Gyapti: First Workshop on AI-Supported Education for Computer Science

Shared by Leigh Ann Sudol-DeLyser (Visiting Scholar, New York University) with the SIGCSE list.Dear SIGCSE-ers! I would like to announce the First Workshop on AI-Supported Education for Computer Science to be held at the Artificial Intelligence in Education conference this summer in Memphis and invite the submission of papers from the SIGCSE community. Please see the website at: https://sites.google.com/site/aiedcs2013/Submissions are due by April 12, 2013.

Workshop Description:

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