mlsanityData to 7 Oct 2026

PlanFitting: Personalized Exercise Planning with Large Language Model-driven Conversational Agent

arXiv 2309.12555 ↗First posted 22 Sep 2023cs.HC cs.AI cs.CLcited by 6

On the map: LLM agents and tool use → General LLM agent frameworks and benchmarks

Abstract (arXiv)

Creating personalized and actionable exercise plans often requires iteration with experts, which can be costly and inaccessible to many individuals. This work explores the capabilities of Large Language Models (LLMs) in addressing these challenges. We present PlanFitting, an LLM-driven conversational agent that assists users in creating and refining personalized weekly exercise plans. By engaging users in free-form conversations, PlanFitting helps elicit users' goals, availabilities, and potential obstacles, and enables individuals to generate personalized exercise plans aligned with established exercise guidelines. Our study -- involving a user study, intrinsic evaluation, and expert evaluation -- demonstrated PlanFitting's ability to guide users to create tailored, actionable, and evidence-based plans. We discuss future design opportunities for LLM-driven conversational agents to create plans that better comply with exercise principles and accommodate personal constraints.

Key facts. Cited by 6 papers in the map (data through 7 Oct 2026). Placed in General LLM agent frameworks and benchmarks, one of 16 directions in LLM agents and tool use. Builds on 8 of its 67 references that are in the map. First posted on arXiv 22 Sep 2023.

Builds on

8 of 67 references.

Closest ancestors · 1

Same direction, newest first

  1. 2023Multi-Agent Collaboration: Harnessing the Power of Intelligent LLM Agents5 Jun

Other references · 7

  1. 2022Large Language Models Encode Clinical Knowledgecited by 1,807
  2. 2021Retrieval Augmentation Reduces Hallucination in Conversationcited by 691
  3. 2022Large Language Models Struggle to Learn Long-Tail Knowledgecited by 452
  4. 2023PALR: Personalization Aware LLMs for Recommendationcited by 96
  5. 2022Keep Me Updated! Memory Management in Long-term Conversationscited by 62
  6. 2023Leveraging Large Language Models to Power Chatbots for Collecting User Self-Reported Datacited by 48
  7. 2022MyMove: Facilitating Older Adults to Collect In-Situ Activity Labels on a Smartwatch with Speechcited by 4

Built on by

Cited by year (2026 to 7 Oct)

Citing papers per year: 2025: 1; 2026: 5.

6 papers.

  1. 2026Toward Personalized LLM-Powered Agents: Foundations, Evaluation, and Future Directionscited by 10
  2. 2026Designing KRIYA: An AI Companion for Wellbeing Self-Reflectioncited by 1
  3. 2025Autiverse: Eliciting Autistic Adolescents' Daily Narratives through AI-guided Multimodal Journalingcited by 0
  4. 2026Cross-Model Consistency of AI-Generated Exercise Prescriptions: A Repeated Generation Study Across Three Large Language Modelscited by 0
  5. 2026Enhancing Fitness Intelligence through Domain-Specific LLM Post-Trainingcited by 0
  6. 2026PDEU-Bench: Benchmarking the Personalized Planning Lifecycle of Tool-Calling LLM Agentscited by 0

Similar, not cited

Close in wording, no citation either way

  1. 2025Conversational Planning for Personal Plans
  2. 2024Navigating the Unknown: A Chat-Based Collaborative Interface for Personalized Exploratory Tasks
  3. 2025Plan-and-Act: Improving Planning of Agents for Long-Horizon Tasks
  4. 2023Beyond ChatBots: ExploreLLM for Structured Thoughts and Personalized Model Responses
  5. 2023Conversational Health Agents: A Personalized LLM-Powered Agent Framework
  6. 2025Enhancing Personalized Multi-Turn Dialogue with Curiosity Reward
  7. 2023Probing the Multi-turn Planning Capabilities of LLMs via 20 Question Games
  8. 2025PersonaLens: A Benchmark for Personalization Evaluation in Conversational AI Assistants