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Towards Human-AI Collaborative Infor...

Zheng, Chengbo,

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Towards Human-AI Collaborative Information Synthesis /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Towards Human-AI Collaborative Information Synthesis // Chengbo Zheng.
作者:	Zheng, Chengbo,
面頁冊數:	1 electronic resource (199 pages)
附註:	Source: Dissertations Abstracts International, Volume: 86-06, Section: B.
Contained By:	Dissertations Abstracts International86-06B.
標題:	Large language models. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=31810402
ISBN:	9798346823568

Towards Human-AI Collaborative Information Synthesis /
Zheng, Chengbo,

Towards Human-AI Collaborative Information Synthesis /Chengbo Zheng. - 1 electronic resource (199 pages)

Source: Dissertations Abstracts International, Volume: 86-06, Section: B.

Synthesizing information into a summative and coherent representation is a common necessity. For example, researchers need to synthesize relevant literature to ground their studies. The information synthesis process typically involves gathering and evaluating information from various sources, transforming information, and organizing it coherently. Although foundation models, such as large language models (LLMs), offer significant potential for automating this process, they still exhibit deficiencies like bias and instability. Additionally, users often seek control over the synthesis process to integrate their unique contexts or to learn from the process.This thesis investigates the human-AI collaboration approach to information synthesis, aiming to optimally leverage the capabilities of foundation models while maintaining user agency. In the first part of the thesis, we adopt a user-centered design approach to explore how human-AI collaboration can be integrated into common information synthesis tasks. First, we designed DiscipLink, an LLM-powered interactive system that supports knowledge integration for interdisciplinary research, focusing on guiding the orientation of information gathering and rapid screening of relevant literature. Through two user studies, we found DiscipLink supports researchers more comprehensively in performing interdisciplinary information-seeking and better learning from the search process. Our second work presents NB2Slides, an AI-assisted presentation slide creation system that supports data scientists in synthesizing their technical work for communication with stakeholders. In our exploratory user study, data scientists found NB2Slides easy to use and highly preferred its human-AI collaboration workflow.The second part of this thesis examines how to enable novel information synthesis workflows through human-AI collaboration. Specifically, we explore how to synthesize a self-assessment report from students' use of AI tools in course projects. Lacking the empirical understanding of the needs and challenges in this synthesis task, we approached the problem guided by researchthrough-design (RtD). We initially conducted co-design workshops to explore students' motivations and attitudes. Based on the insights gathered, we designed an intelligent tool, SelfGauge, to help students synthesize their AI usage and project traces into self-assessment reports, thus supporting their reflection on their AI-enhanced learning journeys.In summary, this thesis contributes design knowledge on forming human-AI workflows for information synthesis and provides an empirical understanding of human-AI interaction to inform future interactive AI system design.

English

ISBN: 9798346823568Subjects--Topical Terms:

3775019
Large language models.

Towards Human-AI Collaborative Information Synthesis /
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520 $a Synthesizing information into a summative and coherent representation is a common necessity. For example, researchers need to synthesize relevant literature to ground their studies. The information synthesis process typically involves gathering and evaluating information from various sources, transforming information, and organizing it coherently. Although foundation models, such as large language models (LLMs), offer significant potential for automating this process, they still exhibit deficiencies like bias and instability. Additionally, users often seek control over the synthesis process to integrate their unique contexts or to learn from the process.This thesis investigates the human-AI collaboration approach to information synthesis, aiming to optimally leverage the capabilities of foundation models while maintaining user agency. In the first part of the thesis, we adopt a user-centered design approach to explore how human-AI collaboration can be integrated into common information synthesis tasks. First, we designed DiscipLink, an LLM-powered interactive system that supports knowledge integration for interdisciplinary research, focusing on guiding the orientation of information gathering and rapid screening of relevant literature. Through two user studies, we found DiscipLink supports researchers more comprehensively in performing interdisciplinary information-seeking and better learning from the search process. Our second work presents NB2Slides, an AI-assisted presentation slide creation system that supports data scientists in synthesizing their technical work for communication with stakeholders. In our exploratory user study, data scientists found NB2Slides easy to use and highly preferred its human-AI collaboration workflow.The second part of this thesis examines how to enable novel information synthesis workflows through human-AI collaboration. Specifically, we explore how to synthesize a self-assessment report from students' use of AI tools in course projects. Lacking the empirical understanding of the needs and challenges in this synthesis task, we approached the problem guided by researchthrough-design (RtD). We initially conducted co-design workshops to explore students' motivations and attitudes. Based on the insights gathered, we designed an intelligent tool, SelfGauge, to help students synthesize their AI usage and project traces into self-assessment reports, thus supporting their reflection on their AI-enhanced learning journeys.In summary, this thesis contributes design knowledge on forming human-AI workflows for information synthesis and provides an empirical understanding of human-AI interaction to inform future interactive AI system design.
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