You will delve into the personalization of learning with Artificial Intelligence at the best digital university in the world, according to Forbes"

The development of learning materials with generative AI has the potential to revolutionize education by enabling the automatic generation of high-quality, personalized educational content. For example, algorithms such as GPT-3 can generate explanations, exercises and examples in an automated way. This is useful for creating specific reading materials, study guides and exercises for each student. AI also produces multimedia materials such as graphics, animations and videos to improve knowledge retention. 

For this reason, TECH is launching a Postgraduate diploma that will focus on teaching practice with generative AI. The syllabus will analyze in detail strategies for implementing projects in the classroom, using the most sophisticated technological tools. The syllabus will also delve into the identification, extraction and preparation of educational data. In this line, the program will use Machine Learning techniques to interpret trends and patterns. Likewise, the university program will provide multiple case studies of successful predictions in educational environments. In this way, teaching professionals will be qualified to successfully address challenges in the classroom.

As for the methodology of this program, it should be noted that it reinforces its innovative character. TECH provides students with a 100% online educational environment, adapting to the needs of busy professionals who want to advance their careers. It also uses the Relearningteaching system, based on the repetition of key concepts to fix knowledge and facilitate learning.  In this way, the combination of  requirement for students is that they have an electronic device with Internet access (such as a cell phone, computer or tablet) to access the Virtual Campus and access the most innovative didactic material.

You will design teacher quality evaluation surveys and take advantage of student feedback to optimize your educational proposals"

This Postgraduate diploma in Application of Artificial Intelligence Techniques for the Teaching Profession 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 Application of Artificial Intelligence Techniques for the Teaching Profession
• 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

Looking to enrich your educational decision-making? Get it thanks to the Intelligent Automation tools that this program will provide you with"

The program’s teaching staff includes professionals from the sector who contribute their work experience to this 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 effectively manage data analysis to prevent and solve educational problems"

Forget about memorizing! With the Relearning system you will integrate the concepts in a natural and progressive way"

This Postgraduate diploma will provide students with a comprehensive approach to the development of AI projects in the educational context. To achieve this, the syllabus will provide students with the most innovative tools to make informed decisions. In addition, the syllabus will delve into a variety of algorithms for predictive data analysis, corresponding to academic performance. In this sense, the program will focus on how AI contributes to the evaluation and personalization of teaching. It will also provide keys for the application of pedagogical strategies, aimed at correcting activities and developing teaching materials.

This Postgraduate diploma combines teaching excellence with the technological revolution of Machine Learning, to keep you at the forefront of education"

Module 1. Data analysis and application of AI techniques for educational personalization

1.1. Identification, Extraction and Preparation of Educational Data

1.1.1. Methods of Collection and Selection of Relevant Data in Educational Settings
1.1.2. Data Cleaning and Normalization Techniques for Educational Analyses
1.1.3. Importance of Data Integrity and Quality in Educational Research

1.2. Analysis and Evaluation of Educational Data with AI for Continuous Improvement in the Classroom

1.2.1. Use of Machine Learning Techniques to Interpret Educational Trends and Patterns
1.2.2. Evaluating the Impact of Pedagogical Strategies using Data Analytics
1.2.3. Integration of AI-based Feedback for the Optimization of the Teaching Process

1.3. Definition of Academic Performance Indicators from Educational Data

1.3.1. Establishment of Key Metrics for Evaluating Student Achievement
1.3.2. Comparative Analysis of Indicators to Identify Areas for Improvement
1.3.3. Correlation Between Academic Indicators and External Factors Using AI

1.4. AI Tools for Educational Decision Making and Monitoring

1.4.1. AI-based Decision Support Systems for Educational Administrators
1.4.2. Role of AI in Educational Resource Planning and Allocation
1.4.3. Optimization of Educational Processes through Predictive Analytics

1.5. AI Technologies and Algorithms for Predictive Analysis of Academic Achievement Data

1.5.1. Fundamentals of Predictive Modeling in Education
1.5.2. Use of Classification and Regression Algorithms to Predict Trends in Education
1.5.3. Case Studies of Successful Predictions in Educational Environments

1.6. Application of Data Analytics with AI for the Prevention and Solution of Educational Problems

1.6.1. Early Identification of Academic Risks through Predictive Analytics
1.6.2. Data-driven Intervention Strategies to Address Educational Challenges
1.6.3. Assessing the Impact of AI-based Solutions in Education

1.7. Personalized Diagnosis of Learning Difficulties from Data Analytics with AI

1.7.1. AI Techniques for the Identification of Learning Styles and Learning Difficulties
1.7.2. Integration of Data Analysis into Individualized Educational Support Plans
1.7.3. Case Studies of Diagnoses Improved by the Use of AI

1.8. Data Analysis and Application of AI for Identification of Special Educational Needs

1.8.1. AI Approaches to the Detection of Special Educational Needs
1.8.2. Personalization of Teaching Strategies Based on Data Analysis
1.8.3. Evaluation of the Impact of AI on Educational Inclusion

1.9. Personalization of Learning with AI from Academic Performance Data Analytics

1.9.1. Creating Adaptive Learning Pathways using AI
1.9.2. Implementation of Recommender Systems for Educational Resources
1.9.3. Individual Progress Measurement and Real-Time Adjustments via AI

