helpResearch questionLLM Code Generation and Programming Assistants
How can LLM-driven assistants (e.g., Copilot) help users learn software features while automating task completion?Developer Tools and Programming Support / LLM Code Generation and Programming Assistants
How can LLM-driven assistants (e.g., Copilot) help users learn software features while automating task completion?
Similar questions
helpResearch questionLLM Code Generation and Programming Assistants
How can an AI assistant supporting natural language interaction be designed to generate complex LLM workflows?helpResearch questionLLM Code Generation and Programming Assistants
What key role does a multi-agent architecture play in collecting requirements and generating LLM workflows?helpResearch questionLLM Code Generation and Programming Assistants
What advantages does ChainBuddy offer over existing tools in reducing LLM workflow design difficulty and improving satisfaction?helpResearch questionLLM Code Generation and Programming Assistants
How can information processing tasks (IPTs) be described in natural language to optimize communication between humans and LLMs?helpResearch questionLLM Code Generation and Programming Assistants
What are the effects of programming experience, example use, and interactivity on task description and understanding?helpResearch questionLLM Code Generation and Programming Assistants
How do LLMs such as GPT-4 differ in performance when directly answering questions versus generating code?Related papers
CHI 2025
ChainBuddy: An AI-assisted Agent System for Generating LLM Pipelines
Jingyue Zhang, Ian Arawjo
CHI 2025
How Humans Communicate Programming Tasks in Natural Language and Implications For End-User Programming with LLMs
Madison Pickering, Helena Williams, Alison Gan
CHI 2025
How Scientists Use Large Language Models to Program
Gabrielle O'Brien
CHI 2025
Do It For Me vs. Do It With Me: Investigating User Perceptions of Different Paradigms of Automation in Copilots for Feature-Rich Software
Anjali Khurana, Xiaotian Su, April Yi Wang
CHI 2024
How Beginning Programmers and Code LLMs (Mis)read Each Other
Sydney Nguyen, Hannah McLean Babe, Yangtian Zi
CHI 2023
"What It Wants Me To Say": Bridging the Abstraction Gap Between End-User Programmers and Code-Generating Large Language Models
Michael Xieyang Liu, Advait Sarkar, Carina Negreanu
UIST 2022
Exploring the Learnability of Program Synthesizers by Novice Programmers
Dhanya Jayagopal, Justin Lubin, Sarah E. Chasins