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AI and Education

The research theme on AI and education aims at applying some of the IIIA AI techniques to the field of education. It does not aim at completely automating some processes and replacing the human component, but supporting the human through mechanisms like team formation that allow for building more efficient teams, or peer-based assessments that support assessments in massive online courses. 

Contact: Carles Sierra


It is always challenging to make predictions about the impact of technology on an economic or social sector. However, all recent analysis make it clear that repetitive tasks, or those with little added value by the humans who perform them, are going to be redesigned to facilitate their automation through the use of Artificial Intelligence (AI) techniques. Banking and commerce are examples of sectors that are undergoing a profound transformation, partly enabled by AI techniques such as chat-bots or personalization systems, leading to a notable reduction in employment. On the contrary, the education sector will continue to need the human component, and permanently, since the school fulfils an essential socializing function for the development of people. This need does not mean that AI is not going to impact educational processes; it has; it does and will continue to do so.    

Today we have numerous AI applications, not necessarily developed specifically for education, but which are very useful in the education world. For example, automatic subtitling of videos, tutoring systems that interact in natural language, or the realistic conversion of text to human speech. 

AI, in its origins, was already applied to education, in particular personalized education. Adapting the contents to each student is a pedagogical imperative that is difficult for teachers to achieve when the groups are large or the economic resources dedicated to education are limited. Several research groups developed simple systems of personalized education in the 1960s. Today, these systems have reached a remarkable level of sophistication. For example, over the past 15 years, the ALEKS system (aleks.com) developed in the United States has improved the performance of millions of students in mathematics. This system raises problems with an open response, analyses the answer and, thanks to a machine learning system, identifies errors and skills not acquired to explain the error to the student and recommend new problems that help to obtain the necessary skills. This type of system continues to be developed in different countries. The one created by Squirrel AI in Shanghai with more than three million students, and with a great improvement in individual performance is noteworthy (http://squirrelai.com/product/ials). We will no doubt see these systems more frequently over the next decade and covering areas increasingly distant from STEM, where they focus on today.

Collaborative Learning

Social phycology, AI and Ethics together can provide valuable models for peer feedback and teamworks

 
Team Formation

Global economy demands to restructure education to encourage entrepreneurship, creativity and risk-taking. Learning based on teamwork is the path to follow. Within collaborative and task-based education, one of the recurring problems is how to form teams of students. AI allows the analysis of a multitude of factors (sociological, competence, psychological, etc.) to explore the enormous space of possible combinations and find the optimal teams of students in different scenarios and contexts. 

Peer Evaluation

Progress will be made in automatic and peer evaluation processes, which will further democratize education online and throughout life. Advances in natural language processing and computer vision combined with explanation techniques will make the self-assessment that systems provide to students much more informative and useful. Likewise, peer assessment combined with AI techniques will allow the assessment of large groups of online education to be acceptable to teachers. 

Lesson Plans

There are a number of available tools that support teachers in the management of lesson plans on the web. However, none of them is task-centred and support any form of lesson plan's execution over the web. At IIIA, we are interested in the design and execution of these pedagogical workflows. Our Lesson Plans allows to coordinate interactions, ensuring the rules set by the lesson plan are followed, where lesson plans are designed with respect to a selected rubric. Once the lesson plan is defined, a specific graphical user interface (GUI) is automatically generated to allow students navigate through the lesson. Every time the tutor modifies a workflow, a new GUI is generated accordingly without any programming effort. 

Personalised Learning

Hybrid recommender systems and learning analytics allows creating custom-made contents and learning itineraries.

Based on data analytics, Artificial Intelligence algorithms can provide a learning context for the particular needs of students or group of students. We study and create models and algorithms that automatically recommend custom contents and create learning itineraries for the learning needs of students. 

Serious Games

Combine Virtual Reality, AI and gamification to promote learning by playing.

Artificial Intelligence and Virtual Reality provide a rich environment for game-based learning, also called serious games. We develop new personalisation techniques that can be integrated in virtual games to create learning environments where to study and practice several subjects in an inmersive and entretained way.

