DOCTORATE IN ARTIFICIAL INTELLIGENCE (BLENDED LEARNING)

The
Doctorate in Artificial Intelligence and Applied Algorithmic Ethics is
designed for professionals holding a Master’s degree who seek to
specialize in the development, application, and critical analysis of
artificial intelligence technologies from an ethical, technical, and
social perspective, with a focus on scientific research,
university-level teaching, or leadership in high-impact technological
initiatives.
Eligible applicants include:

• Engineers in Computer Engineering, Computer Systems Engineering, Industrial Engineering, Telecommunications, or Electronics.

• Scientists in Mathematics, Physics, Statistics, Data Science, or Computational Science.

• Professionals from other disciplines,
  including Social Sciences, Philosophy, Law, Health Sciences, and
  Education (with prior experience or complementary training in artificial
  intelligence, technology ethics, or data analytics).

• Develop advanced research competencies in the field of Artificial Intelligence.

• Prepare specialists with strong technical expertise in algorithmic modeling, machine learning, and language processing.

• Apply artificial intelligence solutions to social and sector-specific challenges.

• Design and evaluate intelligent technologies with consideration for human diversity.

• Promote rigorous, ethical, and interdisciplinary training aimed at anticipating technological risks.

• Foster leadership in technological innovation processes.

• Encourage a critical and transformative perspective on technological development.

Career Opportunities

Upon completion of the Doctorate,
graduates will be qualified to hold senior leadership and research
positions, leading projects focused on technological, scientific, and
social innovation within academic, corporate, and public-sector
environments. They will be prepared to design, develop, and manage
advanced artificial intelligence and data science solutions while
applying principles of ethics, efficiency, and reproducibility.
Furthermore, graduates will be qualified to pursue careers in
university-level teaching and specialized research in artificial
intelligence and emerging technologies.
Graduates may pursue the following professional career paths:

• Artificial intelligence specialist

• Data scientist

• Machine learning engineer

• Researcher in natural language processing

• Consultant in ethics and responsible artificial intelligence

• Data and intelligent systems architect

• Director of technological innovation

• University professor and researcher in universities and research centers

• Advisor in technology policy and digital transformation

• Developer of deep learning and automation solutions

• Specialist in smart cities and urban sustainability

• Consultant in data science for decision-making

• Entrepreneur or manager of AI-based technological projects

Study Plan

First and Second Semesters
PHASE I: ADVISING PHASE
During
this initial phase, coordination is established between the student and
the assigned academic advisor. With the support of the advisor, the
student will prepare the preliminary doctoral dissertation outline
proposal. The academic organization of doctoral studies requires the
development of a research work plan, the framework of which will be
developed throughout this advising phase.
Duration: 480 hours
Purpose: To guide the definition of the research topic, objectives, hypotheses, and methodology.

PHASE II: SCIENTIFIC RESEARCH METHODOLOGY
During
this phase of the program, coordination between the student and the
assigned academic advisor continues. The student will continue
developing the doctoral dissertation outline proposal with continuous
guidance from the advisor. Doctoral studies involve the preparation of a
research work plan, whose framework will continue to be developed
throughout the advising phase.
Duration: 240 hours
Purpose: To transform the initial proposal into a robust research plan.

PHASE III: INTRODUCTION TO RESEARCH METHODS
The general objectives of this course are:

• To understand and apply the scientific research process by assessing its objectives, elements, and purposes.

• To identify different types of research, as well as their characteristics and purposes.

• To apply research methods corresponding to the Social Sciences.

• To establish the differences between qualitative and quantitative research.

• To formulate a research problem consistent with its theoretical justification.

• To learn how to formulate scientific hypotheses.

• To interpret and analyze data collected from the research sample.

• To analyze and develop the components of a research project.

• To understand the structure and formal aspects of a doctoral dissertation.

• To use the statistical software package SPSS.

With
the support of the Dissertation Director, the student must prepare the
doctoral dissertation outline proposal, enabling the gradual development
of research competencies.
Duration: 240 hours
Purpose: To develop the doctoral dissertation outline proposal.

Third and Fourth Semesters
PHASE I: TEACHING PHASE
Throughout
this phase, students complete a series of online courses defined
according to their previous academic background and the topic of the
doctoral dissertation. During this phase, doctoral candidates must
complete a set of courses aligned with the selected dissertation topic,
with the first two courses being mandatory and the third corresponding
to an elective option. The academic research activities are described
below.
The mandatory courses are as follows:
A1: Advanced Programming in Python and R (Mandatory)
This
practical course is designed to develop mastery of libraries,
algorithms, and advanced paradigms in data science and artificial
intelligence. Students will learn to develop efficient, reproducible,
and ethically responsible code applicable to both research and
professional environments, with a focus on real-world projects and
process automation.
Objectives:

• To understand the principles of advanced programming applied to data analysis, process automation, and AI model development.

