When giving automated feedback to a student working on a beginner's exercise, many programming tutors run into a completeness problem. On the one hand, we want a student to experiment freely. On the other hand, we want a student to write her program in such a way that we can provide constructive feedback. We propose to investigate how we can overcome this problem by using program synthesis, which we use to generate correct solutions that closely match a student program, and give feedback based on the results.
Towards SocratiCode: Designing a Generative AI-Based Programming Tutor for K-12 Students through a 4-Week Participatory Design Study
Harry A. Burke High School（哈利·A·伯克高中）；Millard North High School（米拉德北高中）；Missouri University of Science and Technology（密苏里科技大学）；University of Nebraska Omaha（内布拉斯加大学奥马哈分校）
Leveraging Large Language Model and Story-Based Gamification in Intelligent Tutoring System to Scaffold Introductory Programming Courses: A Design-Based Research Study
Do Hackers Dream of Electric Teachers?: A Large-Scale, In-Situ Measurement of Cybersecurity Student Behaviors and Educational Performance with AI Tutors
Arizona State University（亚利桑那州立大学）；Vanderbilt University（范德堡大学）
The Missing Evaluation Axis: What 10,000 Student Submissions Reveal About AI Tutor Effectiveness
University of California, Berkeley, USA（加州大学伯克利分校）；North Carolina State University, USA（北卡罗来纳州立大学）；University of Pittsburgh, USA（匹兹堡大学）；Aalto University, Finland（阿尔托大学）
