Knowledge work demands sustained self-regulation, prioritization, and reflection-yet existing planning tools only partially support these needs. Digital to-do list applications feature task persistence but lack goal representation. Paper-based planning frameworks offer effective planning strategies but cannot adapt to individual users. Conversational AI systems enable flexible reflection but lack persistence and accountability. Moreover, none of these tools address a fundamental challenge: users' expressed demands often diverge from their underlying needs. This paper introduces seneca, a conceptual framework for a personalized, AI-assisted planner that integrates the complementary strengths of these three approaches. seneca combines a conversational agent that scaffolds reflection and asks clarifying questions, a persistent database that tracks goals and behavioral patterns, and a processor that synchronizes information between them. We describe this architecture and outline a phased evaluation strategy combining automated testing with simulated users and longitudinal human studies measuring goal attainment, planning realism, and goal-value alignment.

翻译：知识工作需要持续的自律、优先级排序和反思——然而现有的规划工具仅能部分满足这些需求。数字待办事项列表应用虽然具有任务持久性，但缺乏目标表征。基于纸张的规划框架提供了有效的规划策略，但无法适应个体用户。对话式AI系统支持灵活反思，但缺乏持久性和问责机制。更重要的是，这些工具均未解决一个根本性挑战：用户明确表达的需求往往与其潜在真实需求存在偏差。本文提出seneca——一个整合上述三类方法互补优势的个性化AI辅助规划器概念框架。seneca融合了三个组件：通过提问引导反思的对话代理、追踪目标与行为模式的持久化数据库，以及在二者间同步信息的处理器。我们描述了该架构，并概述了阶段性评估策略——该策略结合自动化测试、模拟用户实验，以及测量目标达成度、规划现实性与目标价值一致性的纵向人类研究。