Program Presentation
The Master in Artificial Intelligence applied to Education is a graduate program designed to accompany teachers and education professionals in an unavoidable transformation: the ethical, critical, and pedagogically sound integration of artificial intelligence (AI) in educational processes.
The rapid expansion of AI-based tools is changing the way we teach, assess, and manage educational environments. In the face of these changes, this master's degree offers rigorous and up-to-date training, oriented both to professional development and to initiation in educational research applied to real Latin American contexts—marked by cultural diversity, digital divides, and the needs for pedagogical innovation.
The program is structured on three essential pillars: pedagogical innovation, advanced digital competencies, and ethical and responsible use of emerging technologies. The opportunities and challenges that AI poses for education are thus critically analyzed: personalization of learning, educational analytics, task automation, immersive environment design and data management, as well as the issues that most concern the global education community: privacy, algorithmic bias, authorship, transparency, and equity.
AI integration in education is a priority not only in Europe—it is also part of the educational plans and agendas of Latin American countries and international organizations, such as UNESCO, UNECLAC, the Inter-American Development Bank (IDB), and ministries of education in the region. In this context, the master's degree prepares its graduates to assume roles including:
- Innovative teachers, capable of designing personalized, inclusive, and ethical experiences supported by AI.
- Researchers in educational AI, with skills to analyze data, evaluate emerging technologies, and create evidence for educational improvement.
- Educational leaders, prepared to promote pedagogical and technological transformation processes in different institutions and educational contexts.
Professional Direction
Throughout the program, students will learn to:
- Integrate AI in the design of classes, courses, and face-to-face, virtual, and hybrid learning experiences.
- Use machine learning tools, natural language processing, adaptive platforms, and learning analytics.
- Implement up-to-date teaching practices based on scientific evidence, appropriate to the educational challenges of the region.
Research Direction
In addition, the master's degree qualifies graduates to:
- Design rigorous research in educational AI applicable to school and community contexts in Latin America, using qualitative, quantitative, and mixed methodologies.
- Analyze and interpret educational data using advanced AI-supported techniques.
- Develop a research project that contributes to the improvement of teaching practice and the educational system from a critical, inclusive, and situated perspective.
Who is the programme for?
The program is aimed at education professionals who wish to integrate artificial intelligence in an ethical, innovative, and pedagogically grounded manner into their practice, as well as those seeking to initiate or strengthen their career in educational research linked to emerging technologies.
It is especially oriented to people with degrees in educational areas, such as the following:
- Undergraduates in Teaching, in any of its specialties.
- Undergraduates in Pedagogy.
- Undergraduates in Social Education.
- Undergraduates in Early Childhood Education.
- Undergraduates in Primary Education.
- Master’s Degree in Education.
- Master’s Degree in University Teaching.
Likewise, university graduates whose education is equivalent or related to the above, either by belonging to new degrees of the university system or by coming from international education systems, may also be admitted.
Requisitos de acceso
Se establece como requisito previo acreditar un nivel B2 del MCERL en lengua inglesa durante el proceso de reserva de plaza y antes de poder formalizar la matrícula (u otros asimilables a juicio competente de la universidad) mediante documentos expedidos por instituciones reconocidas oficialmente como las Escuelas Oficiales u otras entidades acreditadoras reconocidas por la universidad con una antigüedad no superior a 3 años. En el caso de que el postulante no pueda acreditar su nivel de inglés mediante este mecanismo, la propia universidad le realizará una prueba de nivel en la que deberá igualmente obtener, al menos, un nivel de B2.
Diploma
Upon completing the program, students will be awarded the Master's Degree in Bilingual Education , issued by the university in which they are enrolled.
Program Structure
Duration
The Master in Artificial Intelligence applied to Education has 90 credits.
The duration of the program varies between 12 and 17 months, depending on the student's commitment. During this period, students must successfully pass all the corresponding evaluations as well as the Final Project.
Objectives
The following are the learning objectives of this master's degree:
- Interpret educational data using qualitative, quantitative, and mixed methods, applying ethical, validity, cybersecurity, and data protection criteria.
