Panel Session

“The AI-LEAP project: LEArning Personalization with AI and of AI”

The last few years have witnessed a growing attention towards Artificial Intelligence (AI), whose tools and techniques found widespread application with a significant impact on the life of each of us, with normative, political, social, economic, ethical, psychological implications. However, the same years also witnessed a growing gap between the progress of science, on one side, and people's knowledge even of the basic principles of AI, on the other. The complex process of construction/interpretation of the AI-permeated world requires the construction of the right conceptual tools in the people and in the society: “AI is a question of culture”. Multiple competences, and abilities come into play for correctly reading and understanding AI at work, instead of watching it with suspicion or with awe. Personalization is critical to developing methods and techniques that democratise design, innovation, and knowledge creation, and make citizens aware and active members of society. The individual characteristics of the learners play a critical role in preparing them for life with AI and for a potentially unpredictable job market, helping them develop an understanding of how everyday technologies work, to increase the supply of talent, and to train future professionals in a variety of fields and help them understand AI and integrate it into their work in an ethical and safe manner. The project AI-LEAP seeks to develop innovative solutions to promote learning of AI and learning with AI. The challenges of AI-LEAP are threefold:

● Educational challenge: design personalised learning experiences (how do learners differ in terms of cognitive abilities? How to organise learning materials?)
● Technological challenge: develop and implement personalised tools that can be tailored to the individual learner (how can AI be used to avoid purely algorithm-driven execution?)
● Dissemination challenge: facilitate the growth of an AI culture (how can fear/awe of AI be avoided? How can the actual functioning/benefits of AI be made accessible?)

The project is organised into three sub-projects.
The T3-AI (Personalizing Test to Tailor Training of AI) will investigate how early identification of each child’s differential mastery of foundational cognitive capabilities involved in machine learning, probabilistic AI, and symbolic AI allows for a personalised training that strengthens the individual weaknesses.

The Teach E-AI 2C (Teaching Embodied Artificial Intelligence to Children) sub-project, led by Univ. of Naples, Italy, aims at devising specific learning materials that can be assembled into a personalised learning experience tailored to the individual’s mastery of specific skills. The sub-project is based on hands-on educational practices that allow learning by doing. This sub-project is focused on Embodied AI and overcoming a strictly algorithm-driven approach to AI by explaining how biological systems work, how to replicate them in artificial systems, how to develop intelligent behaviors and how to apply these principles to artificial systems that interact with the physical world, i.e. robots.

The PTPC-AI (Personalized Training of Professional Competencies with AI) sub-project, led by Univ. of Eastern Piedmont, Italy, is based on the assumption that the personalization that can be achieved through the use of AI representations and reasoning is key to effective and tailored training for professionals who are not IT, such as physicians, and proposes advanced medical education through different forms of simulation based on computerised clinical guidelines.

Panel Speakers

Matteo Baldoni is full professor at the Department of Computer Science of the University of Torino since 2021. He received a Ph.D. in Computer Science in May 1998 with a thesis on automated reasoning about multi-modal logics and its application to logic programming. He has over 25 years of experience of research in AI, and he has expertise in the fields of multi-agent systems, computational logic, semantic web, agent-oriented software engineering, socio-technical systems, interaction protocols, and business process management, e-learning. He co-authored more than 150 research journal papers and conference papers and he edited 20 volumes published by international publishers. He organised the XIII and the XIX International Conference of the Italian Association for Artificial Intelligence (AIxIA 2013, Torino, Italy; AIxIA 2020, virtual conference), the 19th and the 22nd International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2016, Phuket, Thailand; PRIMA 2019, Torino, Italy). He is an elected member of the steering committee of the Italian Association for Artificial Intelligence (since 2011). He was responsible for the sub-project 1, led by University of Torino, of the project SMaILE (Simple Methods for Artificial Intelligence Learning and Education) and the principle investigator of the project AI-LEAP (AI-LEAP:LEArning Personalization with AI and of AI) both founded by Fondazione Compagnia di San Paolo.
Paolo Terenziani was born in Turin (Italy) on July 4th, 1963. Since October 2000, he is Full Professor in the Department of Science and Technological Innovation of the University of Eastern Piedmont, Alessandria, Italy. His research activity started in 1987 and has mainly focused on the areas of Artificial Intelligence, Databases, and Medical Informatics. As regards Artificial Intelligence, his activity initially concerned Natural Language Understanding. After the PhD thesis, he switched to the areas of Knowledge Representation, temporal reasoning, diagnosis, data retrieval, and process mining. In the field of DataBases, he mainly focused on the extension of “standard” relational models and algebrae to deal with time-related phenomena, and with the semantics of temporal databases. As regards Medical Informatics, since 1997 he is involved with two main hospital in Piedmont, Italy, in a long-term project for the development of a semi-automatic manager of clinical guidelines. Paolo Terenziani published more than 150 papers about these topics in refereed international journals and conference proceedings (in particular, 15 IEEE TKDE, 2 AIJ, 9 AIMJ). As a recognition of his research merits, in 1998 he got the annual “Artificial Intelligence Prize” from the Italian Association for Artificial Intelligence.
Michela Ponticorvo, PhD, is currently fixed term researcher, teacher in Psychometrics and scientific responsible of NAC “Orazio Miglino” (Natural and Artificial Cognition Laboratory) at University of Naples “Federico II”. Graduated in Psychology, she obtained a doctorate in Artificial Intelligence and Psychology and she is the author, also together with other members of NAC, of more than 80 works published nationally and internationally. Her activity is dedicated both to basic research and applied research mainly on the study of spatial and numerical representations, the construction of artificial models in the cognitive field and the application of these models in education and psychometrics. She has a long experience in teaching AI, in particular she has been working since 2010 on Evolutionary Robotics and its applications in the education field and she is now a teacher in the post-graduation Course on Artificial Intelligence for Humanities at the Department of Humanities, University of Naples “Federico II”.