most citedCodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs

235 citations · 293 across the 5 of their papers we have counts for

collaborators

5 papers

cs.HC202447 cited

Improving Steering and Verification in AI-Assisted Data Analysis with Interactive Task Decomposition

Majeed Kazemitabaar, Jack Williams, Ian Drosos +4

LLM-powered tools like ChatGPT Data Analysis, have the potential to help users tackle the challenging task of data analysis programming, which requires expertise in data processing…

cs.HC2024

Supporting Annotators with Affordances for Efficiently Labeling Conversational Data

Austin Z. Henley, David Piorkowski

Without well-labeled ground truth data, machine learning-based systems would not be as ubiquitous as they are today, but these systems rely on substantial amounts of correctly labe…

cs.HC2024235 cited

CodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs

Majeed Kazemitabaar, Runlong Ye, Xiaoning Wang +4

Timely, personalized feedback is essential for students learning programming. LLM-powered tools like ChatGPT offer instant support, but reveal direct answers with code, which may h…

cs.HC20232 cited

Conversational Challenges in AI-Powered Data Science: Obstacles, Needs, and Design Opportunities

Bhavya Chopra, Ananya Singha, Anna Fariha +4

Large Language Models (LLMs) are being increasingly employed in data science for tasks like data preprocessing and analytics. However, data scientists encounter substantial obstacl…

cs.HC20239 cited

How Novices Use LLM-Based Code Generators to Solve CS1 Coding Tasks in a Self-Paced Learning Environment

Majeed Kazemitabaar, Xinying Hou, Austin Henley +3

As Large Language Models (LLMs) gain in popularity, it is important to understand how novice programmers use them. We present a thematic analysis of 33 learners, aged 10-17, indepe…