AI and Education

Directors of Scientific Unit: Prof. Chiara Panciroli

 

During the past years, developments in Artificial Intelligence (AI) brought substantial changes in the collection and processing of empirical data that play an increasingly relevant role in different contexts of educational research.

Nowadays, educational institutions are managing a massive production of data that feed new AI models based on contents and processes analysis in different fields. The development of intelligent and highly personalized learning environments, for example, nurture the development of increasingly inclusive educational systems able to guarantee universal access to knowledge.

Moreover, one additional area of reflection is the one related to AI and Digital Citizenship, which focuses on how to achieve adequate AI education through training actions and specific tools to achieve an ethic expression about algorithms and technology. This research area leads to important and urgent reflections from a philosophical/pedagogical point of view on the conscious use of AI models in our society.

In relation to these areas, the AI and Education scientific unit aims to develop a multi-prospective research able to identify and propose innovative and sustainable educational models connected to the applications of AI, hence contributing to their wide mapping in different contexts (schools, universities, training institutions, cultural places, public and private associations, societies and territories). This scientific unit’s objective is also to propose and build a methodological research approach aimed at fostering a critical and open understanding of the multiple declinations that are defined in the relationship between AI and education.

In short, some main areas of study:

Artificial Intelligence and educational research

  • Artificially Intelligent agents for Teaching;
  • Learning with AI Systems;
  • Architectures for AI-based Educational Systems;
  • AI for training;
  • Adaptive Educational Systems.

Artificial intelligence and knowledge management

  • Knowledge representation;
  • Big data and Machine learning for education;
  • Robot Intelligence;
  • Social Network Analysis;
  • Change management

Artificial intelligence and ethics

  • AI based social/moral and ethical learning;
  • Socio-cultural effects;
  • Ethical approaches and Normative constraints;
  • Policies on AI in Instruction;
  • AI and digital citizenship.