• To apply advanced structures, functions, libraries, and paradigms in Python and R within data science projects.

• To implement efficient algorithms and structures in reproducible working environments.

• To assess the ethical use of code in contexts involving the research, development, and application of artificial intelligence.

Duration: 160 hours
Purpose: To
train students to design advanced solutions in Python and R by
integrating efficiency, reproducibility, and ethics into data science
and artificial intelligence projects.

A2: Data Modeling and Feature Engineering (Mandatory)
This
practical course is designed to develop expertise in Python and R for
data analysis, automation, and AI development. Students will implement
efficient and ethically responsible algorithms applicable to real-world
projects and professional environments, with an emphasis on reproducible
code and innovative solutions.
Objectives:

• To understand the different types of data used in econometric and analytical processes.

• To understand the principles of advanced programming applied to data analysis, process automation, and AI model development.

• To apply advanced structures, functions, libraries, and paradigms in Python and R within data science projects.

• To implement efficient algorithms and structures in reproducible working environments.

• To assess the ethical use of code in contexts involving the research, development, and application of artificial intelligence.

Duration: 160 hours
Purpose: To
prepare experts capable of developing advanced, efficient, and ethical
solutions in Python and R for data analysis, automation, and artificial
intelligence in both research and professional environments.

A3: NLP (Natural Language Processing) and Multicultural Language Processing (Mandatory)
This
course explores the technical and linguistic foundations of Natural
Language Processing (NLP), analyzing its application in tasks such as
classification, translation, and bias detection. Students will learn to
implement models using modern tools while prioritizing ethical,
linguistic, and intercultural sensitivity. The course focuses on
designing inclusive solutions that reflect the diversity of local and
global populations by combining technological innovation with social
responsibility.
Objectives:

• To understand the principles of advanced programming applied to data analysis, process automation, and AI model development.

• To understand the technical and linguistic foundations of Natural Language Processing (NLP) in multicultural contexts.

• To
  analyze NLP architectures and their applications in tasks such as
  classification, sentiment analysis, translation, and bias detection.

• To implement NLP models using modern tools in projects with linguistic, ethical, and intercultural sensitivity.

• To design language-based solutions that respect the cultural and linguistic diversity of local and global populations.

Duration: 160 hours
Purpose: To
prepare specialists in NLP capable of developing technical, ethical,
and multicultural solutions using advanced tools for natural language
analysis and innovation.

A4: Elective I
Duration: 160 hours
Students may choose one of the following courses:

1. Neural Networks and Deep Learning

This
course explores the mathematical and computational foundations of
neural networks, preparing students to implement deep learning models
for classification, regression, and signal processing tasks. Students
will learn to evaluate performance, interpretability, and potential
biases in order to ensure robust and responsible solutions. The
practical approach promotes the design of ethical, efficient, and
reproducible AI systems applicable in research and professional
environments. The course combines advanced theory with real-world
projects aimed at solving technological and social challenges.
Objectives:

• To explain the mathematical and computational functioning of artificial neural networks.

• To implement deep learning models for classification, regression, and complex signal processing tasks.

• To evaluate the performance, interpretability, and potential biases of neural network models in real-world contexts.

• To
  design artificial intelligence solutions based on deep neural networks,
  guided by principles of ethics, efficiency, and reproducibility.

1. Data Science for Decision-Making

This
course addresses the methodological and epistemological foundations of
data science, focusing on its application as a key tool for informed
decision-making. Students will learn to analyze structured and
unstructured data, transforming them into useful, actionable, and
ethically responsible knowledge, particularly in areas such as public
policy, healthcare, education, and economics. Through data mining,
statistical analysis, and visualization techniques, students will
develop practical solutions to real-world problems. The course
emphasizes the design of data-informed interventions guided by
principles of justice, transparency, and effectiveness.
Objectives:

• To understand the methodological and epistemological foundations of data science as a decision-support tool.

• To analyze structured and unstructured data in order to generate useful, actionable, and ethically responsible knowledge.

• To
  apply data mining, statistical analysis, and visualization techniques
  to address challenges in public policy, healthcare, education, or
  economics.

• To design data-informed intervention proposals based on principles of justice, transparency, and effectiveness.

1. Global Ethics in Smart Cities

This
course explores the principles of global ethics applied to the design
and management of smart cities, analyzing the challenges posed by AI,
big data, and sensor technologies in urban environments. Students will
examine ethical, social, and political dilemmas while evaluating urban
technologies through the lenses of justice, equity, privacy, and
sustainability. The practical approach promotes the design of urban
interventions that integrate algorithmic ethics, citizen participation,
and respect for cultural diversity. The course prepares students to lead
responsible technological projects focused on collective well-being.
Objectives:

• To understand the principles of global ethics applied to the design and management of smart cities.

