AI-Facilitated Episodic Future Thinking For Adults with Obesity
On the map: LLM reasoning → Clinical and biomedical applications
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.
Key facts. No citing papers in the map yet (data through 7 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.
Builds on
7 of 76 references.
References · 7
- 2023GPT-4 Technical Reportcited by 18,132
- 2024MindScape Study: Integrating LLM and Behavioral Sensing for Personalized AI-Driven Journaling Experiencescited by 24
- 2024Contextual AI Journaling: Integrating LLM and Time Series Behavioral Sensing Technology to Promote Self-Reflection and Well-being using the MindScape Appcited by 14
- 2023Jigsaw: Supporting Designers to Prototype Multimodal Applications by Chaining AI Foundation Modelscited by 11
- 2024Coaching Copilot: Blended Form of an LLM-Powered Chatbot and a Human Coach to Effectively Support Self-Reflection for Leadership Growthcited by 7
- 2024Can a Funny Chatbot Make a Difference? Infusing Humor into Conversational Agent for Behavioral Interventioncited by 4
- 2023AI Chatbot for Generating Episodic Future Thinking (EFT) Cue Texts for Healthcited by 1
Built on by
Not cited yet.
Similar, not cited
Close in wording, no citation either way
- 2024Habit Coach: Customising RAG-based chatbots to support behavior change
- 2025MindEval: Benchmarking Language Models on Multi-turn Mental Health Support
- 2025CGM-Led Multimodal Tracking with Chatbot Support: An Autoethnography in Sub-Health ↗
- 2024VCounselor: A Psychological Intervention Chat Agent Based on a Knowledge-Enhanced Large Language Model
- 2026SynthAgent: A Multi-Agent LLM Framework for Realistic Patient Simulation -- A Case Study in Obesity with Mental Health Comorbidities
- 2023An Integrative Survey on Mental Health Conversational Agents to Bridge Computer Science and Medical Perspectives
- 2026Never say never: Exploring the effects of available knowledge on agent persuasiveness in controlled physiotherapy motivation dialogues
- 2026When Workout Buddies Are Virtual: AI Agents and Human Peers in a Longitudinal Physical Activity Study