IIIA develops AI-based software components to offer schools and teachers tools to implement at classrooms. Our toolbox currently offers tools for peer assessments, team composition and lesson plans creation and execution. In what follows, you can play with and test the different demonstrators that shows some of the functionalities offered by our AI-based components.

Team Formation

Cultivation of teamwork, community building, and leadership skills are valuable classroom goals that are more and more introduced at schools. Our aim is to contribute with software technologies that provide teachers with tools to create teams that perform well at diferent levels. 

Synergetic Teams Tool

Partitioning groups of students into competence and cogenial teams for a problem-solving. Eduteams is a Webapp that support the composition of Synergetic teams of students at the classroom.

Congenial Teams Tool 

Partitioning groups of students into gender and psychologically balanced problem-solving teams. Eduteams is a Webapp that support the composition of congenial teams of students at the classroom.

Educational Teams to Companies

Desicion support component to help assign group of students to a Intership project or task. Edu2Com is an Artificial Intelligence component for allocating teams to tasks or projects based on competencies and preferences.

Peer Evaluation

Involving students into accessing others supports teachers but also increase students skills and knowledge. Our aim is to offers computational tools that support the peer assessment in and out of classrooms.

Collaborative Assessment [demo]

Combines teacher and peer assessments to reduce the number of evaluations to make.

Lesson Plans

Our aim is to allow teachers and students to participate into a more flexible, open and collaborative online learning environment. We build tools to support flexible ways to build, share and use collaborative Lesson Plans.

Lesson Plan Editor [demo]

Lesson plan editor to create peer to peer lessons.

Lesson Plan Online Execution [demo]

An online learning environment where executing peer to peer lesson plans.