- Employ neural networks, adaptive models, and natural language processing techniques to develop educational tasks, such as sentiment analysis, classification, academic tutoring, and personalization of learning.
- Formulate AI-mediated didactic proposals—including intelligent tutoring, automated assessment, educational analytics, and microlearning—based on pedagogical and technological evidence.
- Apply active methodologies based on emerging technologies (augmented reality, virtual reality, big data, generative AI, and three-dimensional environments) in different disciplinary areas.
- Build learning experiences from a neurodidactic perspective, integrating content, strategies, and disruptive environments that favor collaboration and decision-making.
- Guide teaching teams in the ethical and pedagogical adoption of AI and neuroeducation, promoting attention to diversity, emotional management, and improvement of classroom practices.
- Characterize the fundamentals of artificial intelligence, machine learning, and deep learning, as well as their applications in face-to-face, virtual, and hybrid educational environments.
- Describe the essential principles of neuroeducation, addressing brain functioning, plasticity, executive functions, learning modes, and critical analysis of neuromyths.
- Integrate neuroeducational approaches in the personalization of learning, considering attention, memory, emotions, neurodiversity, and the cognitive gap of the student body.
- Develop a Master's Final Project that coherently articulates the knowledge and skills acquired, aimed at the improvement of real problems in educational contexts.
Career Opportunities
This master's degree prepares students to assume the following roles:
- Innovative teacher, capable of incorporating AI, active strategies, and advanced digital resources in the teaching-learning process.
- Coordinator of educational transformation, responsible for promoting digitalization and institutional improvement projects based on emerging technologies.
- Designer of AI-mediated learning experiences, specialized in creating adaptive content, personalized activities, and virtual or immersive environments.
- Consultant in educational technologies, accompanying institutions, teaching teams, and training projects in the ethical and effective adoption of AI-based solutions.
- Learning analytics specialist, dedicated to interpreting educational data to guide pedagogical decisions and strengthen assessment.
- Researcher in artificial intelligence applied to education, developing studies, pilots, and innovation projects aimed at improving the quality of education.
Study Plan
The curriculum of the Master in Artificial Intelligence Applied to Education consists of 90 credits and is made up of the following subjects:
| MODULE 1: MACHINE LEARNING AND NLP APPLIED TO EDUCATIONAL ENVIRONMENTS | ||
|---|---|---|
| # | SUBJECTS | CREDITS |
| 1 | Machine Learning and Neural Networks | 7 |
| 2 | Natural Language Processing | 6 |
| TOTAL | 13 | |
| MODULE 2: ARTIFICIAL INTELLIGENCE FOR THE IMMERSIVE CLASSROOM | ||
|---|---|---|
| # | SUBJECTS | CREDITS |
| 1 | Design of Educational Environments with Artificial Intelligence | 7 |
| 2 | Application of Artificial Intelligence in the Classroom and Virtual Classroom | 7 |
| 3 | Extended Reality and Artificial Intelligence | 7 |
| TOTAL | 21 | |
| MODULE 3: BIG DATA AND LEARNING ANALYTICS: PERSONALIZATION AND EVALUATION OF LEARNING | ||
|---|---|---|
| # | SUBJECTS | CREDITS |
| 1 | From Data to Information | 7 |
| 2 | Learning Assessment and Artificial Intelligence | 7 |
| TOTAL | 14 | |
| MODULE 4: PROFESSIONAL DEVELOPMENT AND EDUCATIONAL RESEARCH METHODOLOGY | ||
|---|---|---|
| # | SUBJECTS | CREDITS |
| 1 | Teacher Professional Development | 6 |
| 2 | Seminar on Initiation to Research in Artificial Intelligence Applied to Education | 2 |
| 3 | Research Methodologies in Artificial Intelligence Applied to Education | 5 |
| TOTAL | 13 | |
| MODULE 5: EDUCATIONAL NEUROSCIENCE AND IA | ||
|---|---|---|
| # | SUBJECTS | CREDITS |
| 1 | Biological and Genetic Foundations of the Brain | 7 |
| 2 | Neuroscience in the Educational Context | 6 |
| 3 | Educational Neuroscience: Myths and Evidence | 6 |
| TOTAL | 19 | |
| MODULE 6: FINAL PROJECT | ||
|---|---|---|
| # | SUBJECTS | CREDITS |
| 1 | Master's Final Project | 10 |
| TOTAL | 10 | |
*The number of credits and the duration of the master's degree may vary depending on the university issuing the degree.
