activity
20232026
most citedCodeAid: Evaluating a Classroom Deployment of an LLM-based Programming Assistant that Balances Student and Educator Needs

235 citations · 332 across the 6 of their papers we have counts for

collaborators

6 papers

cs.HC2026★ 1 cited

Gazeify Then Voiceify: Physical Object Referencing Through Gaze and Voice Interaction with Displayless Smart Glasses

Zheng Zhang, Mengjie Yu, Tianyi Wang +6

Smart glasses enhance interactions with the environment by using head-mounted cameras to observe the user's viewpoint, but lack the visual feedback used for common interactions. We…

cs.HC2024

Exploring the Design Space of Cognitive Engagement Techniques with AI-Generated Code for Enhanced Learning

Majeed Kazemitabaar, Oliver Huang, Sangho Suh +2

Novice programmers are increasingly relying on Large Language Models (LLMs) to generate code for learning programming concepts. However, this interaction can lead to superficial en…

cs.HC2024★ 47 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

OmniActions: Predicting Digital Actions in Response to Real-World Multimodal Sensory Inputs with LLMs

Jiahao Nick Li, Yan Xu, Tovi Grossman +2

The progression to "Pervasive Augmented Reality" envisions easy access to multimodal information continuously. However, in many everyday scenarios, users are occupied physically, c…

cs.HC2024★ 235 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.HC2023★ 49 cited

ABScribe: Rapid Exploration & Organization of Multiple Writing Variations in Human-AI Co-Writing Tasks using Large Language Models

Mohi Reza, Nathan Laundry, Ilya Musabirov +7

Exploring alternative ideas by rewriting text is integral to the writing process. State-of-the-art Large Language Models (LLMs) can simplify writing variation generation. However,…