Recent developments in Artificial Intelligence are influencing people's everyday lives. The educational context is no exception, and AI is permeating the learning and teaching experiences. New Artificial Intelligence models offer possibilities to create new systems for enhancing the learning experience and supporting teachers in their roles. Applications supported by AI can highly impact both the teacher's and students' experiences, providing value added to education; some examples are systems for the personalization of contents,  automatic assessment, grading, and smart tutoring in general.

Systems for Generative Artificial Intelligence, such as ChatGPT, DALL-E, Gemini, and Claude, with several other Large and Small Language Models and Machine/Deep Learning Systems, are available to many people, and their potential and risks must be investigated. The involvement of experts in the discussion of their impacts, effects, opportunities, and benefits, without underestimating limitations and risks, has become crucial. Specifically designed actions must be undertaken to promote a conscious use of these tools to empower students and teachers.

The workshop aims to gather papers related, but not limited, to the following topics: 

• Artificial Intelligence systems in educational contexts

• New perspective on developing AI applications in education

• Open Source approaches for Large and Small Language Models, with application in Education

• Human-AI cooperation: opportunities for teaching and learning

• Artificial Intelligence to promote inclusive education 

• Tackling challenges of the sustainable development goal 4 (SDG4)

• Developing new skills in the AI era

• Empowering teachers and teaching with artificial Intelligent systems

• Ethical usage of AI algorithms to manage education data

• Explainable AI in the educational contexts

• Automatic Evaluation of Learning and Assessment Content

• Learning Analytics Solutions to Support On-Time Feedback

• Adaptive Lifelong Learning

• AI and Ubiquitous learning

• AI for personalized learning

Davide Taibi:
davide.taibi@itd.cnr.it

Daniele Schicchi:
daniele.schicchi@itd.cnr.it

Marco Temperini:
marte@diag.uniroma1.it 

Carla Limongelli:
carla.limongelli@uniroma3.it

Gabriella Casalino:
gabriella.casalino@uniba.it