Description of the Subjects
MODULE 1: MACHINE LEARNING AND NLP APPLIED TO EDUCATIONAL ENVIRONMENTS
- MACHINE LEARNING AND NEURAL NETWORKS
How does machine learning work? What is machine learning? Difference between machine learning and deep learning. Types of machine learning: supervised, unsupervised, and reinforcement learning. Neural networks, data processing, and object detection. Performance comparison between neural networks and machine learning. Practical applications of artificial intelligence in diverse fields, machine learning, deep learning and natural language processing (NLP): task automation, virtual agents, chatbots, conversational AI. Design of personalized learning experiences. Adaptation of contents according to the student's needs. Recommender systems.
- NATURAL LANGUAGE PROCESSING
Natural Language Processing (NLP). Large language models (LLM). Natural language and content representation. Syntactic and semantic analysis. Creation and comprehension of texts. Application of natural language processing systems in teaching. Machine Learning, Language, and Perception: Intelligent Agents and Interaction. Text Mining
MODULE 2: ARTIFICIAL INTELLIGENCE FOR THE IMMERSIVE CLASSROOM
- DESIGN OF EDUCATIONAL ENVIRONMENTS WITH ARTIFICIAL INTELLIGENCE
Principles of Artificial Intelligence and its educational application; generative intelligences to co-evolve and co-create with AI; critical analysis of neuromyths and their impact on planning and instruction; effective implementation of AI in educational environments and analysis of future scenarios; neuroeducational design of AI-supported programs, courses, and content; learning strategies in disruptive environments; development of personalized pathways, practices, and trajectories; creation of content and activities adjusted to learning needs, paces, and styles; curricular integration of AI to promote autonomy and participation; automated assessment and feedback; and technopedagogical planning of experiences, lessons, and materials with AI tools.
- APPLICATION OF ARTIFICIAL INTELLIGENCE IN THE CLASSROOM AND VIRTUAL CLASSROOMS
Trends in e-Learning (personalized learning, microlearning, gamification, and mobile learning); integration of AI in the classroom through adaptive platforms, intelligent tutoring, and data analysis; active methodologies supported by technology (flipped classroom, collaborative learning, EAS, and Visual Thinking); use of emerging technologies, such as virtual and augmented reality, video games, simulations, and metaverse; teaching tools for planning, communication, and content creation; educational programming and robotics; and generative AI for the production of text, images, audio, and the use of chatbots and virtual assistants.
- EXTENDED REALITY AND ARTIFICIAL INTELLIGENCE
Convergence between neuroscience, artificial intelligence, and extended reality: critical review of theoretical frameworks and recent findings. AI applications for research on cognitive processes (memory, attention, decision-making) and adaptive learning design. Analysis of algorithms and platforms for educational personalization: potential, limitations, and ethical debates. Neuroeducational strategies supported by extended reality: multisensory experiences and their impact on attention, memory, and motivation. Critical evaluation of didactic tools based on AI and ER (chatbots, learning analytics, immersive environments). Design and analysis of educational experiences with AI and ER from a research perspective: emerging lines and challenges. Emerging trends in immersive AI.
MODULE 3: BIG DATA AND LEARNING ANALYTICS: PERSONALIZATION AND EVALUATION OF LEARNING
- FROM DATA TO INFORMATION
How educational data informs teaching and the difference between data, information, and knowledge; contextualization strategies and data analytics; digital footprint, privacy, and security in online learning; fundamentals of cybersecurity and data protection in educational platforms; digital responsibility and rights in virtual environments; Big Data and machine learning algorithms applied to education; data mining and prediction of student performance; data collection, cleaning, processing, and visualization for pedagogical decision-making; educational analytics tools from basic to advanced; privacy and security regulations (GDPR, FERPA, and others); ethical considerations in the use of educational data; and development of institutional privacy and information security policies.
