seneca: A Personalized Conversational Planner
Simon Bohnen, Gabriel Garbers, Lukas Ellinger, Georg Groh

TL;DR
seneca is a personalized AI planner that combines conversational reflection, persistent goal tracking, and adaptive synchronization to improve self-regulated planning.
Contribution
introduces seneca, a novel framework integrating conversational AI, persistent data, and synchronization for personalized planning support.
Findings
architecture of seneca described
evaluation strategy outlined with automated and human studies
aims to improve goal alignment and planning realism
Abstract
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…
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