AI-Assisted Goal Setting Improves Goal Progress Through Social Accountability

2026-03-18 · Michel Schimpf, Julian Voigt, Thomas Bohné arxiv

Helping people identify and pursue personally meaningful career goals at scale remains a key challenge in applied psychology. Career coaching can improve goal quality and attainment, but its cost and limited availability restrict access. Large language model (LLM)-based chatbots offer a scalable alternative, yet the psychological mechanisms by which they might support goal pursuit remain untested. Here we report a preregistered three-arm randomised controlled trial (N = 517) comparing an AI career coach ("Leon," powered by Claude Sonnet), a matched structured written questionnaire covering closely matched reflective topics, and a no-support control on goal progress at a two-week follow-up. The AI chatbot produced significantly higher goal progress than the control (d = 0.33, p = .016). Compared with the written-reflection condition, the AI did not significantly improve overall goal progress, but it increased perceived social accountability. In the preregistered mediation model, perceived accountability mediated the AI-over-questionnaire effect on goal progress (indirect effect = 0.15, 95% CI [0.04, 0.31]), whereas self-concordance did not. These findings suggest that AI-assisted goal setting can improve short-term goal progress, and that its clearest added value over structured self-reflection lies in increasing felt accountability.

📄 PDF Abstract BibTeX arXiv:2603.17887

Code (0)

등록된 구현이 없습니다.

OnGoal: Tracking and Visualizing Conversational Goals in Multi-Turn Dialogue with Large Language Models

2025-08-28 · Adam Coscia, Shunan Guo, Eunyee Koh, Alex Endert arxiv

As multi-turn dialogues with large language models (LLMs) grow longer and more complex, how can users better evaluate and review progress on their conversational goals? We present OnGoal, an LLM chat interface that helps…

Learning to Assist Agents by Observing Them

2021-10-04 · Antti Keurulainen, Isak Westerlund, Samuel Kaski, Alexander Ilin

The ability of an AI agent to assist other agents, such as humans, is an important and challenging goal, which requires the assisting agent to reason about the behavior and infer the goals of the assisted agent. Training…

AI Agent reinforcement-learning Reinforcement Learning Reinforcement Learning (RL)

Deep Reinforcement Learning for Optimizing RIS-Assisted HD-FD Wireless Systems

2021-10-10 · Alice Faisal, Ibrahim Al-Nahhal, Octavia A. Dobre, Telex M. N. Ngatched

This letter investigates the reconfigurable intelligent surface (RIS)-assisted multiple-input single-output (MISO) wireless system, where both half-duplex (HD) and full-duplex (FD) operating modes are considered together…

Deep Reinforcement Learning reinforcement-learning Reinforcement Learning (RL)

GR-MG: Leveraging Partially Annotated Data via Multi-Modal Goal-Conditioned Policy

2024-08-26 · Peiyan Li, Hongtao Wu, Yan Huang, Chilam Cheang 외

The robotics community has consistently aimed to achieve generalizable robot manipulation with flexible natural language instructions. One primary challenge is that obtaining robot trajectories fully annotated with both …

Few-Shot Learning Image Generation Robot Manipulation Zero-shot Generalization

SurgGoal: Rethinking Surgical Planning Evaluation via Goal-Satisfiability

2026-01-15 · Ruochen Li, Kun Yuan, Yufei Xia, Yue Zhou 외 arxiv

Surgical planning integrates visual perception, long-horizon reasoning, and procedural knowledge, yet it remains unclear whether current evaluation protocols reliably assess vision-language models (VLMs) in safety-critic…