Project

T3-AI

T3-AI has three goals:

T3-AI Goal 1: Facilitate learning of machine learning and probabilistic AI approaches by training basic abilities that are innate to children. The ability to think about the possible/impossible instances of a given category is an example of a basic ability children need in constructing data sets.

T3-AI Goal 2: To develop a tool to assess each child’s developmental level of the basic abilities required to learn AI. Such an assessment would allow personalized training sessions to be developed to improve AI learning.

T3-AI Goal 3: With the support of Treccani Futura and of POP-AI, dissemination of the results of the research, the practices and tools in the school world.

Teach E-AI 2C has three goals:

Teach E-AI 2C Goal 1: Introduce E-AI to children and early adolescents. Based on the profiles of the young learners, activities will be tailored to each of them.

Teach E-AI 2C Goal 2: Create an integrated platform to complement the Teach E-AI 2C learning units. As far as we know, there are no other tools for E-AI that specifically target younger learners and are intuitive and easy-to-use in school context. The project will fill this gap by implementing the Teach E-AI 2C robotic farm, an integrated hardware/software system for evolutionary and interactive robotics. This platform will be implemented in such a way that it can be easily used by children and kids, allowing them to see the fundamentals and concepts of E-AI basics in action and practice them hands-on. In this challenge, implementing a solid and effective system is critical to ensure usability.

Teach E-AI 2C Goal 3: dissemination of educational integrated learning path on E-AI, personalized/customized to different educational needs to a broad audience.

MedEd-AI has three major goals:

MedEd-AI Goal 1: To apply AI methodologies in order to personalize the education of professionals, considering physicians as a concrete example. Specifically, we will address the context of continuous medical education, and we will focus on advanced AI Clinical Decision Support techniques based on computer-interpretable clinical guidelines.

MedEd-AI Goal 2: Design of an innovative AI-based verification framework for evaluation and self-evaluation for the support of personalized training experiences.

MedEd-AI Goal 3. Dissemination is a key challenge in order to lay the foundation for an “AI culture” for professionals, in particular, for what concerns “decision support”.