- LEARNING ASSESSMENT AND ARTIFICIAL INTELLIGENCE
Learning assessment in the digital era, from teacher observation to automated analysis; potential, limits, and challenges of AI assessment; diagnostic, formative, summative, and self-assessment modalities; learning analytics to detect needs, trajectories, and risk of dropout; personalization of pathways and feedback through AI; adaptive methods and algorithms that adjust difficulty and content; automated assessment with essay correction, intelligent rubrics, and semantic analysis; virtual tutors and intelligent feedback; multimodal assessment with voice, emotion, and engagement analysis; AI-assisted instrument design and validation; algorithmic question construction; ethics, equity, and bias in digital assessment; and practical applications, such as competency-based assessment, digital portfolios, and gamification.
MODULE 4: PROFESSIONAL DEVELOPMENT AND EDUCATIONAL RESEARCH METHODOLOGY
- TEACHER PROFESSIONAL DEVELOPMENT
Redefining teacher education: Learning AI or learning with AI. The role of the teacher as an architect of educational change. Digital skills and competencies of teachers. Reflexivity, digital capabilities, and resilience of teachers: personalized continuing education. Teachers' emotions: keys to their students' learning. Neuroeducation in the classroom: a guide for action. Creating and participating in online practice communities: distributed creativity in the educational context. Creative collaboration between teachers and AI systems: tools and platforms. Educational management and AI: entrepreneurship, innovation, and digital creativity.
- INTRODUCTORY SEMINAR ON ARTIFICIAL INTELLIGENCE APPLIED TO EDUCATION RESEARCH
Research and scientific literacy initiation in artificial intelligence applied to education. Research in artificial intelligence applied to education. Critical reading and analysis of scientific articles: structure of a scientific article; criteria for critical reading; identification of theoretical frameworks and methodological strategies; basic tools for literature analysis. Terminology, publication structures, and sources of scientific information: key terms in artificial intelligence applied to education; typology of scientific publications; databases and specialized academic repositories; search criteria and evaluation of sources; and bibliographic reference managers and APA citation standards. Basic ethics of research and the use of scientific evidence.
- RESEARCH METHODOLOGIES IN ARTIFICIAL INTELLIGENCE APPLIED TO EDUCATION
Research designs in educational and socio-educational contexts; principles of applied research and coherence between problem, objectives, and methodological design; quantitative, qualitative, and mixed data collection methods; triangulation and criteria of validity and reliability; development of initial methodological proposals (formulation of the problem, objectives, hypotheses, questions, sampling, instruments, and analysis techniques); advanced ethical considerations in research with students, teachers, and communities; regulations on data protection and informed consent; responsible management of sensitive information and ethical communication; prevention of cognitive and cultural biases in interpretation; and review of the functioning of protocols and ethical committees in educational research.
MODULE 5: EDUCATIONAL NEUROSCIENCE AND IA
- BIOLOGICAL AND GENETIC FOUNDATIONS OF THE BRAIN
The evolutionary origin of the brain. The brain as part of the nervous system. Neurons, neurotransmitters, and neural networks, Genes and learning. Plasticity and epigenetics. Anatomy and functional morphology of the brain. The formation of the brain: from conception to adulthood.
- NEUROSCIENCE IN THE EDUCATIONAL CONTEXT
Education in context. Learning theories. Position of neuroscience in education. The scientific method in neuroeducation. Learning from neuroscience. Emotional education. Emotions and learning. What is emotional intelligence?
- EDUCATIONAL NEUROSCIENCE: MYTHS AND EVIDENCE
Epistemological foundations of educational neuroscience as an interdisciplinary field, in dialogue with cognitive psychology and education. Critical analysis of neuromyths and other pseudoscientific beliefs in education, based on systematic reviews, meta-analyses, and studies on teachers' scientific literacy. Study of cognitive, metacognitive, and self-regulatory processes (attention, memory, executive functions, and learning monitoring) from an empirical and methodological perspective. Evaluation of scientifically based pedagogical strategies and critical analysis of evidence-based teaching programs and practices. Analysis of research on life habits, brain health, and learning (sleep, diet, physical activity, stress, multitasking) and their educational implications.