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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 ( 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 ( 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 Blum
Scientific Researcher
Phone Ext. 431840

Lissette Lemus del Cueto
Contract Engineer
Phone Ext. 431823

Alejandra López de Aberasturi Gómez
PhD Student
Phone Ext. 431831

Nardine Osman
Tenured Scientist
Phone Ext. 431826

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

Jordi Sabater-Mir
Tenured Scientist
Phone Ext. 431856

Carles Sierra
Research Professor
Phone Ext. 431801

In Press
Nardine Osman,  Ronald Chenu-Abente,  Qiang Shen,  Carles Sierra,  & Fausto Giunchiglia (In Press). Empowering Users in Online Open Communities. SN Computer Science. [BibTeX]  [PDF]
Manel Rodríguez Soto,  Nardine Osman,  Carles Sierra,  Paula Sánchez Veja,  Rocío Cintas García,  Cristina Farriols Danes,  Montserrat García Retortillo,  & Sílvia Mínguez Maso (2024). (pp. 8). to appear at the Special Session on AI with Awareness Inside of the 16th International Conference on Agents and Artificial Intelligence (ICAART 2024). [BibTeX]  [PDF]
Roger Xavier Lera Leri,  Enrico Liscio,  Filippo Bistaffa,  Catholijn M. Jonker,  Maite Lopez-Sanchez,  Pradeep K. Murukannaiah,  Juan A. Rodríguez-Aguilar,  & Francisco Salas-Molina (2024). Aggregating value systems for decision support. Knowledge-Based Systems, 287, 111453. [BibTeX]  [PDF]
Adrià Fenoy,  Filippo Bistaffa,  & Alessandro Farinelli (2024). An attention model for the formation of collectives in real-world domains. Artificial Intelligence, 328, 104064. [BibTeX]  [PDF]
Núria Vallès-Peris,  & Miquel Domènech (2024). Digital Citizenship at School: Democracy, Pragmatism and Rri. Technology in Society, 76. [BibTeX]
Errikos Streviniotis,  Athina Georgara,  Filippo Bistaffa,  & Georgios Chalkiadakis (2024). FairPlay: A Multi-Sided Fair Dynamic Pricing Policy for Hotels. Proceedings of the AAAI Conference on Artificial Intelligence, 38, 22368-22376. [BibTeX]  [PDF]
Thiago Nardine Osman,  & Marco Schorlemmer (2024). Is This a Violation? Learning and Understanding Norm Violations in Online Communities. Artificial Intelligence, 327. [BibTeX]
M Serramia Amoros,  M Lopez-Sanchez,  Juan A. Rodríguez-Aguilar,  & S Moretti (2024). Value alignment in participatory budgeting. Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems . [BibTeX]  [PDF]
Francisco Salas-Molina,  Filippo Bistaffa,  & Juan A. Rodríguez-Aguilar (2023). A general approach for computing a consensus in group decision making that integrates multiple ethical principles. Socio-Economic Planning Sciences, 89, 101694. [BibTeX]  [PDF]
Jordi Ganzer-Ripoll,  Natalia Criado,  Maite Lopez-Sanchez,  Simon Parsons,  & Juan A. Rodríguez-Aguilar (2023). A model to support collective reasoning: Formalization, analysis and computational assessment. Journal of Artificial Intelligence Research. [BibTeX]  [PDF]
Francisco Salas-Molina,  Juan A. Rodríguez-Aguilar,  & Montserrat Guillén (2023). A multidimensional review of the cash management problem. Financial Innovation, 9. [BibTeX]  [PDF]
Thiago Freitas Santos,  Nardine Osman,  & Marco Schorlemmer (2023). A multi-scenario approach to continuously learn and understand norm violations. Autonomous Agents and Multi-Agent Systems, 37, 38. [BibTeX]
