seneca: A Personalized Conversational Planner

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 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.