ECTS credits ECTS credits: 6
ECTS Hours Rules/Memories Hours of tutorials: 1 Expository Class: 10 Interactive Classroom: 40 Total: 51
Use languages Spanish, Galician
Type: Ordinary Degree Subject RD 1393/2007 - 822/2021
Departments: Electronics and Computing
Areas: Computer Science and Artificial Intelligence
Center Higher Technical Engineering School
Call: Second Semester
Teaching: With teaching
Enrolment: Enrollable
In the course Integrative AI Project 1 (PIIA1), students will work in teams to develop an AI project that addresses a real and practical problem proposed by an external entity (companies or institutions). The project will require the integration of skills and knowledge acquired in previous courses, the development of new capabilities and learning, and the enhancement of interpersonal communication, and teamwork skills. Students are expected to be proactive in identifying appropriate methods and techniques and in proposing solutions to the problem addressed in their project.
PIIA1 introduces students to the professional module, which will continue in the fourth year with the subjects that form part of it. Throughout the semester, knowledge of AI models and strategies is integrated with project management training and essential professional skills for the application of the technical knowledge acquired during previous semesters.
The activities to be developed should contribute to:
-Achieving professional competencies by providing a practical perspective on the application of the knowledge and skills required in the job market.
-Offering an up-to-date and global view of the demand for AI solutions across different economic sectors, also highlighting professional career opportunities that can be developed from Galicia.
-Bringing students closer to advances in research and innovation in a highly dynamic market.
The course is structured into three blocks of teaching activities. Two of these focus on developing the knowledge and skills required to tackle an artificial intelligence project that addresses a use case in a specific sector of the economy. The third block is dedicated to the planning, design, and development of the selected project, as well as its public presentation and defense:
• Block 1: Management of the AI project life cycle
Training in techniques and methodologies for planning and managing AI projects from functional, technical, and project delivery perspectives. This block also introduces students to the strategic, regulatory, and market trends that impact the adoption of AI across various economic sectors:
B1.1. Introduction to AI-based projects
B1.2. Market trends in AI projects
B1.3. Ethical, legal, and socio-economic perspectives in AI projects
B1.4. Project management methodologies and standards
B1.5. Team and resource management
B1.6. Management and preparation of project documentation
• Block 2: Professional and communication skills
Training and practice in professional and communication skills that students will apply in the design, development, and presentation of the project:
B2.1. Interpersonal skills and teamwork
B2.2. Conflict resolution
B2.3. Communication and facilitative leadership
B2.4. Effective presentations
B2.5. Communication and marketing
• Block 3: Project execution
Execution of the project, with a comprehensive life cycle management approach:
B3.1. Identification of the challenge/problem – Alignment with the market
B3.2. Development of the practical case
B3.3. Analysis and presentation of results
Given PIIA-1 is a course primarily focused on integrating the knowledge acquired so far in the degree program and follows a challenges and projects based learning methodology, much of the bibliography will be specifically dependent on the chosen project. The specific references, resources, and materials required for the course will be provided by the instructor during the course introduction and when presenting the different topics and objectives.
Throughout the semester, a variety of documentary resources will be used, including traditional bibliographic references as well as audiovisual materials, opinion articles, and socio-economic reports tailored to the presented projects. Each project must include its own sources and documentary references as part of the work to be developed.In any case, below is a (non-exhaustive) list of recommended references and resources that will be used in the course:
-Project Management Institute. (2017). A Guide to the Project Management Body of Knowledge (PMBOK Guide) (6th ed.). Project Management Institute.
-Sutherland, J. (2014). Scrum: The Art of Doing Twice the Work in Half the Time. Crown Business.
-Huyen, C. (2022). Designing Machine Learning Systems: An Iterative Process for Production-Ready Applications. O’Reilly Media, Inc.
-Russell, S., & Norvig, P. (2022). Artificial Intelligence: A Modern Approach (4th ed.). Pearson.
-European Union. (2024). Artificial Intelligence Act. Official Journal of the EU, L123, 1–35.
-European Union. (2016). General Data Protection Regulation (GDPR). Official Journal of the European Union, L119, 1–88.
-Stanford University. (2025). Artificial Intelligence Index Report 2025. Human-Centered AI Institute, Stanford University.
-Gartner. (2024). Hype Cycle for Emerging Technologies 2024.
https://www.gartner.com/en/newsroom/press-releases/2024-08-21-gartner-2…
-Boston Consulting Group. (2024). Where’s the Value in AI? October 24, 2024.
https://web-assets.bcg.com/a5/37/be4ddf26420e95aa7107a35aae8d/bcg-where…
Competences
SPECIFIC
- CE12] Knowing the fundamentals of artificial intelligence algorithms and models for solving problems of a certain complexity, understanding their computational complexity and having the ability to design new models.
