EstudIA

CLIENT

Educational centers, universities, academies, teachers, tutors, and companies with training programs seeking a digital solution to create content, evaluate student progress, and personalize the learning experience through artificial intelligence.

SERVICES

Design and development of an AI-powered educational platform featuring quiz creation, student progress tracking, metrics visualization, personalized learning, and accessibility features.

TECHNOLOGIES

The platform combines generative AI and natural language processing to interpret documents, generate educational content, and adapt learning to each user. Additionally, it incorporates data analytics, document connectors, SaaS architecture, and security systems to protect the information of both teachers and students.

Estud-IA is an AI-driven educational platform that transforms how didactic content is created, managed, and personalized. The solution enables teachers and training centers to generate study materials, quizzes, and tracking metrics from educational documents without requiring technical expertise. Thanks to its AI capabilities, the platform adapts learning to the pace, progress, and needs of each student. Its intuitive and accessible approach streamlines teaching work, enhances the student experience, and fosters a more inclusive education. Furthermore, it includes features such as text-to-speech conversion and data visualization to support diverse learning styles. With Estud-IA, technology becomes a key tool for driving more efficient, flexible, and personalized teaching.

1. Project Description, Objectives, and Priorities

The ESTUD-IA project was created to transform the EdTech sector by integrating disruptive technologies into learning processes. In a context where educational personalization is key, many current platforms lack the intelligent tools necessary to adapt to each student’s individual pace. The general objective is to develop an advanced educational platform that utilizes Generative and Predictive Artificial Intelligence to optimize academic performance and teaching management.

The final result is conceived as an intelligent digital environment capable of generating dynamic content, predicting learning difficulties, and offering a hyper-personalized and efficient educational experience.

Main Objective: To develop an innovative educational platform that integrates Generative AI models for content creation and Predictive models for student progress analysis. The solution aims to reduce school dropout rates and improve educational quality through an accessible interface and advanced analytics tools for both institutions and students.

Specific Objectives:

Generative AI Implementation: Develop LLM-based engines for the automatic generation of exercises, summaries, and personalized virtual tutoring.
Predictive Analytics Models: Create algorithms capable of identifying behavioral patterns and predicting the risk of academic failure before it occurs.
Educational Data Architecture: Build a robust and secure infrastructure for storing and processing large volumes of academic data, ensuring the privacy of minors.
Interoperability with Educational Systems: Develop connectors and APIs that allow seamless integration with existing LMS (Learning Management Systems) on the market.
UX Optimization for Learning: Design an intuitive interface that fosters student engagement using gamification principles and AI.
Safety and Ethical Compliance: Ensure that the use of AI is ethical, transparent, and strictly complies with the GDPR in sensitive environments such as education.

2. Geographical or Thematic Scope of Execution

This project is at the forefront of Educational Digitalization (EdTech). It represents a decisive step toward a more inclusive, intelligent, and sustainable education system, aligned with European strategies for digital training and technological capacity building.

3. Total EU Aid Assigned, Budget, or Total Investment

The total approved budget for the ESTUD-IA project is €203,006.00. CDTI has granted aid in the form of a loan of €166,464.00 to the company DIMENSIONA CONSULTORIA TECNOLOXICA SL. This aid is co-funded by the European Regional Development Fund (ERDF/FEDER), under the slogan “A way to make Europe.”

4. Duration or Execution Period

The project formally began on 10/24/2024, and its technical execution period extends until 04/30/2026, meeting the innovation milestones established by CDTI.

5. European Union Brand Graphic Elements

Project Results

Implementation of Project Management Methodology

Implementation of an agile methodology (Scrum/Kanban) adapted to the development of AI models. Version control protocols and technological risk management have been established, ensuring fluid communication between the data science team and software developers.

Deliverables:

Technical-economic progress reports.
Risk management and quality plan.

Requirements Definition

Identification of pedagogical and technical needs for the platform’s design. Functional requirements for the text generation engines and key parameters for training predictive learning models have been defined.

Deliverables:

Functional and technical requirements report.
User Experience (UX/UI) design document.
Data Protection Impact Assessment (DPIA) specific to educational environments.

Development of the Educational Platform

Construction of the base infrastructure and the platform’s user interface. Implementation of user management systems and secure learning environments, with a scalable architecture capable of supporting multiple training centers simultaneously.

Deliverables:

Developed base platform.
Code repositories for the application and backend.
Server infrastructure and connection APIs.

Artificial Intelligence Integration

Development and integration of Generative AI engines for content creation and the Predictive engine for student analysis. This includes training models with specific educational datasets to ensure accuracy and avoid AI biases.

Deliverables:

Technical documentation of the Generative AI model.
Predictive algorithm for academic performance.
Validation report of the trained models.

Platform Testing

Execution of stress tests and pedagogical validation of the system. Unit and integration tests are carried out, as well as controlled pilots to verify that AI suggestions are coherent and pedagogically useful for teachers and students.

Deliverables:

Technical test case report.
Analysis of educational pilot results.
User manual and guide for best practices in educational AI.

EstudIA

Project Results

Implementation of Project Management Methodology

Implementation of an agile methodology (Scrum/Kanban) adapted to the development of AI models. Version control protocols and technological risk management have been established, ensuring fluid communication between the data science team and software developers.

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