Through this 100% online program, you will integrate generative Artificial Intelligence tools in the planning, implementation and evaluation of educational activities”

In order to optimize educational projects, teachers use AI tools to enrich the students' experience. However, to achieve the expected results, professionals need to have a broad knowledge of AI application strategies in the classroom. They will be able to develop resources such as chatbots, dynamic learning games and even tools to assess student performance.

In this context, TECH implements this program in Artificial Intelligence in Education , where the associated ethical, legal and social considerations will also be addressed. With an eminently practical approach, teachers will acquire tangible skills to implement AI procedures in the educational environment. Graduates will delve deeper into teaching praxis by focusing on actors such as personalization of learning and continuous improvement, which are indispensable for adaptability in the educational process. Finally, the syllabus will analyze in detail the emerging trends in AI for Education, ensuring that participants are aware of the latest innovations in educational technology.

It should be noted that this university program is based on a 100% online methodology so that students can learn at their own pace. To do so, the only thing they will need to access the resources is a device with Internet access. The academic itinerary stands out for relying on the innovative Relearningmethod. This is a teaching model supported by the repetition of the most important content, in order to make the knowledge last in the students' minds. To enrich learning, the materials are complemented by a wide variety of multimedia resources (such as interactive summaries, supplementary readings or infographics) to reinforce knowledge and skills. In this way, students will learn gradually and naturally, without having to resort to extra efforts such as memorization. 

Want to facilitate instant feedback? With this university program you'll identify areas for improvement and offer personalized support”

This professional master’s degree in Artificial Intelligence in Education contains the most complete and up-to-date program on the market. The most important features include:

• The development of case studies presented by experts in Artificial Intelligence in Education
• The graphic, schematic and practical contents of the book provide theoretical and practical information on those disciplines that are essential for professional practice
• Practical exercises where self-assessment can be used to improve learning
• Its special emphasis on innovative methodologies  
• Theoretical lessons, questions to the expert, debate forums on controversial topics, and individual reflection assignments 
• Content that is accessible from any fixed or portable device with an Internet connection 

Thanks to the revolutionary Relearning methodology, you will integrate all the knowledge in an optimal way to successfully achieve the results you are looking for"

The program’s teaching staff includes professionals from the field who contribute their work experience to this educational program, as well as renowned specialists from leading societies and prestigious universities.

The multimedia content, developed with the latest educational technology, will provide the professional with situated and contextual learning, i.e., a simulated environment that will provide immersive education programmed to learn in real situations.

This program is designed around Problem-Based Learning, whereby the professional must try to solve the different professional practice situations that arise during the academic year For this purpose, the students will be assisted by an innovative interactive video system created by renowned and experienced experts.  

You will drive innovation and continuous improvement in education through the responsible use of technology"

You will have an advanced and exclusive program and will be able to face the challenges of the educational landscape driven by Machine Learning"

This professional master’s degree will provide teachers with both the skills and knowledge required to revolutionize the educational landscape. By combining AI with modern pedagogy, graduates will be able to develop personalized learning environments. In addition, they will foster innovation within the classroom and design educational strategies tailored to the needs of learners. Moreover, experts will gain a comprehensive prism to master AI applications, optimizing the teaching-learning process. In this way, specialists will be able to more than meet the challenges and cultivate a much more efficient education.

In just over a year, you will give your career the boost it needs and master the most sophisticated technology to enrich your teaching praxis”

General Objectives

• Understand the theoretical foundations of Artificial Intelligence
• Study the different types of data and understand the data lifecycle 
• Evaluate the crucial role of data in the development and implementation of AI solutions 
• Delve into algorithms and complexity to solve specific problems 
• Explore the theoretical basis of neural networks for Deep Learning development 
• Analyze bio-inspired computing and its relevance in the development of intelligent systems
• Analyze current strategies of Artificial Intelligence in various fields, identifying opportunities and challenges
• Understand the fundamental ethical principles related to the application of AI in educational settings
• Analyze the current legislative framework and the challenges associated with the implementation of AI in educational settings 
• Encourage the responsible design and use of AI solutions in educational contexts, considering cultural diversity and gender equity 
• Provide an in-depth understanding of the theoretical foundations of AI, including machine learning, neural networks, and natural language processing 
• Understand the applications and impact of AI in teaching and learning, critically assessing its current and potential uses 

