The teaching methodology will consist of in-class activities, lectures, laboratory activities, workshops and autonomous work.

Innovative methodologies such as problem-based learning and challenge-based learning are included.

A detailed schedule outlining the content of each session will be presented on the first day of the course and will be available on the course’s Virtual Campus, where students will find the various teaching materials deemed appropriate by the instructors and necessary information for effective course monitoring.

Note: 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.

The course consists of the following assessment activities:

• Activity A: Coursework, 30% of the final grade. Three laboratory practicals (2 in groups and 1 individual).
• Activity B: Research project, 30% of the final grade (group work).
• Activity C: 1 exam, 20% of the final grade.
• Activity D: 1 exam, 20% of the final grade.

To pass the course, a minimum average grade of 5 in activities C and D is required. It is mandatory to pass the exams in order to pass the course. The average of the two exams must be at least 5. To be able to calculate the average, a minimum score of 4 in each exam is required, and the combined average of both exams must be at least 5. The exams can be retaken. There is no minimum grade required to sit the resit exam.

It is not mandatory to pass the practical assignments to pass the course. The practicals can only be retaken if they could not be completed for medical reasons (with supporting documentation).

It is not mandatory to pass the coursework project to pass the course. Due to its nature, the coursework project cannot be retaken. During the course there will be 9 practical sessions dedicated to the coursework project. Students who miss more than 2 of these practical sessions (without medical justification) will not be eligible to be assessed for this project.

Students are entitled to a resit of the course if they have been assessed in activities representing at least two-thirds of the total course grade. The research project is excluded from the final resit.

In the case of a second enrolment, students may take a single synthesis exam, which will consist of a test covering both theoretical and practical content. The course grade will correspond to the mark obtained in this synthesis exam.

For this course, the use of Artificial Intelligence (AI) technologies is permitted exclusively as support tools, such as searching for articles and bibliography using the tools indicated in class. Students must clearly identify the parts generated using this technology, specify the tools used, and include a critical reflection on how these have influenced the process and final outcome of the activity. Lack of transparency in the use of AI in this assessed activity will be considered academic dishonesty and may result in a partial or total penalty on the grade of the activity, or more severe sanctions in serious cases.

This course does not include a single assessment system.

Any irregularity in an assessment activity (academic fraud, plagiarism, or improper use of AI, unless such use is expressly authorized in the course syllabus) that could lead to a significant change in the grade will result in that activity being graded as 0. If the syllabus requires a minimum grade in that activity to pass the course, or if multiple irregularities occur in assessment activities within the same course, the final grade for the course will be 0. In addition, disciplinary proceedings may be initiated against the student involved in such irregularities.

Any student suspected of submitting work generated by AI, produced by others, or copied; including AI-generated content without proper citation, or not falling within permitted uses, may be required to provide drafts or other materials demonstrating that the work is original and their own. They may also be asked to explain or justify their work separately. Teaching staff may also use AI detection systems or carry out any checks they deem appropriate. If, after review, irregularities are detected, the work may be graded as zero and the student may be subject to further disciplinary measures.