- SC15] Knowing and knowing how to apply and correctly explain the validation techniques of artificial intelligence solutions.
TRANSVERSALS
- TR1] Ability to communicate and transmit knowledge, skills and abilities.
- TR2] Ability to work in a team, in interdisciplinary environments and managing conflicts.
- TR3] Ability to create new models and solutions autonomously and creatively, adapting to new situations. Initiative and entrepreneurial spirit.
- TR4] Ability to introduce the gender perspective in models, techniques and solutions based on artificial intelligence.
- TR5] Ability to develop models, techniques and solutions based on artificial intelligence that are ethical, non-discriminatory and reliable.
- TR6] Ability to integrate legal, social, environmental and economic aspects inherent to artificial intelligence, analysing its impacts, and committing to the search for solutions compatible with sustainable development.
PIIA-1 also contributes to the development of the core and general competencies of the degree through practical, challenge-based work dynamics aimed at addressing a problem via the design and execution of an AI project
Learning outcomes
- Be able to identify and know the basic stages necessary to successfully address an AI project.
- Design, develop and evaluate an IA project.
- Write a scientific-technical report of the project carried out.
- Present in public (to teachers and peers) the work carried out, demonstrating and critically communicating the main results achieved with the development of the project.
The teaching methodology, while taking individual work into account, will emphasize teamwork through learning based on activities, practical cases, challenges, and projects, thereby fostering autonomous and proactive learning.
Lectures will introduce students to the strategic, regulatory, and ethical frameworks they must consider when undertaking an AI project. They will also present the key concepts for defining, planning, designing, and managing the project life cycle. Additionally, students will be introduced to teamworking skills, risk management, and effective presentation and communication techniques. These sessions will also include the Interactive Demand Workshops (TID), where the current state of AI adoption across different sectors of the economy will be presented by partner companies and institutions. These collaborators will also present the use cases/challenges that student project teams will be expected to address.
Before the start of the second part of the course (interactive classes), the different student teams (project teams) must be formed, and each team will be assigned one of the proposed use case projects for PIIA1.
The second part of the course will begin with each team already focused on its specific project. Students must go through the phases of planning, design, and execution of the work, culminating in a public presentation and final submission. Throughout the project life cycle, intermediate milestones and deliverables will be established, to be presented and discussed during interactive sessions, and will be subject to evaluation. These sessions will also include time for bilateral meetings between each project team and the teaching staff, considered as project meetings, which will be essential for tracking progress and making key decisions.
To provide students with practical exposure to the professional world, PIIA-1 will include the participation of professionals from companies and institutions that are part of the USC’s Network of Partner Entities for AI Training (RedeECIA-profesional), who will collaborate in the following activities:
Delivery of Professional Seminars (SP): These seminars will provide key insights, methodologies, and techniques for successfully managing and developing AI projects, including strategic, ethical, and legal considerations.
Interactive Demand Workshops (TID): Based on cooperative learning through problem-solving and discussions around proposed use cases, these workshops will help students understand the challenges different economic sectors face in developing AI projects.
Co-supervision of AI Projects (CP): During the TID workshops, a use case map will be presented from which students can select the project they wish to develop. Each project will be assigned a co-supervisor from RedeECIA.
Assessment of the course will be based on the following activities and corresponding weightings:
-Training in project management and professional skills (30%): Includes the preparation of reports, assignments, and the resolution of practical cases, which will be presented and discussed in class.
-Project implementation (70%): Covers the planning, design, development, documentation throughout the project life cycle, and public presentation. This activity is mandatory. The various project deliverables will be presented and discussed in class. Documented project follow-up meetings will help track progress and team performance, which will be considered throughout the continuous assessment process. Participation in class and each team member's proactive attitude in facing project challenges will also be evaluated.
The following mandatory activities and submissions are planned:
- At the end of Block 1 and Block 2, students must complete two practical exercises, which will be presented and discussed in class.
- Throughout Block 3: Project Implementation, students must complete the following partial submissions throughout the project lifecycle, until the final submission and presentation.
AVP1: Project Plan (2nd week of Block 3)
AVP2: Functional and Technical Requirements (4th week of Block 3)
AVP3: Final Submission (penultimate week of Block 3)
AVP4: Presentation and Defense (last week of Block 3)
The course may also include participation in other mandatory activities (such as attendance at talks, seminars, workshops, and technical visits), which will be incorporated into the above evaluation components depending on the type of activity. These will be announced during the course presentation or as it progresses.
