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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
Contract Researcher
Phone Ext. 431849

Christian Blum
Scientific Researcher
Phone Ext. 431840

Lissette Lemus del Cueto
Contract Engineer
Phone Ext. 431823

Alejandra Lopez de Aberasturi Gómez
Industrial 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
Filippo Bistaffa,  Georgios Chalkiadakis,  & Alessandro Farinelli (In Press). Efficient Coalition Structure Generation via Approximately Equivalent Induced Subgraph Games. IEEE Transactions on Cybernetics. https://doi.org/10.1109/TCYB.2020.3040622. [BibTeX]  [PDF]
Nardine Osman,  Ronald Chenu-Abente,  Qiang Shen,  Carles Sierra,  & Fausto Giunchiglia (In Press). Empowering Users in Online Open Communities. SN Computer Science. [BibTeX]  [PDF]
Marc Serramia,  Maite López-Sánchez,  & Juan A. Rodríguez-Aguilar (In Press). Value-aligned AI: Lessons learnt from value-aligned norm selection. Digital Society. [BibTeX]  [PDF]
2022
Nieves Montes,  Nardine Osman,  & Carles Sierra (2022). A computational model of Ostrom's Institutional Analysis and Development framework. Artificial Intelligence, 311, 103756. https://doi.org/10.1016/j.artint.2022.103756. [BibTeX]  [PDF]
Tomas Trescak,  Roger 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]
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). ijcai.org. https://doi.org/10.24963/ijcai.2022/32. [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]
Georgina Curto,  Nieves Montes,  Carles Sierra,  Nardine Osman,  & Flavio Comim (2022). A norm optimisation approach to {SDGs}: tackling poverty by acting on discrimination. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence . International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2022/726. [BibTeX]
Athina Georgara,  Juan A. Rodríguez-Aguilar,  & Carles Sierra (2022). Building Contrastive Explanations for Multi-Agent Team Formation. Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems . International Foundation for Autonomous Agents and Multiagent Systems. [BibTeX]  [PDF]
Manel Rodríguez Soto,  Juan A. Rodríguez-Aguilar,  & Maite López-Sánchez (2022). Building Multi-Agent Environments with Theoretical Guarantees on the Learning of Ethical Policies. . Adaptive and Learning Agents Workshop at AAMAS 2022 (ALA 2022). [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]
Nieves Montes (2022). Engineering Socially-Oriented Autonomous Agents and Multiagent Systems. Proceedings of the Thirty-First International Joint Conference on Artificial Intelligence . International Joint Conferences on Artificial Intelligence Organization. https://doi.org/10.24963/ijcai.2022/833. [BibTeX]
Thiago Freitas Dos Santos,  Nardine Osman,  & Marco Schorlemmer (2022). Ensemble and Incremental Learning for Norm Violation Detection. Piotr Faliszewski, Viviana Mascardi, Catherine Pelachaud, & Matthew E. Taylor (Eds.), 21st International Conference on Autonomous Agents and Multiagent Systems, {AAMAS}2022, Auckland, New Zealand, May 9-13, 2022 (pp. 427--435). International Foundation for Autonomous Agents and Multiagent Systems (IFAAMAS). https://doi.org/10.5555/3535850.3535899. [BibTeX]
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]
Jesús Vega,  M. T. Ceballos,  B. Cobo,  F. J. Carrera,  Pere Garcia Calvés,  & Josep Puyol-Gruart (2022). Event Detection and Reconstruction Using Neural Networks in TES Devices: a Case Study for Athena/X-IFU. Publications of the Astronomical Society of the Pacific, 134, 024504. https://doi.org/10.1088/1538-3873/ac5159. [BibTeX]  [PDF]
Dave de Jonge,  & Dongmo Zhang (2022). {GDL}as a Unifying Domain Description Language for Declarative Automated Negotiation. Piotr Faliszewski, Viviana Mascardi, Catherine Pelachaud, & Matthew E. Taylor (Eds.), 21st International Conference on Autonomous Agents and Multiagent Systems, {AAMAS}2022, Auckland, New Zealand, May 9-13, 2022 (pp. 1935--1937). International Foundation for Autonomous Agents and Multiagent Systems {(IFAAMAS)}. https://doi.org/10.5555/3535850.3536158. [BibTeX]  [PDF]
Rodríguez Soto,  Marc Serramia,  Maite López-Sánchez,  & Juan A. Rodríguez-Aguilar<code> (2022). Instilling moral value alignment by means of multi-objective reinforcement learning. Ethics and Information Technology, 24. https://doi.org/10.1007/s10676-022-09635-0. [BibTeX]  [PDF]
Dave de Jonge,  Filippo Bistaffa,  & Jordi Levy (2022). Multi-Objective Vehicle Routing with Automated Negotiation. Applied Intelligence, 52, 16916-16939. https://doi.org/10.1007/s10489-022-03329-2. [BibTeX]  [PDF]
Jianglin Qiao,  Dongmo Zhang,  & Dave de Jonge (2022). Priority-Based Traffic Management Protocols for Autonomous Vehicles on Road Networks. Guodong Long, Xinghuo Yu, & Sen Wang (Eds.), {AI}2021: Advances in Artificial Intelligence - 34th Australasian Joint Conference, {AI}2021, Sydney, NSW, Australia, February 2-4, 2022, Proceedings (pp. 240--253). Springer. https://doi.org/10.1007/978-3-030-97546-3\_20. [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]
