The First Workshop on AI-supported Education for Computer Science (AIEDCS) held at AIED 2013

on July 13 in Memphis, Tennessee, USA

*we are at Room 226
The global economy depends increasingly upon Computer Science and Information Technology professionals to maintain and expand the infrastructure on which business, education, governments, and social networks rely. Demand is growing for a global workforce that can keep up with the increasingly pervasive technology and hard computational problems. For these reasons, there is increased recognition that computer science and informatics are becoming, and should become, part of a well-rounded education for every student.  However, along with an increased number and diversity of students studying computing comes the need for more supported instruction and an expansion in pedagogical tools to be used with novices.  In particular, the study of computer science often requires a large element of practice, often self-guided as homework or lab work.  The use of practice as a significant piece of the learning process is a perfect place for AI-supported tools to become an integral part of current course practices.  

Designing and deploying AI techniques within computer science learning environments presents numerous important challenges. First, computer science focuses largely on problem solving skills in a domain with an infinitely large problem space.  Modeling the possible problem solving strategies of experts and novices requires techniques that represent a large and complex solution space and address many types of unique but correct solutions to problems.  Additionally, with current approaches to intelligent learning environments for computer science, problems that are provided by AI-supported educational tools are often difficult to generalize to new contexts. The need is great for advances that address these challenging research problems. Finally, there is growing need to support affective and motivational aspects of computer science learning, to address widespread attrition of students from the discipline. Addressing these problems as a research community, AIED researchers are poised to make great strides in building intelligent, highly effective AI-supported learning environments and educational tools for computer science and information technology.

Topics of interest 

  • Student modeling for computer science learning
  • Adaptation and personalization within computer science learning environments
  • AI-supported tools that support teachers or instructors of computer science
  • Intelligent support for pair programming or collaborative computer science problem solving
  • Automatic question generation or programming problem generation techniques
  • Affective and motivational concerns related to computer science learning
  • Automatic computational artifact analysis or goal/plan recognition to support adaptive feedback or automated assessment
  • Discourse and dialogue research related to classroom, online, collaborative, or one-on-one learning of computer science
  • Online or distributed learning environments for computer science


  • Barbara Di Eugenio, University of Illinois at Chicago
  • Sharon I-Han HsiaoColumbia University
  • Kristy Elizabeth Boyer, North Carolina State University
  • Nguyen-Thinh Le, Clausthal University of Technology
  • Leigh Ann Sudol-DeLyser, New York University