mlsanityData to 14 Oct 2026

ChatCLIDS: Simulating Persuasive AI Dialogues to Promote Closed-Loop Insulin Adoption in Type 1 Diabetes Care

arXiv 2509.00891 ↗First posted 31 Aug 2025cs.AI cs.CLcited by 1

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Abstract (arXiv)

Real-world adoption of closed-loop insulin delivery systems (CLIDS) in type 1 diabetes remains low, driven not by technical failure, but by diverse behavioral, psychosocial, and social barriers. We introduce ChatCLIDS, the first benchmark to rigorously evaluate LLM-driven persuasive dialogue for health behavior change. Our framework features a library of expert-validated virtual patients, each with clinically grounded, heterogeneous profiles and realistic adoption barriers, and simulates multi-turn interactions with nurse agents equipped with a diverse set of evidence-based persuasive strategies. ChatCLIDS uniquely supports longitudinal counseling and adversarial social influence scenarios, enabling robust, multi-dimensional evaluation. Our findings reveal that while larger and more reflective LLMs adapt strategies over time, all models struggle to overcome resistance, especially under realistic social pressure. These results highlight critical limitations of current LLMs for behavior change, and offer a high-fidelity, scalable testbed for advancing trustworthy persuasive AI in healthcare and beyond.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 18 of its 60 references that are in the map. First posted on arXiv 31 Aug 2025.

Builds on

18 of 60 references.

References · 18

  1. 2023Judging LLM-as-a-Judge with MT-Bench and Chatbot Arenacited by 8,626
  2. 2023A Survey on Large Language Model based Autonomous Agentscited by 2,242
  3. 2024A Survey on LLM-as-a-Judgecited by 1,423
  4. 2024How Johnny Can Persuade LLMs to Jailbreak Them: Rethinking Persuasion to Challenge AI Safety by Humanizing LLMscited by 573
  5. 2024From Generation to Judgment: Opportunities and Challenges of LLM-as-a-judgecited by 494
  6. 2025HealthBench: Evaluating Large Language Models Towards Improved Human Healthcited by 429
  7. 2023Large Language Models Are State-of-the-Art Evaluators of Translation Qualitycited by 420
  8. 2023GPTScore: Evaluate as You Desirecited by 361
  9. 2024LLM Agents Grounded in Self-Reports Enable General-Purpose Simulation of Individualscited by 249
  10. 2024AgentClinic: a multimodal agent benchmark to evaluate AI in simulated clinical environmentscited by 231
  11. 2023A Comprehensive Analysis of the Effectiveness of Large Language Models as Automatic Dialogue Evaluatorscited by 45
  12. 2022StoryER: Automatic Story Evaluation via Ranking, Rating and Reasoningcited by 21
6 more
  1. 2023PaniniQA: Enhancing Patient Education Through Interactive Question Answeringcited by 21
  2. 2024Comparing Two Model Designs for Clinical Note Generation; Is an LLM a Useful Evaluator of Consistency?cited by 20
  3. 2024MedQA-CS: Objective Structured Clinical Examination (OSCE)-Style Benchmark for Evaluating LLM Clinical Skillscited by 17
  4. 2024Towards Social AI: A Survey on Understanding Social Interactionscited by 16
  5. 2024MCQG-SRefine: Multiple Choice Question Generation and Evaluation with Iterative Self-Critique, Correction, and Comparison Feedbackcited by 14
  6. 2023EHRTutor: Enhancing Patient Understanding of Discharge Instructionscited by 4

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Cited by year (2026 to 14 Oct)

Citing papers per year: 2026: 1.

1 paper.

  1. 2026Rethinking Patient Education as Multi-turn Multi-modal Interactioncited by 0