ai-interviewer-des-phenomenology

name	ai-interviewer-des-phenomenology
description	AI interviewer methodology for operationalizing Descriptive Experience Sampling (DES) into an explicit, inspectable reasoning architecture. Grounded in the established phenomenological method with co-development by DES originator Russell T. Hurlburt. Use when implementing AI systems for studying inner experience at scale, conducting qualitative interviews with temporal grounding, or developing LLM-based research platforms for subjective experience sampling.
metadata	{"arxiv_id":"2607.20310","authors":"Jona Carmon, Clara Bersch, Charles Fernyhough, Russell T. Hurlburt, Simone Kühn","published":"2026-07-22","title":"Capturing Inner Experience At Scale: An AI Interviewer Co-Developed with the Founder of a Landmark Phenomenological Method","tags":["descriptive-experience-sampling","phenomenological-method","ai-interviewer","inner-experience","llm-interviewing","temporal-grounding","qualitative-research","psychological-science"]}
license	Complete terms in LICENSE.txt

AI Interviewer for Descriptive Experience Sampling (DES)

This skill provides the methodology for an AI interviewer that operationalizes Descriptive Experience Sampling (DES) into an explicit, inspectable reasoning architecture. It represents the first AI system grounded in an established method for studying inner experience, co-developed with DES originator Russell T. Hurlburt.

Core Architecture

The AI interviewer implements a structured reasoning process at each turn:

1. Quality Appraisal: Evaluates participant messages across eleven quality dimensions
2. Conservative Accounting: Maintains a careful record of what has been established
3. Stage-Appropriate Intervention: Selects interventions based on interview progression
4. Non-Leading Query Composition: Generates single, non-leading questions
5. Temporal Grounding Priority: Always ensures temporal grounding precedes experiential content

Implementation Context

• Derived from the full corpus of DES transcripts
• Refined through collaboration with method originator Russell T. Hurlburt
• Runs inside Introscope application platform
• Supports study delivery via shareable links
• Enables researcher review of sampled experience

When to Use This Skill

• Implementing AI systems for studying subjective experience at scale
• Developing LLM-based qualitative interviewing platforms
• Creating research tools that require temporal grounding before experiential content
• Building applications that need to balance depth and scale in experience sampling
• Operationalizing established phenomenological methods in AI systems

Key Concepts

• Descriptive Experience Sampling (DES): In-depth investigation of specific moments through expert expositional interviews
• Temporal Grounding: Establishing when an experience occurred before exploring its content
• Eleven Quality Dimensions: Framework for appraising participant responses
• Conservative Account: Method for tracking established facts without overinterpretation
• Non-Leading Queries: Questions designed to avoid suggesting answers or interpretations

Activation Keywords

• descriptive experience sampling
• phenomenological method
• ai interviewer
• inner experience
• llm interviewing
• temporal grounding
• qualitative research
• psychological science