The rapid advancements in generative artificial intelligence (AI), particularly in large language models (LLMs) and multimodal models, are set to revolutionize the landscape of education. These AI-driven technologies offer immense potential to transform how students’ epistemic progression grows, how educators can develop richer content, and how Gen AI can help in decision making process involved in the process of assessment desigining, administration and evaluation. Beyond test development and automated (or semi-automated) assessment, Gen-AI can enhance personalized learning, content generation, and support for creative and higher-order thinking skills linked to problem-based learning in the classroom, sparking a new era of education. 

This full day workshop, titled "Epistemics and Decision-Making in AI-Supported Education," aims to bring together researchers, educators, and AI practitioners to explore the implications of generative AI for education. The workshop will explore the intersection of Gen-AI, large foundation models (e.g., GPT-4, LLaMA, Gemini), and educational practices, focusing on how these technologies can be leveraged to create intelligent, adaptive, and inclusive learning environments. However, with these opportunities come challenges, such as the need for explainability, accountability, and trustworthiness in the decision-making process of AI-driven educational tools, which are critical for ensuring broad adoption and equitable access. 

The workshop will feature keynote talks, peer-reviewed papers, and discussions on the latest research and practical applications of Gen-AI in education. Selected papers will be published as the proceedings of AIED. This workshop is designed to foster collaboration and knowledge-sharing across fields, promote innovation, and address the ethical and technical challenges posed by generative AI in education. Participating in this event means you are not just an attendee but an integral part of a community working towards a common goal.

Topics of interest include, but are not limited to: 

• Large foundation models for educational content generation and assessment 

• Personalized learning and decision-making with generative AI 

• AI-enhanced creativity and problem-solving in education 

• Generative AI for supporting higher-order thinking and creative tasks 

• AI-based agents that support adaptivity and collaborative learning 

• Trustworthy and fair AI for education (explainability, fairness, privacy) 

• Ethical and security concerns in generative AI for education 

• Generative multi-agent systems for educational decision-making 

• Fine-tuning large models for specific educational contexts and challenges 

• Practical use cases of AI applications in the classroom and explore the evolving roles of teachers in integrating AI into their epistemic practices.