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This work aims to enhance long-term task planning by integrating large language models and human-AI collaboration.

March 6, 2026ACM SIGIR Forum0 citations

LLM-Based Long-Term Life Task Planning to Reduce Human Uncertainty

Key Points

This work aims to enhance long-term task planning by integrating large language models and human-AI collaboration.
Proposes a new type of long-term life task.
Introduces the Goal Oriented Long-term liFe planning (GOLF) framework.
Uses a simulation-based approach to evaluate LLMs in various long-term planning scenarios.
Establishes a benchmark for assessing LLM effectiveness.
Demonstrates potential improvements in task decomposition and iterative planning using the GOLF framework.
Shows that LLMs can effectively assist in managing uncertainty during long-term tasks.
Highlights multifaceted contributions to cognitive science, information science, and AI interaction.

Abstract

In long-term life tasks, people often face challenges from uncertainty in tasks and information-seeking, which can create difficulties in decision-making and task completion. Recent advancements in Artificial Intelligence (AI), especially in Large Language Models (LLMs), offer transformative capabilities in domain-specific task planning and problem-solving. Despite these innovations, there is limited understanding of how such technologies can be applied to assist humans in long-term life tasks. This dissertation work seeks to address this gap by exploring how human-AI collaboration, mediated through LLM-based agents, can improve long-term life task planning and uncertainty management. To achieve this, this dissertation first proposes the long-term life task type and investigates how people may use AI tools to assist them in planning long-term life tasks and cope with uncertainty. Secondly, it proposes the Goal Oriented Long-term liFe planning (GOLF) framework that integrates LLMs to emulate human-AI collaboration in diverse long-term life task domains. The framework facilitates task decomposition and iterative planning while evaluating uncertainties throughout the process. Thirdly, the study introduces a GOLF benchmark to evaluate and generalize the effectiveness of LLMs in long-term planning scenarios. This study operates at the intersection of cognitive science, information science, and human-AI interaction, and makes multifaceted contributions. Theoretically, by introducing the concept of long-term life tasks and proposing the GOLF framework, it explores the potential of human-AI collaborations for long-term planning and uncertainty management. Methodologically, through a simulation-based approach and the LLM benchmark, it enables systematic evaluation of LLMs in long-term planning scenarios across domains. This work bridges the gap between human cognitive strategies and AI planning capabilities, providing a robust foundation for future advancements in AI-assisted task management and goal achievement. Awarded by: The University of Oklahoma, Norman, OK, USA on 4 December 2025. Supervised by: Jiqun Liu.

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