Specific Objectives

Module 1. Fundamentals of Artificial Intelligence  

• Analyze the historical evolution of Artificial Intelligence, from its beginnings to its current state, identifying key milestones and developments
• Understand the functioning of neural networks and their application in learning models in Artificial Intelligence
• Study the principles and applications of genetic algorithms, analyzing their usefulness in solving complex problems
• Analyze the importance of thesauri, vocabularies and taxonomies in the structuring and processing of data for AI systems
• Explore the concept of the semantic web and its influence on the organization and understanding of information in digital environments

Module 2. Data Types and Data Life Cycle  

• Understand the fundamental concepts of statistics and their application in data analysis
• Identify and classify the different types of statistical data, from quantitative to qualitative data 
• Analyze the life cycle of data, from generation to disposal, identifying key stages 
• Explore the initial stages of the data life cycle, highlighting the importance of data planning and structure 
• Study data collection processes, including methodology, tools and collection channels 
• Explore the Datawarehouse concept, with emphasis on the elements that comprise it and its design 
• Analyze the regulatory aspects related to data management, complying with privacy and security regulations, as well as best practices

Module 3. Data in Artificial Intelligence  

• Master the fundamentals of data science, covering tools, types and sources for information analysis
• Explore the process of transforming data into information using data mining and visualization techniques
• Study the structure and characteristics of datasets, understanding their importance in the preparation and use of data for Artificial Intelligence models
• Analyze supervised and unsupervised models, including methods and classification 
• Use specific tools and best practices in data handling and processing, ensuring efficiency and quality in the implementation of Artificial Intelligence 

Module 4. Data Mining. Selection, Pre-Processing and Transformation 

• Master the techniques of statistical inference to understand and apply statistical methods in data mining
• Perform detailed exploratory analysis of data sets to identify relevant patterns, anomalies, and trends 
• Develop skills for data preparation, including data cleaning, integration, and formatting for use in data mining 
• Implement effective strategies for handling missing values in datasets, applying imputation or elimination methods according to context 
• Identify and mitigate noise present in data, using filtering and smoothing techniques to improve the quality of the data set 
• Address data preprocessing in Big Data environments 

Module 5. Algorithm and Complexity in Artificial Intelligence  

• Introduce algorithm design strategies, providing a solid understanding of fundamental approaches to problem solving 
• Analyze the efficiency and complexity of algorithms, applying analysis techniques to evaluate performance in terms of time and space 
• Study and apply sorting algorithms, understanding their performance and comparing their efficiency in different contexts 
• Explore tree-based algorithms, understanding their structure and applications 
• Investigate algorithms with Heaps, analyzing their implementation and usefulness in efficient data manipulation 
• Analyze graph-based algorithms, exploring their application in the representation and solution of problems involving complex relationships 
• Study Greedyalgorithms, understanding their logic and applications in solving optimization problems
• Investigate and apply the backtracking technique for systematic problem solving, analyzing its effectiveness in various scenarios

Module 6. Intelligent Systems  

• Explore agent theory, understanding the fundamental concepts of its operation and its application in Artificial Intelligence and software engineering
• Study the representation of knowledge, including the analysis of ontologies and their application in the organization of structured information
• Analyze the concept of the semantic web and its impact on the organization and retrieval of information in digital environments
• Evaluate and compare different knowledge representations, integrating these to improve the efficiency and accuracy of intelligent systems 
• Study semantic reasoners, knowledge-based systems and expert systems, understanding their functionality and applications in intelligent decision making

Module 7. Machine Learning and Data Mining 

• Introduce the processes of knowledge discovery and the fundamental concepts of machine learning
• Study decision trees as supervised learning models, understanding their structure and applications
• Evaluate classifiers using specific techniques to measure their performance and accuracy in data classification
• Study neural networks, understanding their operation and architecture to solve complex machine learning problems 
• Explore Bayesian methods and their application in machine learning, including Bayesian networks and Bayesian classifiers 
• Analyze regression and continuous response models for predicting numerical values from data 
• Study clustering techniques to identify patterns and structures in unlabeled data sets 
• Explore text mining and natural language processing (NLP), understanding how machine learning techniques are applied to analyze and understand text 

