seneca: A Personalized Conversational Planner

2026-04-21

AI Summary

Existing planning tools struggle to align users’ surface-level requests with their deeper goals and lack mechanisms for sustained tracking, reflective guidance, and accountability. This work proposes a novel personalized planning system that integrates conversational AI for reflective prompting, a persistent goal database, and a multi-source information synchronization processor to dynamically refine recommendations through continuous behavioral pattern analysis. The system innovatively couples goal articulation, long-term tracking, and need-alignment mechanisms to effectively bridge the gap between intention and action. Evaluation employs a phased strategy combining automated testing, simulated user interactions, and longitudinal human studies, demonstrating significant improvements in goal attainment rates, plan realism, and alignment between goals and personal values.

Technology Category

• Humans and AI: Human-Aware Planning and Behavior Prediction
• Multiagent Systems: Multiagent Planning
• Planning, Routing, and Scheduling: Planning with Language Models

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Search and Retrieval-Augmented AI: Personalized, context-aware and across-device search
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

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.

Problem

• planning tools
• self-regulation
• goal representation
• user needs
• knowledge work

Innovation

• personalized conversational planner
• goal-value alignment
• behavioral pattern tracking
• reflective scaffolding
• AI-assisted planning