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

Each student may have different learning difficulties, and educators are responsible for detecting signs of these difficulties. In this context, Learning Automation makes it easier for the teaching team to create personalized teaching plans that adapt to both the strengths and weaknesses of each student.

In turn, Artificial Intelligence helps users to significantly improve their academic results and retain knowledge over a long period of time. One example of this is the integration of intelligent agents in educational platforms. By means of materials such as chatbots, students can ask questions about educational content and obtain effective and immediate answers. This also helps teachers to free themselves from certain tasks, focusing on other more important ones.

Faced with this reality, TECH has launched a pioneering program that will delve into the optimization of teaching practice through Artificial Intelligence. Designed by specialists in this area, the syllabus will promote the personalization of learning based on academic performance data, supported by algorithms. In line with this, the syllabus will offer experts innovative strategies to develop various educational projects, for example, games for learning.

In turn, the didactic materials will analyze the application of Machine Learning tools for educational planning. Graduates will use them to create didactic materials, correct exams and generate surveys to improve their academic proposals.

On the other hand, the methodology of this program reinforces its innovative character. TECH offers a 100% online educational environment, tailored to the needs of busy professionals seeking to advance their careers. Relearning methodology is also employed, based on the repetition of key concepts to fix knowledge and facilitate learning. In this way, the combination of flexibility and a robust pedagogical approach makes it highly accessible.

You will develop teacher quality assessment surveys to take advantage of your students' feedback and optimize your educational plans’’

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. Its 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

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 sector who contribute their work experience to this specializing 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 course. For this purpose, students will be assisted by an innovative interactive video system created by renowned and experienced experts.

Do you want to enrich your educational decision-making? Achieve it with the Intelligent Automation tools that this program will provide you"

You will use Data Analysis to effectively prevent and solve educational problems. Enroll now!"

This university program will focus on the development of Artificial Intelligence projects in the educational field. For this purpose, the syllabus will provide teachers with the most advanced Machine Learning tools, aimed at making educational decisions. The syllabus will delve into a variety of algorithms for predictive data analysis, corresponding to academic performance. It will also address in detail how Artificial Intelligence contributes to aspects such as evaluation or personalization of learning. It will also provide the keys for the application of useful pedagogical strategies for the correction of activities and the development of teaching materials.

This Postgraduate diploma merges teaching excellence with the technological revolution of Artificial Intelligence, so that you can stay 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. Applying H2O.ai in the 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. Implementation of TensorFlow in the Interpretation of Educational Trends and Patterns using Machine Learning Techniques
1.2.2. Evaluating the Impact of Pedagogical Strategies using Data Analytics
1.2.3. Application of Trinka in the 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. Decision Support Systems Based on tome.ai for Educational Administrators
1.4.2. Use of Trello for Educational Resource Planning and Allocation
1.4.3. Optimization of Educational Processes through Predictive Analytics with Orange Data Mining

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. Evaluating the Impact of DataRobot 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 with IBM Watson Education
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 Special Educational Needs Screening with Gooroo
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 Smart Sparrow
1.9.2. Real-Time Individual Progress Measurement and Adjustment via Squirrel AI Learning
1.9.3. Data Protection and Privacy Techniques for Educational Systems with Google Cloud Security

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 with Algor 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 Developing Educational Projects: TensorFlow Playground
2.2.2. Tools for Educational Projects in History
2.2.3. Tools for Educational Projects in Mathematics; Wolfram Alpha
2.2.4. Tools for Educational Projects in English: Grammarly

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 AI Projects in Specific Subjects

2.4.1. Mathematics and AI: Thinkster Math
2.4.2. History and AI
2.4.3. Languages and AI: Deep L
2.4.4. Other Subjects: Watson Studio

2.5. Project 1: Developing Educational Projects Using Machine Learning with Khan Academy

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: Integrating Intelligent Agents into Educational Platforms with Knewton

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 with Qualtrics

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

2.10. Analysis and Continuous Improvement of AI in Education with Edmodo Insights

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

Module 3. Teaching Practice with Generative Artificial Intelligence

3.1. Generative AI Technologies for Use in Education

3.1.1. Current Market: Artbreeder, Runway ML and DeepDream Generator
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: Altitude Learning
3.2.2. Tools and Their Application
3.2.3. Education and AI
3.2.4. Evolution

3.3. Creating Teaching Materials with Generative AI Using Story Ai, Pix2PIx and NeouralTalk2

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

3.4. Development of Evaluation Tests using Generative AI with Quizgecko

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 Activities and Evaluative Tests using Generative AI with Grandscope 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: Gans

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 Education

Make the most of this opportunity to surround yourself with expert professionals and learn from their work methodology"