# AI-Facilitated Episodic Future Thinking For Adults with Obesity

arXiv: https://arxiv.org/abs/2503.16484 · HTML: https://mlsanity.com/paper/2503.16484/

## Key facts

- No citing papers in the map yet (data through 14 Oct 2026).
- Placed in Clinical and biomedical applications, one of 17 directions in LLM reasoning.
- Builds on 7 of its 76 references that are in the map.
- First posted on arXiv 8 Mar 2025.

## Placed in

- LLM reasoning / [Clinical and biomedical applications](https://mlsanity.com/lr/clinical-and-biomedical-applications/)

## Builds on (most cited first, 7 of 7)

- [GPT-4 Technical Report](https://mlsanity.com/paper/2303.08774/) (2023)
- [MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiences](https://mlsanity.com/paper/2409.09570/) (2024)
- [Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape App](https://mlsanity.com/paper/2404.00487/) (2024)
- [Jigsaw: Supporting Designers to Prototype Multimodal Applications by Chaining AI Foundation Models](https://mlsanity.com/paper/2310.08574/) (2023)
- [Coaching Copilot: Blended Form of an LLM-Powered Chatbot and a Human Coach to Effectively Support Self-Reflection for Leadership Growth](https://mlsanity.com/paper/2405.15250/) (2024)
- [Can a Funny Chatbot Make a Difference? Infusing Humor into Conversational Agent for Behavioral Intervention](https://mlsanity.com/paper/2403.00365/) (2024)
- [AI Chatbot for Generating Episodic Future Thinking (EFT) Cue Texts for Health](https://mlsanity.com/paper/2311.06300/) (2023)

## Abstract (arXiv)

Episodic Future Thinking (EFT) involves vividly imagining personal future events and experiences in detail. It has shown promise as an intervention to reduce delay discounting-the tendency to devalue delayed rewards in favor of immediate gratification- and to promote behavior change in a range of maladaptive health behaviors. We present EFTeacher, an AI chatbot powered by the GPT-4-Turbo large language model, designed to generate EFT cues for users with lifestyle-related conditions. To evaluate the feasibility and usability of EFTeacher, we conducted a mixed-methods study that included usability assessments, user evaluations based on content characteristics questionnaires, and semi-structured interviews. Qualitative findings indicate that participants perceived EFTeacher as communicative and supportive through an engaging dialogue. The chatbot facilitated imaginative thinking and reflection on future goals. Participants appreciated its adaptability and personalization features, though some noted challenges such as repetitive dialogue and verbose responses. Our findings underscore the potential of large language model-based chatbots in EFT interventions targeting maladaptive health behaviors.