Generative Problem Solving (GPS) is defined as the capability of automatically or semi-
automatically generating data, problems, and solutions. In addition to text-based
question answering, GPS addresses computational problem solving that integrates
classic and modern AI technologies, data and knowledge engineering, and other
computer science disciplines.

TOPICS OF INTEREST​​ include applications of Generative AI and Generative Problem
Solving for learning of various subjects in K12 and higher education that include, but are
not limited to:

• Sciences

• Technology

• Engineering

• Mathematics

• Art

• Social studies

• Humanities

• Music

• Geography

• Health

• Languages

• Economics

 

Generative technologies for:

• Structured, semi-structured, and unstructured data

• Use cases

• Scenarios

• Problems

• Solutions

And fundamental AI and computer science technologies that include, but are not limited
to:

• Computer vision

• Software engineering

• Computer architecture

• Robotics

• Multimedia

• Data and knowledge engineering  

• Semantic computing

• Multi-modal human-computer interaction

 

Contributions will be reviewed for quality and relevance to the workshop’s theme.
Theoretical and applied papers and papers that capture best practices and lessons
learned from field studies are encouraged. Submission of preliminary results would also
be considered.

 

Submissions in PDF format through this link by August 20, 2025.

Workshop Organizers
 
Dick Bulterman, Vrije Universiteit Amsterdam, The Netherlands
Chih-Hung Chang, Washington University School of Medicine in St. Louis, USA
Bryan Chou, Cal State Pomona, USA 
Daniela D'Auria, Free University of Bozen-Bolzano, Italy
Gary Glesener, Access Physical Models, LLC, USA
Julienne Greer, University of Texas, Arlington, USA
Luca Muratore, IIT, Italy
​Fabio Persia, University of L'Aquila, Italy
Giovanni Pilato, Italian Research Council, Italy
Florian Schimanke, HSW University of Applied Sciences, Germany

Mustafa Sert, Başkent University, Turkey
Phillip C.-Y. Sheu, University of California, Irvine, USA
Atsuo Yoshitaka, JAIST, Japan