Filippo Bistaffa
Tenured Scientist
Phone Ext. 431849

Christian Clemens Blum
Scientific Researcher
Phone Ext. 431840

Lissette Lemus del Cueto
Engineer
Phone Ext. 431823

Alejandra López de Aberasturi Gómez
PhD Student
Phone Ext. 431831

Nardine Osman Alameh
Tenured Scientist
Phone Ext. 431826

Juan A. Rodríguez-Aguilar
Research Professor
Phone Ext. 431861

Margarida Romero Velasco
Research Professor

Jordi Sabater-Mir
Tenured Scientist
Phone Ext. 431856

Carles Sierra García
Research Professor
Phone Ext. 431801

2025
Roger Xavier Lera Leri,  Filippo Bistaffa,  Tomas Trescak,  & Juan A. Rodríguez-Aguilar (2025). Computing Job-Tailored Degree Plans Towards the Acquisition of Professional Skills. Annals of Operations Research, 1--34. [BibTeX]  [PDF]
Sara Cooper,  Bartomeu Pou,  Arnau Mayoral-Macau,  Alberto Redondo,  David Rios,  & Raquel Ros (2025). EMOROBCARE: A Low-Cost Social Robot for Supporting Children with Autism in Therapeutic Settings. International Conference on Social Robotics (ICSR) . [BibTeX]  [PDF]
2024
Adrià Fenoy,  Filippo Bistaffa,  & Alessandro Farinelli (2024). An attention model for the formation of collectives in real-world domains. Artificial Intelligence, 328, 104064. https://doi.org/10.1016/j.artint.2023.104064. [BibTeX]  [PDF]
Núria Vallès-Peris (2024). Digitalització i sostenibilitat. L'escola com a esperança per cuidar del futur. Joan Amer Fernàndez (Eds.), Anuari de l'Educació de les Illes Balears 2024 (pp 13-24). Fundació Guillem Guifré de Colonya. [BibTeX]
2023
Dimitra Bourou,  Marco Schorlemmer,  & Enric Plaza (2023). An Image-Schematic Analysis of Hasse and Euler Diagrams. Maria M. Hedblom, & Oliver Kutz (Eds.), Proceedings of The Seventh Image Schema Day co-located with The 20th International Conference on Principles of Knowledge Representation and Reasoning (KR 2023), Rhodes, Greece, September 2nd, 2023 . CEUR-WS.org. https://ceur-ws.org/Vol-3511/paper\_05.pdf. [BibTeX]
Athina Georgara,  Raman Kazhamiakin,  Ornella Mich,  Alessio Palmero Approsio,  Jean-Christoph Pazzaglia,  Juan A. Rodríguez-Aguilar,  & Carles Sierra (2023). The AI4Citizen pilot: Pipelining AI-based technologies to support school-work alternation programmes. Applied Intelligence. https://doi.org/10.1007/s10489-023-04758-3. [BibTeX]  [PDF]
2022
Tomas Trescak,  Roger Xavier Lera Leri,  Filippo Bistaffa,  & Juan A. Rodríguez-Aguilar (2022). Agent-Assisted Life-Long Education and Learning. Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems . International Foundation for Autonomous Agents and Multiagent Systems. [BibTeX]  [PDF]
Athina Georgara,  Juan A. Rodríguez-Aguilar,  & Carles Sierra (2022). Allocating teams to tasks: an anytime heuristic competence-based approach. Dorothea Baumeister, & Jörg Rothe (Eds.), Multi-Agent Systems - 19th European Conference, {EUMAS}2022, Düsseldorf, Germany, September 14-16, 2022, Revised Selected Papers . Springer International Publishing. [BibTeX]  [PDF]
Dimitra Bourou,  Marco Schorlemmer,  & Enric Plaza (2022). Embodied Sense-Making of Diagrams as Conceptual Blending with Image Schemas. Maria M. Hedblom, & Oliver Kutz (Eds.), Proceedings of the Sixth Image Schema Day, Jönköping, Sweden, March 24-25th, 2022 . CEUR-WS.org. [BibTeX]  [PDF]
Dimitra Bourou,  Marco Schorlemmer,  & Enric Plaza (2022). Euler vs Hasse Diagrams for Reasoning About Sets: A Cognitive Approach. Valeria Giardino, Sven Linker, Richard Burns, Francesco Bellucci, Jean-Michel Boucheix, & Petrucio Viana (Eds.), Diagrammatic Representation and Inference - 13th International Conference, Diagrams 2022, Rome, Italy, September 14-16, 2022, Proceedings (pp. 151--167). Springer. https://doi.org/10.1007/978-3-031-15146-0_13. [BibTeX]
Athina Georgara,  Juan A. Rodríguez-Aguilar,  & Carles Sierra (2022). Privacy-Aware Explanations for Team Formation. Proceedings of the 24th International Conference on Principles and Practice of Multi-Agent Systems . [BibTeX]  [PDF]
2021
Nieves Montes,  Nardine Osman,  & Carles Sierra (2021). Enabling Game-Theoretical Analysis of Social Rules. IOS Press. https://doi.org/10.3233/FAIA210120. [BibTeX]  [PDF]
Pablo Noriega,  & Txetxu Ausìn (2021). Ethical, Legal, Economic and Social Implications. Sara Degli Esposti, & Carles Sierra (Eds.), White Paper on Artificial Intelligence, Robotics and Data Science (pp 120-141). Consejo Superior de Investigaciones Científicas (España). [BibTeX]  [PDF]
Athina Georgara,  Juan A. Rodríguez-Aguilar,  & Carles Sierra (2021). Towards a Competence-Based Approach to Allocate Teams to Tasks. Proceedings of the 20th International Conference on Autonomous Agents and MultiAgent Systems (pp. 1504–1506). International Foundation for Autonomous Agents and Multiagent Systems. [BibTeX]  [PDF]
2020
Athina Georgara,  Carles Sierra,  & Juan A. Rodríguez-Aguilar (2020). TAIP: an anytime algorithm for allocating student teams to internship programs. arXiv preprint arXiv:2005.09331. [BibTeX]  [PDF]
  • UNESCO Declaration. In May 2019, around 100 UNESCO member states made a series of recommendations that mark the way forward in the coming years. The first and most relevant is that AI has to be integrated into the education system. AI must be taught and at the same time used to strengthen student learning. This integration and use must be based on scrupulous respect for human rights. It must serve to train students with a critical spirit regarding the use of this technology that allows them to understand the risks and take advantage of the opportunities it offers us. The future of AI in the educational world is fascinating.  
  • SQUIRREL AI. An online education company specialising in intelligent adaptive education.