groups.
  14. Analyse a situation and identify its points for improvement.
  15. Weigh up the risks and opportunities of both one's own and other people's proposals for improvement.
  16. Plan and execute academic projects in the field of big data.
  17. Solve basic problems in big data.
  18. Share experiences with the group as a path to learning, in order to work subsequently in multidisciplinary groups.
  19. Differentiate between the various types of existing architectures for working with big data.
  20. Extract large volumes of data from social networks and the new digital media in particular.
  21. Describe and explain the theoretical and practical particularities of articifial intelligenve in communicative spaces.
  22. Explain and define machine learning, deep learning and data science in the area of communication.

Contents

  1. The Artificial Intelligence (AI) ecosystem
  2. The ethics of AI
  3. Machine learning
  4. Applications of AI in communication
  5. AI and business



Learning activities and methodology

Title Hours ECTS Learning outcomes
Theoric exam 3 0.12 6, 11, 12, 13, 17, 19
Seminars 16 0.64 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 13, 14, 15
Practical exercises 16 0.64 3, 4, 5, 6, 7, 9, 10, 11, 12, 14, 15, 16, 17, 18, 20
Master classes 15 0.6 4, 6, 11, 12, 13, 14, 19
Study: Reading and synthesis of text 56 2.24 4, 6, 8, 11, 13, 14, 19
Tutorials (individual or group face-to-face activity aimed at solving learning problems) 10 0.4 1, 3, 4, 10, 14

The course is structured around three teaching methodologies: lectures, theoretical-practical seminars, and practical exercises focused on the application of artificial intelligence (AI) in communication contexts.

  • Lectures aim to convey the core contents of the syllabus and provide a solid theoretical foundation.
  • Theoretical-practical seminars are designed to connect theoretical concepts with their application through case analysis, discussion, and problem-solving.
  • Practical exercises will allow students to apply the knowledge acquired by designing and developing solutions that integrate AI in real or simulated communication scenarios.

The detailed schedule and session contents will be presented on the first day of class and will also be available on the virtual campus. There, students will find descriptions of the practical exercises, teaching materials, and all necessary information to successfully follow the course.

Attendance and active participation in the seminar and practical exercise sessions are mandatory.

Fifteen minutes of one class session, within the calendar established by the faculty or degree program, will be reserved for students to complete the surveys evaluating the teaching performance and the course or module.

The course content will be sensitive to issues related to gender perspective and the use of inclusive language.


Annotation: within the schedule set by the centre or degree programme, 15 minutes of one class will be reserved for students to evaluate their lecturers and their courses or modules through questionnaires.

Assessment

Continuous assessment activities

Title Weight Hours ECTS Learning outcomes
Theoric Exams 40% 3 0.12 2, 4, 6, 11, 12, 13, 14, 19, 21, 22
Seminars 30% 11 0.44 1, 3, 4, 5, 6, 7, 10, 12, 14, 15, 16
Practical exercises 30% 20 0.8 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 13, 14, 15, 16, 17, 18, 19, 20

This course includes both continuous assessment and single assessment options. To choose the single assessment option, students must notify the teaching staff by October 1st at the latest.

Continuous assessment will be based on four components:

  • Theoretical Exam I (20% of the final grade)
  • Theoretical Exam II (20% of the final grade)
  • Practical Exercises (30% of the final grade)
  • Theoretical-Practical Seminars (30% of the final grade)

The final grade will be the sum of the scores obtained in each component.

To pass the course, students must achieve a passing grade or a minimum average of 4 out of 10 across the theoretical exam.


In the unique assessment mode, the evaluation will be structured as follows:

  • Individual activity: Theoretical exam (40% of the final grade)
  • Individual activity: Project (30% of the final grade)
  • Individual activity: Theoretical-practical seminar (30% of the final grade)

The final grade will be the sum of the scores obtained in each component.

To pass the course, it is necessary to pass or obtain at least 4out of 10 on the theoretical exam.


Optional resit system:


Students will have the right to resit the course only if they have been assessed in the theoretical exams and in 2/3 of the seminars and practical exercises.

Only the theoretical exam is resit-eligible. Seminars and practical exercises cannot be resat unless force majeure is duly justified.

If the average score of the two theoretical exams is below 3, the student will not be eligible for resit.

The maximum grade for the resit theoretical exam will be 6 out of 10.

In this course, the use of Artificial Intelligence (AI) technologies is permitted as an integral part of assignment development, provided that the final outcome demonstrates a significant contribution from the student in terms of analysis and personal reflection. Students must clearly identify any content generated using AI, specify the tools employed, and include a critical reflection on how these technologies have influenced both the process and the final result of the assignment. Failure to disclose the use of AI in this assessed activity will be considered a breach of academic integrity and may result in a partial or total penalty to the assignment grade, or more serious sanctions in severe cases.

Bibliography

Ramírez Gil, William A & Ramiréz Gil, Carlos Mario. Introducción a la inteligencia artificial aplicada al marketing. Ra-Ma. 2023.

Alto, Valentina. Inteligencia artificial generativa con modelos de ChatGPT y OpenAI. Anaya. 2023.

Barceló, Miquel. La intel·ligència Artificial. Editorial UOC. 2005.

Boden, Margaret A. Inteligencia Artificial. Turner Publicaciónes. 2022.

Girón Sierra, José M. Introducción a la Inteligencia Artificial. Editorial Almuzara. 2023.

Ireland, Amy. Filosofía-ficción. Inteligencia Artificial, tecnología oculta y el fin de la humanidad. Holobionte Ediciones. 2022.

López de Mántaras i Badia, Ramon. 100 coses que cal saber sobre intel·ligència Artificial. Cossetània. 2023.

Mitchell, Melanie. Inteligencia Artificial. Guía para seres pensaantes, Capitán Swing. 2024.

 

Specific bibliography for the seminars will be provided during the course.

Software

Code-oriented text editor

Course groups and languages

The information provided is provisional until November 30. After this date, you will be able to consult the language of each group through this link. To access the information, you will need to enter the course CODE

Type of teaching Group Language Semester Shift
(TE) Theory 6 English first semester afternoon
(PLAB) Practical laboratories 61 English first semester afternoon