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Mindalogue: LLM-Powered Nonlinear Interaction for Effective Learning and Task Exploration

Rui Zhang, Ziyao Zhang, Fengliang Zhu, Jiajie Zhou, Anyi Rao

TL;DR

Mindalogue, a system using a non-linear interaction model based on nodes + canvas to enhance user efficiency and freedom while generating structured responses to highlight the potential of non-linear interaction in improving AI tool efficiency and user experience in the HCI field.

Abstract

Current generative AI models like ChatGPT, Claude, and Gemini are widely used for knowledge dissemination, task decomposition, and creative thinking. However, their linear interaction methods often force users to repeatedly compare and copy contextual information when handling complex tasks, increasing cognitive load and operational costs. Moreover, the ambiguity in model responses requires users to refine and simplify the information further. To address these issues, we developed "Mindalogue", a system using a non-linear interaction model based on "nodes + canvas" to enhance user efficiency and freedom while generating structured responses. A formative study with 11 users informed the design of Mindalogue, which was then evaluated through a study with 16 participants. The results showed that Mindalogue significantly reduced task steps and improved users' comprehension of complex information. This study highlights the potential of non-linear interaction in improving AI tool efficiency and user experience in the HCI field.

Mindalogue: LLM-Powered Nonlinear Interaction for Effective Learning and Task Exploration

TL;DR

Mindalogue, a system using a non-linear interaction model based on nodes + canvas to enhance user efficiency and freedom while generating structured responses to highlight the potential of non-linear interaction in improving AI tool efficiency and user experience in the HCI field.

Abstract

Current generative AI models like ChatGPT, Claude, and Gemini are widely used for knowledge dissemination, task decomposition, and creative thinking. However, their linear interaction methods often force users to repeatedly compare and copy contextual information when handling complex tasks, increasing cognitive load and operational costs. Moreover, the ambiguity in model responses requires users to refine and simplify the information further. To address these issues, we developed "Mindalogue", a system using a non-linear interaction model based on "nodes + canvas" to enhance user efficiency and freedom while generating structured responses. A formative study with 11 users informed the design of Mindalogue, which was then evaluated through a study with 16 participants. The results showed that Mindalogue significantly reduced task steps and improved users' comprehension of complex information. This study highlights the potential of non-linear interaction in improving AI tool efficiency and user experience in the HCI field.

Paper Structure

This paper contains 91 sections, 8 figures, 2 tables.

Table of Contents

  1. Introduction
  2. Related Work
  3. Linear Interaction vs. Non-linear Interaction
  4. Text Visualization and Structuring
  5. User Task Cognition and Exploration
  6. Formative Study
  7. Participants and Procedure
  8. Method
  9. Data Collection
  10. Data Analysis
  11. Findings and Discussion
  12. DC1. Limitations and operational inconvenience of linear interaction.
  13. DC2. Expectation for deeper answers and exploration.
  14. DC3. Need for graphical representation.
  15. DC4. Need for structured information and information management.
  16. ...and 76 more sections

Figures (8)

  • Figure 1: Linear Interaction vs Non-linear Interaction. Linear interaction following a fixed sequence, while non-linear interaction allows flexible exploration and dynamic content structuring through a network of nodes.
  • Figure 2: The user interaction in Mindalogue and the four levels of structured content that the system generates.
  • Figure 3: The three core functions of the Mindalogue system, including Node Explanation, Node Examples, and Node Exploration. Through these features, users are able to gain insight into the context and details of concepts, supporting multiple levels of content interaction and exploration.
  • Figure 4: Mindalogue system's custom exploration feature allows users to flexibly explore and adjust the structure of the content by adding, deleting, and collapsing nodes and other operations for non-linear interaction.
  • Figure 5: Evaluation Study Procedure with 3 Phases.
  • ...and 3 more figures