Funding: Ministerio de Ciencia, Innovación y Universidades. Proyectos de Generación de Conocimiento 2025. Plan Estatal de Investigación Científica y Técnica y de Innovación 2024-2027
Project leader: Roberto Therón Sánchez - Alicia García Holgado
Coordinator: Universidad de Salamanca
Reference: PID2025-173630OB-I00
No more institutions involved
Date: 01/09/2026 - 31/08/2030
Budget: 78.500€
Abstract:
The AIDJUSTMENT (AI-agent ecosystem for Diverse foreign language Journeys Utilizing Strategic Teacher-driven Methodological Navigations and Transformations) project aims to advance the state-of-the-art in Artificial Intelligence in Education (AIED) through the rigorous design, implementation, and validation of a novel architecture: the Methodological Orchestration Motor (MOM). This interdisciplinary project is situated at the intersection of Computer Science, Human-Computer Interaction (HCI), and Educational Sciences.
Its General Objective is to develop the MOM as the core of an Explainable AI-Agent Ecosystem (E-AIA) that enables flexible and strategically guided personalization of Foreign Language Learning (FLL) and Content and Language Integrated Learning (CLIL) pathways.
The central innovation lies in the MOM, which operates as a centralized, declarative rule engine designed to manage high-level strategic reasoning. Unlike isolated generative AI tools, the MOM translates the teacher's pedagogical intent (e.g., applying complex methodologies like CLIL or TBLT) and student interaction data (diagnostic inferences based on Machine Learning - ML) into dynamic and adaptive methodological transformations. This defines a new architectural pattern for AIED systems focused on granting control to the educator, shifting the focus from technological application to strategic learning management.
AIDJUSTMENT addresses three critical problems: 1) The insufficient adaptability of platforms to methodological diversity; 2) The lack of diagnostic visibility for teachers; and 3) The "Black Box" problem in AIED, which limits teaching autonomy. To solve the third point, the project integrates Explainable Visual Analytics (XAI) techniques, using Process Mining to visualize and audit the precise sequence of the MOM's methodological decisions, thus bolstering the teacher's professional judgment.
The methodology is rigorously based on the Design Science Research (DSR) framework. The expected scientific impact is high, with a commitment to publishing at least 10 indexed articles, including 5 in high-impact WoS Q1 journals, to disseminate the MOM architecture. The social and economic impact focuses on fostering AI literacy among educators, promoting inclusion through personalized learning experiences, and generating the potential for technology transfer and commercialization of the E-AIA architecture in the EdTech sector.
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