Professor Petia Georgieva, DSc
RAG (Retrieval-Augmented Generation)-enhanced platform for AI-supported teaching and learning
Petia Georgieva is a Professor (with habilitation, DSc) in Machine Learning with the Department of Electronics Telecommunications and Informatics (DETI), University of Aveiro, Portugal, and a Senior Researcher with the Institute of Electronics and Informatics Engineering of Aveiro. She was the head of Signal Processing Lab in IEETA (2009-2013), and previously, a visiting professor in the University of Barcelona, Robotics Institute and Machine Learning Department in Carnegie Mellon University, School of Computing and Communications in University of Lancaster. Petia has participated in more than 30 international and national projects devoted to applying AI algorithms for real problems like medical diagnostics, wireless networks management, brain computer interfaces.
Petia has given a number of keynote talks: IEEE International Conference on Automatics, Robotics and Artificial Intelligence (2026); International Conference on Artificial Intelligence (EPIA 2023); IEEE Workshop on Communication Networks and Power Systems (Brazil, 2023, Brazil); International Conference on Smart Objects and Technologies for Social Good (GOODTECHS, 2023), 9th Balkan Conference in Informatics (Bulgaria, 2019); 10th International Conference on Soft Computing and Pattern Recognition (Portugal, 2018).
Petia has supervised 14 PhD students and published more than 150 publications registered in Scopus. She is a member of the Board of Governors of International Neural Network Society and Associate Editor of Elsevier Pattern Recognition journal (Q1, IP=7.6) and Neural Networks journal (Q1, IP=6.3).
Overview
This talk will present the educational platform ChatToCaseStudy developed in the University of Aveiro aiming to embed AI across different subjects, supporting employability for computer science students, and engaging with industry.
The platform combines AI with course materials (slides, papers, books) to support teachers and students in case-based learning contexts. It uses retrieval-augmented generation (RAG) technology, enabling the AI chatbot to respond based on the specific documents, scenarios, and tasks defined by the teacher.
For teachers, the platform provides tools to build a knowledge base from course documents, create and manage case studies, design tasks at different levels of pedagogical support (hints only, guided support, or full explanations), and review student submissions. For students, the platform offers a task-oriented AI chat grounded in course materials, with quick-help shortcuts, multiple-session support, and a clear view of submission status across all enrolled courses.