1.10. Security and Privacy in the Processing of Educational Data

1.10.1. Ethical and Legal Principles in the Management of Educational Data
1.10.2. Data Protection and Privacy Techniques in AI-based Educational Systems
1.10.3. Case Studies on Security Breaches and their Impact on Education

Module 2. Development of Artificial Intelligence Projects in the Classroom   

2.1. Planning and Design of AI Projects in Education 

2.1.1. First Steps to Plan the Project 
2.1.2. Knowledge Bases 
2.1.3. Design of AI Projects in Education  

2.2. Tools for the Development of Educational Projects with AI 

2.2.1. Tools for the Development of Educational Projects  
2.2.2. Tools for Educational Projects in History 
2.2.3. Tools for Educational Projects in Mathematics 
2.2.4. Tools for Educational Projects in English 

2.3. Strategies for Implementing AI Projects in the Classroom 

2.3.1. When to Implement an AI Project 
2.3.2. Why Implement an AI Project 
2.3.3. Strategies to be Implemented  

2.4. Integration of IA Projects in Specific Subjects 

2.4.1. Mathematics and AI 
2.4.2. History and IA 
2.4.3. Languages and IA 
2.4.4. Other Subjects  

2.5. Project 1: Developing educational projects using machine learning  

2.5.1. First Steps 
2.5.2. Requirements 
2.5.3. Tools to be Used 
2.5.4. Project definition   

2.6. Project 2: Integration of AI in the Development of Educational Games  

2.6.1. First Steps 
2.6.2. Requirements 
2.6.3. Tools to be Used 
2.6.4. Project definition   

2.7. Project 3: Development of Educational Chatbots for Student Assistance

2.7.1. First Steps 
2.7.2. Requirements 
2.7.3. Tools to be Used 
2.7.4. Project definition  

2.8. Project 4: Integration of Intelligent Agents in Educational Platforms 

2.8.1. First Steps 
2.8.2. Requirements 
2.8.3. Tools to be Used 
2.8.4. Project definition  

2.9. Evaluating and Measuring the Impact of AI Projects in Education 

2.9.1. Benefits of Working with AI in the Classroom 
2.9.2. Actual Data 
2.9.3. IA in Classroom  
2.9.4. AI Statistics in Education  

2.10. Analysis and Continuous Improvement of AI in Education Projects 

2.10.1. Current Projects  
2.10.2. Commissioning 
2.10.3. What the Future Holds 
2.10.4. Transforming the Aulas 360 

Module 3. Teaching Practice with Generative Artificial Intelligence   

3.1. Generative AI Technologies for Use in Education 

3.1.1. Current Market 
3.1.2. Technologies in Use 
3.1.3. What is to Come 
3.1.4. The Future of the Classroom 

3.2. Application of Generative AI Tools in Educational Planning 

3.2.1. Planning Tools 
3.2.2. Tools and their Application 
3.2.3. Education and AI 
3.2.4. Evolution  

3.3. Creation of Didactic Materials with Generative AI 

3.3.1. AI and its Uses in the Classroom 
3.3.2. Tools to Create Didactic Material 
3.3.3. How to Work with the Tools 
3.3.4. Commands 

3.4. Development of Evaluation Tests using Generative AI 

3.4.1. AI and its Uses in the Development of Evaluation Tests  
3.4.2. Tools for the Development of Evaluation Tests  
3.4.3. How to Work with the Tools 
3.4.4. Commands  

3.5. Enhanced Feedback and Communication with Generative AI 

3.5.1. AI in Communication 
3.5.2. Application of Tools in the Development of Communication in the Classroom 
3.5.3. Advantages and Disadvantages  

3.6. Correction of Evaluative Activities and Tests using Generative AI 

3.6.1. AI and its Uses in the Correction of Evaluative Activities and Tests 
3.6.2. Tools for the Correction of Evaluative Activities and Tests  
3.6.3. How to Work with the Tools 
3.6.4. Commands 

3.7. Generation of Teacher Quality Assessment Surveys through Generative AI 

3.7.1. AI and its Uses in the Generation of Teaching Quality Assessment Surveys using AI  
3.7.2. Tools for the Generation of AI-based Teacher Quality Surveys 
3.7.3. How to Work with the Tools 
3.7.4. Commands 

3.8. Integration of Generative AI Tools in Pedagogical Strategies 

3.8.1. Applications of AI in Pedagogical Strategies 
3.8.2. Correct Uses  
3.8.3. Advantages and Disadvantages 
3.8.4. Generative AI Tools in Pedagogical Strategies 

3.9. Use of Generative AI for Universal Design for Learning 

3.9.1. Generative AI, Why Now? 
3.9.2. AI in Learning 
3.9.3. Advantages and Disadvantages 
3.9.4. Applications of AI in Learning 

3.10. Evaluating the Effectiveness of Generative AI in Education 

3.10.1. Effectiveness Data 
3.10.2. Projects 
3.10.3. Design Purposes 
3.10.4. Evaluating the Effectiveness of AI in Educati

This academic itinerary is exclusive to TECH and you will be able to develop it at your own pace thanks to its 100% online Relearning methodology"