Authoring LLM-Based Assistance for Real-World Contexts and Tasks

Titelangaben

Dang, Hai ; Lafreniere, Ben ; Grossman, Tovi ; Todi, Kashyap ; Li, Michelle:
Authoring LLM-Based Assistance for Real-World Contexts and Tasks.
In: Proceedings of the 30th International Conference on Intelligent User Interfaces. - New York, NY : Association for Computing Machinery , 2025 . - S. 211-230
ISBN 9798400713064
DOI: https://doi.org/10.1145/3708359.3712164

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Abstract

Advances in AI hold the possibility of assisting users with highly varied and individual needs, but the breadth of assistance that these systems could provide creates a challenge for how users specify their goals to the system. To support the authoring of AI assistance for real-world tasks, we propose the concept of Contextually-Driven Prompts (CDPs) that define how an AI assistant should respond to real-world context. We implemented a prototype system for authoring and executing CDPs, which provides suggestions to assist users with finding the right level of assistance for their goal. We also conducted a user study (N=10) to investigate how participants express and refine their goals for real-world tasks. Results revealed a number of strategies for initiating and refining CDPs with suggestions, and implications for the design of future authoring interfaces.