Alejandra Aberasturi Gómez,  Jordi Sabater-Mir,  & Carles Sierra (2022). Probabilistic Models for Competence Assessment in Education. Applied Sciences, 12. https://doi.org/10.3390/app12052368. [BibTeX]  [PDF]
Nieves Montes,  & Carles Sierra (2022). Synthesis and Properties of Optimally Value-Aligned Normative Systems. Journal of Artificial Intelligence Research, 74, 1739--1774. https://doi.org/10.1613/jair.1.13487. [BibTeX]
Ignacio Huitzil,  Marco Schorlemmer,  Nardine Osman,  Pere Garcia Calvés,  Josep Coll,  & Xavier Coll (2022). Towards Automated Compliance Checking of Building Regulations: smartNorms4BIM. Atia Cortés, Francisco Grimaldo, & Tommaso Flaminio (Eds.), Artificial Intelligence Research and Development. Proceedings of the 24th International Conference of the Catalan Association for Artificial Intelligence (CCIA 2022) (pp. 95--104). IOS Press. https://doi.org/10.3233/FAIA220322. [BibTeX]
Roger Lera-Leri,  Filippo Bistaffa,  Marc Serramia,  Maite López-Sánchez,  & Juan A. Rodríguez-Aguilar (2022). Towards Pluralistic Value Alignment: Aggregating Value Systems through ℓₚ-Regression. Proceedings of the 21st International Conference on Autonomous Agents and MultiAgent Systems . International Foundation for Autonomous Agents and Multiagent Systems. [BibTeX]  [PDF]
2021
Dimitra Bourou,  Marco Schorlemmer,  & Enric Plaza (2021). A Cognitively-Inspired Model for Making Sense of Hasse Diagrams. Proc. of the 23rd International Conference of the Catalan Association for Artificial Intelligence (CCIA 2021), October 20-22, Lleida, Catalonia, Spain . [BibTeX]
Filippo Bistaffa,  Christian Blum,  Jesús Cerquides,  Alessandro Farinelli,  & Juan A. Rodríguez-Aguilar (2021). A Computational Approach to Quantify the Benefits of Ridesharing for Policy Makers and Travellers. IEEE Transactions on Intelligent Transportation Systems, 22, 119-130. https://doi.org/10.1109/TITS.2019.2954982. [BibTeX]  [PDF]
Filippo Bistaffa (2021). A Concise Function Representation for Faster Exact {MPE}and Constrained Optimisation in Graphical Models. CoRR, abs/2108.03899. https://doi.org/https://arxiv.org/abs/2108.03899. [BibTeX]  [PDF]
Dave de Jonge,  Filippo Bistaffa,  & Jordi Levy (2021). A Heuristic Algorithm for Multi-Agent Vehicle Routing with Automated Negotiation. Frank Dignum, Alessio Lomuscio, Ulle Endriss, & Ann Now{\\'{e}} (Eds.), {AAMAS}'21: 20th International Conference on Autonomous Agents and Multiagent Systems, Virtual Event, United Kingdom, May 3-7, 2021 (pp. 404--412). {ACM}. https://doi.org/https://dl.acm.org/doi/10.5555/3463952.3464004. [BibTeX]  [PDF]
Jaume Agustí-Cullell,  & Marco Schorlemmer (2021). A Humanist Perspective on Artificial Intelligence. Comprendre, 23, 99--125. [BibTeX]
Carles Sierra (2021). AI's Responsible Agency. . [BibTeX]
Ángeles Manjarrés,  Celia Fernández-Aller,  Maite López-Sánchez,  Juan A. Rodríguez-Aguilar,  & Manuel Sierra Castañer (2021). Artificial Intelligence for a Fair, Just, and Equitable World. IEEE Technology and Society Magazine, 40, 19-24. https://doi.org/10.1109/MTS.2021.3056292. [BibTeX]  [PDF]
Marco Schorlemmer,  & Enric Plaza (2021). A Uniform Model of Computational Conceptual Blending. Cognitive Systems Research, 65, 118--137. https://doi.org/10.1016/j.cogsys.2020.10.003. [BibTeX]  [PDF]
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]
Pablo Noriega,  Harko Verhagen,  Julian Padget,  & Mark d'Inverno (2021). Ethical Online AI Systems Through Conscientious Design. IEEE Internet Computing, 25, 58-64. https://doi.org/10.1109/MIC.2021.3098324. [BibTeX]  [PDF]
Dave de Jonge,  & Dongmo Zhang (2021). GDL as a unifying domain description language for declarative automated negotiation. Autonomous Agents and Multi-Agent Systems, 35. https://doi.org/10.1007/s10458-020-09491-6. [BibTeX]  [PDF]
Manel Rodríguez Soto,  Maite López-Sánchez,  & Juan A. Rodríguez-Aguilar (2021). Guaranteeing the Learning of Ethical Behaviour through Multi-Objective Reinforcement Learning. . Adaptive and Learning Agents Workshop at AAMAS 2021 (ALA 2021). [BibTeX]  [PDF]
Dimitra Bourou,  Marco Schorlemmer,  & Enric Plaza (2021). Image Schemas and Conceptual Blending in Diagrammatic Reasoning: the Case of Hasse Diagrams. Amrita Basu, Gem Stapleton, Sven Linker, Catherine Legg, Emmanuel Manalo, & Petrucio Viana (Eds.), Diagrammatic Representation and Inference. 12th International Conference, Diagrams 2021, Virtual, September 28–30, 2021, Proceedings (pp. 297-314). [BibTeX]
Maite Lopez-Sanchez,  Marc Serramia,  & Juan A Rodríguez-Aguilar (2021). Improving on-line debates by aggregating citizen support. Artificial Intelligence Research and Development. IOS Press. [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.