79 citations · 89 across the 7 of their papers we have counts for
6 papers · 1 filter
The Missing Evaluation Axis: What 10,000 Student Submissions Reveal About AI Tutor Effectiveness
Rose Niousha, Samantha Boatright Smith, Bita Akram +5
Current Artificial Intelligence (AI)-based tutoring systems (AI tutors) are primarily evaluated based on the pedagogical quality of their feedback messages. While important, pedago…
ParaStudent: Closing the Sim2Real Gap in User Simulators for AI Tutor Evaluation
Rose Niousha, Mihran Miroyan, Abigail O'Neill +4
Evaluating Artificial Intelligence (AI) tutor feedback before deployment requires anticipating student engagement, typically assessed through real interaction data. We introduce Pa…
Pensieve Discuss: Scalable Small-Group CS Tutoring System with AI
Yoonseok Yang, Jack Liu, J. D. Zamfirescu-Pereira +1
Small-group tutoring in Computer Science (CS) is effective, but presents the challenge of providing a dedicated tutor for each group and encouraging collaboration among group membe…
A Knowledge-Component-Based Methodology for Evaluating AI Assistants
Laryn Qi, J. D. Zamfirescu-Pereira, Taehan Kim +3
We evaluate an automatic hint generator for CS1 programming assignments powered by GPT-4, a large language model. This system provides natural language guidance about how students…
61A Bot Report: AI Assistants in CS1 Save Students Homework Time and Reduce Demands on Staff. (Now What?)
J. D. Zamfirescu-Pereira, Laryn Qi, Björn Hartmann +2
LLM-based chatbots enable students to get immediate, interactive help on homework assignments, but even a thoughtfully-designed bot may not serve all pedagogical goals. We report h…
Interleaving Computational and Inferential Thinking: Data Science for Undergraduates at Berkeley
Ani Adhikari, John DeNero, Michael I. Jordan
The undergraduate data science curriculum at the University of California, Berkeley is anchored in five new courses that emphasize computational thinking, inferential thinking, and…