• To
  analyze the ethical, social, and political dilemmas emerging from the
  use of artificial intelligence, big data, and sensors in urban
  environments.

• To evaluate the implementation of urban technologies from perspectives of justice, equity, privacy, and sustainability.

• To
  design urban intervention proposals that integrate algorithmic ethics,
  citizen participation, and respect for cultural diversity.

PHASE II: RESEARCH PHASE
The
objective of this phase is to develop one of the most important
sections of a research study: the justification. In previous phases, the
doctoral dissertation outline was developed, including a summarized
formulation of the research problem and objectives. In this phase, the
goal is to substantiate and justify the reasons motivating the study and
to identify the benefits derived from the research. Students will
briefly describe the contextual and theoretical debate in which the
research is situated, defining its relevance and significance.
Duration: 240 hours
Purpose: To develop a solid and well-argued research justification.

PHASE III: FINDINGS AND ACTIONS OF THE RESEARCH
The
objective of this phase is to establish the models, theories, and
concepts relevant to the research problem in order to support the
analysis and interpretation of findings (analogous studies, literature
review supporting the diagnostic process, and theoretical foundations
for the project design). Students must also explain the methodology to
be employed throughout the development of the doctoral dissertation.
Duration: 240 hours
Purpose: To
establish the theoretical-methodological framework of the research,
interpret findings, and justify the methodological design of the
doctoral dissertation.

Fifth and Sixth Semesters
PHASE I: RESEARCH II
The
objective of this phase is to develop and define the empirical
framework of the doctoral dissertation through the formulation of
objectives, hypotheses, and variables. Students will prepare the
research design and determine the type of research, data collection
methods, research instruments, and target study population. During this
phase, data collection through the proposed instruments will begin.
Duration: 240 hours
Purpose: To develop a solid and well-argued justification for the research.

PHASE II: DOCTORAL DISSERTATION INITIATION PHASE
The
objective of this phase is to develop and define the empirical
framework of the doctoral dissertation through the formulation of
objectives, hypotheses, and variables. Students will prepare the
research design and determine the type of research, data collection
methods, research instruments, and target study population. During this
phase, data collection through the proposed instruments will begin.
Duration: 320 hours
Purpose: To
consolidate the empirical framework of the doctoral dissertation
through the definition of objectives, hypotheses, and variables, the
methodological design, and the initiation of data collection.

PHASE III: DOCTORAL DISSERTATION DEVELOPMENT
The
objective of this research phase is to interpret and write the findings
produced through the analysis conducted in the previous phase. This
interpretation must be supported by graphs, tables, and images, as well
as any appendices necessary for understanding the results. During this
phase, students must also begin preparing the “References” section in
strict accordance with APA guidelines.
Duration: 320 hours
Purpose: To
analyze, interpret, and present the research findings, supporting them
with visual resources and appendices, and to prepare bibliographic
references in accordance with APA standards.

FINAL PHASE: DOCTORAL DISSERTATION PROJECT
During
this final phase, under the supervision of the Dissertation Director,
the student will conduct research activities leading to the completion
of the doctoral dissertation project. The full research study will be
written based on the analysis and findings obtained and will be
incorporated into the advanced research protocol. This final stage also
includes the completion and public defense of the dissertation through a
Public Dissertation Defense.
Duration: 320 hours
Purpose: To
consolidate and complete the doctoral dissertation through the full
writing of the research project, its integration into the advanced
research protocol, and preparation for its public defense.

Research Lines

Research Line 1: AI-Enhanced Human-Computer Interaction
Objective: To
investigate how artificial intelligence can improve interaction between
users and software, focusing on intelligent interfaces, AI-enhanced
accessibility, and adaptive user interfaces that learn from user
behavior.
Research Line 2: Automation and Optimization in Software Engineering through AI
Objective: To
study how AI techniques, such as machine learning, can be applied to
automate repetitive tasks in software development, including code
debugging, optimization of testing processes, and software quality
improvement.
Research Line 3: Software Process Improvement through AI
Objective: To
apply artificial intelligence to improve software development processes
through workflow optimization, AI-enhanced decision-making, and task
automation within the software lifecycle.
Research Line 4: Research Methods in Software Engineering with AI
Objective: To
investigate how AI tools can improve research methodologies in software
engineering, from automating data collection to enhancing code analysis
techniques.
Research Line 5: Optimization and Application of Algorithms in Advanced Computational Systems
Objective: This
research line focuses on the improvement and optimization of algorithms
through AI techniques for applications in advanced systems, such as
simulations and distributed systems, as well as their use in predictive
decision-making within complex systems.