What we aim to achieve
Standardized AI Education
Defining clear Intended Learning Outcomes (ILOs) for AI education.
Versatile Databases
Creating diverse, high-quality datasets for experiential learning.
Educational Python Code
Developing hands-on Python programming resources for AI learners.
Interactive MOOC
Launching a dynamic online course for AI education accessibility.
Collaboration & Sharing
Promoting knowledge exchange and best practices in AI education.
Societal Impact
Addressing workforce needs and improving AI literacy worldwide.
What We Have Accomplished
WP3: Creating Databases
- Comprehensive database collection
- Database analysis report
- Implementation of databases into Python
- Data preparation: cleaning, validation, conversion
WP4: AI Python Code
- Survey of AI techniques and methods
- Overview of Python AI libraries
- Development of a unified AI framework
- Interactive AI learning features
WP5: MOOC Development
- MOOC text content creation
- MOOC multimedia content creation (text, figures, videos)
- Fully developed MOOC
- MOOC cross-evaluation
- MOOC translations
Sharing the Outcomes
Publication 1
Reznichenko, Igor; Podržaj, Primož; Požrl, Tomaž; Jovanović, Jelena Saković; Jovanović, Janko; Pašić, Mugdim; Pašić, Mirza; Draskovic, Drazen; Novičić, Marija; Ljubic, Sandi; Batistić, Luka, Cognitive Mapping of Course Syllabi Related to AI: Integrating Bloom’s Taxonomy and Large Language Models, submitted for publication in IEEE Transactions on Learning Technologies
Read MorePartners
UL - University of Ljubljana, Slovenia
Go to page
UNSA - University of Sarajevo, Bosnia and Herzegovina
Go to page
UNIBG - University of Belgrade, Serbia
Go to page
UoM - University of Montenegro, Montenegro
Go to page
UNIRI - University of Rijeka, Croatia
Go to page