Supporting Effective Goal Setting with LLM-Based Chatbots
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866914315711283200 |
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| author | Schimpf, Michel Maier, Sebastian Wyrowski, Anton Christoforakos, Lara Feuerriegel, Stefan Bohné, Thomas |
| author_facet | Schimpf, Michel Maier, Sebastian Wyrowski, Anton Christoforakos, Lara Feuerriegel, Stefan Bohné, Thomas |
| contents | Each day, individuals set behavioral goals such as eating healthier, exercising regularly, or increasing productivity. While psychological frameworks (i.e., goal setting and implementation intentions) can be helpful, they often need structured external support, which interactive technologies can provide. We thus explored how large language model (LLM)-based chatbots can apply these frameworks to guide users in setting more effective goals. We conducted a preregistered randomized controlled experiment ($N = 543$) comparing chatbots with different combinations of three design features: guidance, suggestions, and feedback. We evaluated goal quality using subjective and objective measures. We found that, while guidance is already helpful, it is the addition of feedback that makes LLM-based chatbots effective in supporting participants' goal setting. In contrast, adaptive suggestions were less effective. Altogether, our study shows how to design chatbots by operationalizing psychological frameworks to provide effective support for reaching behavioral goals. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2602_08636 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | Supporting Effective Goal Setting with LLM-Based Chatbots Schimpf, Michel Maier, Sebastian Wyrowski, Anton Christoforakos, Lara Feuerriegel, Stefan Bohné, Thomas Human-Computer Interaction Each day, individuals set behavioral goals such as eating healthier, exercising regularly, or increasing productivity. While psychological frameworks (i.e., goal setting and implementation intentions) can be helpful, they often need structured external support, which interactive technologies can provide. We thus explored how large language model (LLM)-based chatbots can apply these frameworks to guide users in setting more effective goals. We conducted a preregistered randomized controlled experiment ($N = 543$) comparing chatbots with different combinations of three design features: guidance, suggestions, and feedback. We evaluated goal quality using subjective and objective measures. We found that, while guidance is already helpful, it is the addition of feedback that makes LLM-based chatbots effective in supporting participants' goal setting. In contrast, adaptive suggestions were less effective. Altogether, our study shows how to design chatbots by operationalizing psychological frameworks to provide effective support for reaching behavioral goals. |
| title | Supporting Effective Goal Setting with LLM-Based Chatbots |
| topic | Human-Computer Interaction |
| url | https://arxiv.org/abs/2602.08636 |