In client-AI expert collaborations, the planning stage of AI application development begins from the client; a client outlines their needs and expectations while assessing available resources (pre-collaboration planning). Despite the importance of pre-collaboration plans for discussions with AI experts for iteration and development, the client often fails to reflect their needs and expectations into a concrete actionable plan. To facilitate pre-collaboration planning, we introduce PlanTogether, a system that generates tailored client support using large language models and a Planning Information Graph, whose nodes and edges represent information in the plan and the information dependencies. Using the graph, the system links and presents information that guides client's reasoning; it provides tips and suggestions based on relevant information and displays an overview to help understand the progression through the plan. A user study validates the effectiveness of PlanTogether in helping clients navigate information dependencies and write actionable plans reflecting their domain expertise.
@inproceedings{kim2025plantogether,
author = {Kim, Dae Hyun and Jeong, Daeheon and Yadgarova, Shakhnozakhon and Shin, Hyungyu and Son, Jinho and Subramonyam, Hariharan and Kim, Juho},
title = {PlanTogether: Facilitating AI Application Planning Using Information Graphs and Large Language Models},
year = {2025},
isbn = {9798400713941},
publisher = {Association for Computing Machinery},
address = {New York, NY, USA},
url = {https://doi.org/10.1145/3706598.3714044},
doi = {10.1145/3706598.3714044},
booktitle = {Proceedings of the 2025 CHI Conference on Human Factors in Computing Systems},
articleno = {636},
numpages = {23},
keywords = {AI application planning, planning support system, information graph, personalized guidance},
location = {Yokohama, Japan},
series = {CHI '25}
}