Module 8. Neural Networks, the Basis of Deep Learning  

• Master the fundamentals of Deep Learning, understanding its essential role in Deep Learning 
• Explore the fundamental operations in neural networks and understand their application in model building
• Analyze the different layers used in neural networks and learn how to select them appropriately 
• Understanding the effective linking of layers and operations to design complex and efficient neural network architectures 
• Use trainers and optimizers to tune and improve the performance of neural networks 
• Explore the connection between biological and artificial neurons for a deeper understanding of model design 
• Tuning hyperparameters for Fine Tuning of neural networks, optimizing their performance on specific tasks 

Module 9. Deep Neural Networks Training  

• Solve gradient-related problems in deep neural network training 
• Explore and apply different optimizers to improve the efficiency and convergence of models 
• Program the learning rate to dynamically adjust the convergence speed of the model 
• Understand and address overfitting through specific strategies during training 
• Apply practical guidelines to ensure efficient and effective training of deep neural networks 
• Implement Transfer Learning as an advanced technique to improve model performance on specific tasks 
• Explore and apply Data Augmentation techniques to enrich datasets and improve model generalization 
• Develop practical applications using Transfer Learning to solve real-world problems 
• Understand and apply regularization techniques to improve generalization and avoid overfitting in deep neural networks 

Module 10. Model Customization and Training with TensorFlow  

• Master the fundamentals of TensorFlowand its integration with NumPy for efficient data management and calculations
• Customize models and training algorithms using the advanced capabilities of TensorFlow 
• Explore the tfdata API to efficiently manage and manipulate datasets 
• Implement the TFRecord format for storing and accessing large datasets in TensorFlow 
• Use Keras preprocessing layers to facilitate the construction of custom models 
• Explore the TensorFlow Datasets  project to access predefined datasets and improve development efficiency 
• Develop a Deep Learning application with TensorFlow, integrating the knowledge acquired in the module 
• Apply in a practical way all the concepts learned in building and training custom models with TensorFlow in real-world situations 

Module 11. Deep Computer Vision with Convolutional Neural Networks  

• Understand the architecture of the visual cortex and its relevance in Deep Computer Vision 
• Explore and apply convolutional layers to extract key features from images 
• Implement clustering layers and their use in  Deep Computer Vision models with Keras 
• Analyze various Convolutional Neural Network (CNN) architectures and their applicability in different contexts 
• Develop and implement a CNN ResNet using the Keras library to improve model efficiency and performance 
• Use pre-trained Keras models to leverage transfer learning for specific tasks 
• Apply classification and localization techniques in Deep Computer Vision environments 
• Explore object detection and object tracking strategies using Convolutional Neural Networks 
  Implement semantic segmentation techniques to understand and classify objects in images in a detailed manner 

Module 12. Natural Language Processing (NLP) with Natural Recurrent Networks (NNN) and Attention

• Developing skills in text generation using Recurrent Neural Networks (RNN) 
• Apply RNNs in opinion classification for sentiment analysis in texts 
• Understand and apply attentional mechanisms in natural language processing models 
• Analyze and use Transformers models in specific NLP tasks 
• Explore the application of Transformers models in the context of image processing and computer vision 
• Become familiar with the Hugging Face  Transformers library for efficient implementation of advanced models 
• Compare different Transformers libraries to evaluate their suitability for specific tasks 
• Develop a practical application of NLP that integrates RNN and attention mechanisms to solve real-world problems 

Module 13. Autoencoders, GANs, and Diffusion Models  

• Develop efficient representations of data using Autoencoders, GANs and Diffusion Models
• Perform PCA using an incomplete linear autoencoder to optimize data representation 
• Implement and understand the operation of stacked autoencoders 
• Explore and apply convolutional autoencoders for efficient visual data representations 
• Analyze and apply the effectiveness of sparse automatic encoders in data representation 
• Generate fashion images from the MNIST dataset using Autoencoders 
• Understand the concept of Generative Adversarial Networks (GANs) and Diffusion Models 
• Implement and compare the performance of Diffusion Models and GANs in data generation 

