English

seneca: A Personalized Conversational Planner

Human-Computer Interaction 2026-04-22 v1

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.

Keywords

Cite

@article{arxiv.2604.19425,
  title  = {seneca: A Personalized Conversational Planner},
  author = {Simon Bohnen and Gabriel Garbers and Lukas Ellinger and Georg Groh},
  journal= {arXiv preprint arXiv:2604.19425},
  year   = {2026}
}

Comments

accepted to the CHI '26 Workshop on Tools for Thought

R2 v1 2026-07-01T12:28:18.430Z