Francisco Salas-Molina,  David Pla-Santamaria,  & Juan A. Rodríguez-Aguilar (2023). An analytic derivation of the efficient frontier in biobjective cash management and its implications for policies. Annals of Operations Research. [BibTeX]  [PDF]
Dave de Jonge (2023). A New Bargaining Solution for Finite Offer Spaces. Applied Intelligence, 53, 28310--28332. [BibTeX]  [PDF]
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 .\_05.pdf. [BibTeX]
Marc Serramia,  Maite López-Sánchez,  Stefano Moretti,  & Juan A. Rodríguez-Aguilar (2023). Building rankings encompassing multiple criteria to support qualitative decision-making. Information Sciences, 631, 288-304. [BibTeX]  [PDF]
Núria Vallès-Peris,  & Miquel Domènech (2023). Care robots for the common good: ethics as politics. Humanities & Social Sciences Communications, 10, 345. [BibTeX]
Núria Vallès-Peris,  & Miquel Domènech (2023). Caring in the in-between: a proposal to introduce responsible AI and robotics to healthcare. AI and Society, 38, 1685--1695. [BibTeX]
Pompeu; Casanovas,  & Pablo Noriega (2023). Cómo regular lo altamente complejo. Nuevos Diálogos, 2, 25–31. [BibTeX]  [PDF]
Thiago Freitas Santos,  Stephen Cranefield,  Bastin Tony Roy Savarimuthu,  Nardine Osman,  & Marco Schorlemmer (2023). Cross-community Adapter Learning {(CAL)}to Understand the Evolving Meanings of Norm Violation. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, {IJCAI}2023, 19th-25th August 2023, Macao, SAR, China (pp. 109--117). [BibTeX]
Marc Serramia,  Manel Rodriguez-Soto,  Maite Lopez-Sanchez,  Juan A. Rodríguez-Aguilar,  Filippo Bistaffa,  Paula Boddington,  Michael Wooldridge,  & Carlos Ansotegui (2023). Encoding Ethics to Compute Value-Aligned Norms. Minds and Machines, 1--30. [BibTeX]  [PDF]
Filippo Bistaffa (2023). Faster Exact MPE and Constrained Optimization with Deterministic Finite State Automata. Proceedings of the Thirty-Second International Joint Conference on Artificial Intelligence, {IJCAI}2023, 19th-25th August 2023, Macao, SAR, China (pp. 1884--1892). [BibTeX]
Maria Verdaguer,  Núria Vallès-Peris,  Xavier Busquet-Duran,  Eduard Moreno-Gabriel,  Patricia Beroiz,  Antonia {Arreciado Marañón},  Maria Feijoo-Cid,  Miquel Domènech,  Lupicinio Iñiguez-Rueda,  Glòria Cantarell,  & Pere Torán-Monserrat (2023). Implementation of Assisted Dying in Catalonia: Impact on Professionals and Development of Good Practices. Protocol for a Qualitative Study. International Journal of Qualitative Methods, 22, 1--11. [BibTeX]
Celeste Veronese,  Daniele Meli,  Filippo Bistaffa,  Manel Rodríguez Soto,  Alessandro Farinelli,  & Juan A. Rodríguez-Aguilar (2023). Inductive Logic Programming For Transparent Alignment With Multiple Moral Values. . 2nd International Workshop on Emerging Ethical Aspects of AI (BEWARE-23). [BibTeX]  [PDF]
Enrico Liscio,  Roger Lera-Leri,  Filippo Bistaffa,  Roel I. J. Dobbe,  Catholijn M. Jonker,  Maite López-Sánchez,  Juan A. Rodríguez-Aguilar,  & Pradeep K. Murukannaiah (2023). Inferring Values via Hybrid Intelligence. Proceedings of the 2nd International Conference on Hybrid Human Artificial Intelligence (HHAI) (pp. In press). [BibTeX]  [PDF]
Núria Vallès-Peris (2023). La naturalesa sociotècnica de la IA: ètica, política i tecnologia. Nodes. El butlletí de l'ACIA, 59, 15--25. [BibTeX]