The overall grade for each part will be the average of the proposed activities, weighted according to what is specified during the course introduction, and only if a minimum grade of 3 out of 10 is obtained in each activity. Any activity scoring below 3 must be re-evaluated in the second examination session. To pass the course, a minimum grade of 4 out of 10 must be achieved in each component. If this requirement is met, the final course grade will be the weighted average of the two parts, as indicated above. If the minimum score is not reached in one or more components, the final grade will be the lowest score obtained among those components.
Students who do not complete any of the mandatory activities will receive a “Not Presented” grade. To pass the course in the second opportunity, students must complete all pending mandatory parts as specified. Previously obtained grades will be maintained for the remaining components. Given the practical nature of the course, its continuous assessment model, and the centrality of the project’s final delivery and presentation, the project scope may be revised and adapted for second opportunity evaluation depending on specific circumstances.
Due to the highly practical nature of the course and the ongoing supervision of project work, and the absence of a final exam, attendance is mandatory, except in duly justified cases. Although the course is mainly team-based, the teaching staff may conduct individual assessments if the performance of a team does not reflect equal contribution from all members. The public presentation and defense of the project is mandatory to pass the course.
In the case of fraudulent performance of exercises or tests, the provisions of the regulations on the evaluation of students' academic performance and revision of grades will apply (https://www.xunta.gal/dog/Publicados/2011/20110721/AnuncioG2018-190711-…). In application of the ETSE regulations on plagiarism (approved by the Xunta da ETSE on 19/12/2019), the total or partial copying of any practical or theory exercise will result in the failure of the two opportunities of the course, with a grade of 0.0 in both cases (https://www.usc.es/etse/files/u1/NormativaPlagioETSE2019.pdf).
Classroom work time: 51 total hours, divided into 24h (lectures), 26h (practical classes), 1h (tutorials).
Personal work time: 99h (total), divided into 9h (self-study of theory and practicals) and 90h (completion, documentation, presentation and defence of the project).
It is recommended that students have passed all subjects from previous semesters, particularly those related to computer technologies, algorithms, and AI models.
Students are encouraged to develop a project development plan once their project has been assigned, including the definition of tasks and responsibilities for each team member. It is also important that the team maintains consistent activity throughout the semester to ensure the successful completion of the planned tasks, which will generally include not only the delivery of project-related documentation, but also its presentation and discussion.
Students are advised to keep all project documentation updated throughout its life cycle until the final submission, following the methodology that will be presented in class. In addition, weekly project meetings will be held during interactive sessions, serving as a key tool for monitoring the planned timeline of each project. Students are encouraged to make use of these meetings, as well as tutoring sessions, to review any issues encountered, identify risks, and resolve questions or difficulties.
The course will be taught in Spanish and Galician, but some of the bibliography, references, and course materials may be in English.
Teaching will be supported by the USC Virtual platform, which will serve as a repository for course-related materials (texts, presentations, recommended readings, etc.) and a space for student support and tutoring through email and forums.
Maria Del Mar Pereira Alvarez
Coordinador/a- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- mar.pereira.alvarez [at] usc.es
- Category
- Professor: LOSU (Organic Law Of University System) Associate University Professor
Cesar Díaz Parga
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- cesardiaz.parga [at] usc.es
- Category
- Xunta Pre-doctoral Contract
Noel Suárez Barro
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- noel.suarez.barro [at] usc.es
- Category
- Xunta Pre-doctoral Contract
Constanza De La O Andion Garcia
- Department
- Electronics and Computing
- Area
- Computer Science and Artificial Intelligence
- constanza.andion.garcia [at] usc.es
- Category
- Ministry Pre-doctoral Contract
| Tuesday | |||
|---|---|---|---|
| 17:00-18:00 | Grupo /CLE_01 | Spanish | Classroom A4 |
| 18:00-20:00 | Grupo /CLIL_01 | Galician, Spanish | Classroom A4 |
| Thursday | |||
| 17:00-18:00 | Grupo /CLE_01 | Spanish | Classroom A4 |
| 18:00-20:00 | Grupo /CLIL_02 | Galician, Spanish | Classroom A4 |
| 05.22.2026 16:00-20:00 | Grupo /CLE_01 | IA.01 |
| 05.22.2026 16:00-20:00 | Grupo /CLIL_01 | IA.01 |
| 05.22.2026 16:00-20:00 | Grupo /CLIL_02 | IA.01 |
| 05.22.2026 16:00-20:00 | Grupo /CLIL_01 | IA.02 |
| 05.22.2026 16:00-20:00 | Grupo /CLIL_02 | IA.02 |
| 05.22.2026 16:00-20:00 | Grupo /CLE_01 | IA.02 |
| 07.02.2026 16:00-20:30 | Grupo /CLE_01 | IA.01 |
| 07.02.2026 16:00-20:30 | Grupo /CLIL_01 | IA.01 |
| 07.02.2026 16:00-20:30 | Grupo /CLIL_02 | IA.01 |