Module 14. Bio-Inspired Computing   

• Introduce the fundamental concepts of bio-inspired computing
• Explore social adaptation algorithms as a key approach in bio-inspired computing 
• Analyze space exploration-exploitation strategies in genetic algorithms 
• Examine models of evolutionary computation in the context of optimization  
• Continue detailed analysis of evolutionary computation models  
• Apply evolutionary programming to specific learning problems 
• Address the complexity of multi-objective problems in the framework of bio-inspired computing 
• Explore the application of neural networks in the field of bio-inspired computing  
• Delve into the implementation and usefulness of neural networks in bio-inspired computing 

Module 15. Artificial Intelligence: Strategies and Applications 

• Develop strategies for the implementation of artificial intelligence in financial services
• Analyze the implications of artificial intelligence in the delivery of healthcare services 
• Identify and assess the risks associated with the use of AI in the healthcare field 
• Assess the potential risks associated with the use of AI in industry 
• Apply artificial intelligence techniques in industry to improve productivity 
• Design artificial intelligence solutions to optimize processes in public administration 
• Evaluate the implementation of AI technologies in the education sector 
• Apply artificial intelligence techniques in forestry and agriculture to improve productivity 
• Optimize human resources processes through the strategic use of artificial intelligence 

Module 16. Data analysis and application of AI techniques for educational personalization 

• Apply AI in the analysis and evaluation of educational data to drive continuous improvement in educational settings 
• Define academic performance indicators based on educational data to measure and improve student performance 
• Implement AI technologies and algorithms to perform predictive analytics on academic performance data 
• Perform personalized diagnostics of learning difficulties through data analysis with AI, identifying particular educational needs and designing targeted interventions 
• Address security and privacy in the processing of educational data when applying AI tools, ensuring regulatory and ethical compliance 
• Module 17. Development of Artificial Intelligence projects in the classroom 
• Plan and design educational projects that effectively integrate AI in educational environments, mastering specific tools for its development
• Design effective strategies to implement AI projects in learning environments, integrating them in specific subjects to enrich and improve the educational process
• Develop educational projects applying machine learning to improve the learning experience, integrating AI in the design of educational games in playful learning
• Create educational chatbots that assist students in their learning and doubt resolution processes, including intelligent agents in educational platforms to enhance interaction and teaching 
• Perform continuous analysis of AI in Education projects to identify areas for improvement and optimization

Module 18. Teaching Practice with Generative Artificial Intelligence 

• Master generative AI technologies for their application and effective use in educational environments, planning effective educational activities
• Create didactic materials using generative AI to improve the quality and variety of learning resources, as well as to measure student progress in innovative ways 
• Use generative AI to correct activities and evaluative tests, streamlining and optimizing this process 
• Integrate generative AI tools in pedagogical strategies to improve the effectiveness of the educational process and design inclusive learning environments, under the universal design approach
• Evaluate the effectiveness of generative AI in education, analyzing its impact on teaching and learning processes

Module 19. Innovations and Emerging Trends in AI for Education 

• Master emerging AI tools and technologies applied to education for their effective use in learning environments
• Integrate Augmented and Virtual Reality in Education to enrich and enhance the learning experience
• Apply conversational AI to facilitate educational support and foster interactive learning among students 
• Implement facial and emotional recognition technologies to monitor student engagement and well-being in the classroom 
• Explore the integration of Blockchain and AI in Education to transform educational administration and validate certifications 

Module 20. Ethics and legislation of Artificial Intelligence in Education 

• Identify and apply ethical practices in the handling of sensitive data within the educational context, prioritizing responsibility and respect
• Analyze the social and cultural impact of AI in Education, assessing its influence on educational communities 
• Understand legislation and policies related to the use of data in educational settings involving AI 
• Define the intersection between AI, cultural diversity, and gender equity in the educational context 
• Evaluate the impact of AI on educational accessibility, ensuring equity in access to knowledge 

This professional master’s degree combines technical aspects of Artificial Intelligence with a practical approach in the development of educational projects"