Manel Rodríguez Soto,  Maite López-Sánchez,  & Juan A. Rodríguez-Aguilar (2023). Multi-objective reinforcement learning for designing ethical multi-agent environments. Neural Computing and Applications. [BibTeX]  [PDF]
Manel Rodríguez Soto,  Roxana Radulescu,  Juan A. Rodríguez-Aguilar,  Maite López-Sánchez,  & Ann Nowé (2023). Multi-objective reinforcement learning for guaranteeing alignment with multiple values. Adaptive and Learning Agents Workshop (AAMAS 2023) . [BibTeX]  [PDF]
Jianglin Qiao,  Dave de Jonge,  Dongmo Zhang,  Simeon Simoff,  Carles Sierra,  & Bo Du (2023). Price of anarchy of traffic assignment with exponential cost functions. Auton. Agents Multi Agent Syst., 37. [BibTeX]
Miquel Domènech,  & Núria Vallès-Peris (2023). Robots for Care: A Few Considerations from the Social Sciences. Tina Sikka (Eds.), Genetic Science and New Digital Technologies (pp 180--203). Bristol University Press. [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. [BibTeX]  [PDF]
Pablo Noriega,  & Enric Plaza (2023). The Use of Agent-based Simulation of Public Policy Design to Study the Value Alignment Problem. Proc. AIGEL 2022 Artificial Intelligence Governance Ethics and Law Workshop 2022 . CEUR-WS. [BibTeX]  [PDF]
Enrico Liscio,  Roger Xavier Lera Leri,  Filippo Bistaffa,  Roel I. J. Dobbe,  Catholijn M. Jonker,  Maite López-Sánchez,  Juan A. Rodríguez-Aguilar,  & Pradeep K. Murukannaiah (2023). Value Inference in Sociotechnical Systems. Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS) (pp. 1774-1780). [BibTeX]  [PDF]
Nieves Montes,  Nardine Osman,  & Carles Sierra (2022). A computational model of Ostrom's Institutional Analysis and Development framework. Artificial Intelligence, 311, 103756. [BibTeX]  [PDF]
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]
Pol Vidal Lamolla,  Alexandra Popartan,  Toni Perello-Moragues,  Pablo Noriega,  David Sauri,  Manel Poch,  & Maria Molinos-Senante (2022). Agent-based modelling to simulate the socio-economic effects of implementing time-of-use tariffs for domestic water. Sustainable Cities and Society, 86, 104118. [BibTeX]  [PDF]
Jianglin Qiao,  Dave de Jonge,  Dongmo Zhang,  Carles Sierra,  & Simeon Simoff (2022). A Hybrid Model of Traffic Assignment and Control for Autonomous Vehicles. Reyhan Aydogan, Natalia Criado, Jerôme Lang, Victor Sanchez-Anguix, & Marc Serramia (Eds.), {PRIMA}2022: Principles and Practice of Multi-Agent Systems - 24th International Conference, Valencia, Spain, November 16-18, 2022, Proceedings (pp. 208--226). Springer.\_13. [BibTeX]
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]
Dave de Jonge (2022). An Analysis of the Linear Bilateral {ANAC}Domains Using the {M}i{CRO}Benchmark Strategy. Luc Raedt (Eds.), Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence, {IJCAI}2022, Vienna, Austria, 23-29 July 2022 (pp. 223--229). [BibTeX]  [PDF]
Athina Georgara,  Juan A. Rodríguez-Aguilar,  Carles Sierra,  Ornella Mich,  Raman Kazhamiakin,  Alessio P. Approsio,  & Jean-Christophe Pazzaglia (2022). An Anytime Heuristic Algorithm for Allocating Many Teams to Many Tasks. Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems . International Foundation for Autonomous Agents and Multiagent Systems. [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.