Implementation Details
As a proof-of-concept test, we developed an opt-in adaptive survey system integrating Qualtrics with the OpenAI API, specifically using gpt-4o-2024-11-20 at temperature 0.8 after testing to balance conversational naturalness with response consistency. The prompts include structured instructions governing tone, pacing, and content focus, specifically directing the chatbot to explore stress sources, coping mechanisms, and the extent to which stress interferes with daily life. The architecture employed a three-prompt rotation system with preserved chat history using a sliding window approach to maintain conversational continuity, JSON-structured outputs with signal tokens ("next", "end", or null) for flow control, and comprehensive JavaScript-based fallback mechanisms for API failures, malformed responses, network errors, and participant exit requests.
Participants were able to opt for the regular survey from the start or during the chat-based survey using command-based controls, allowing users to type "SKIP" to bypass questions or "STOP" to immediately terminate the AI interaction and proceed to traditional survey sections. We implemented frequent conversation state saves and detailed error categorization to distinguish between specific failure types (network failures, JSON parsing errors, API timeouts). All chatbot interactions are recorded as embedded data within the Qualtrics platform, ensuring seamless integration with existing survey analytics.
Test Objectives
This pilot implementation was designed to evaluate participant responses to conversational survey formats, measure opt-in rates for AI-enhanced surveys, quantify technical reliability through error tracking, and identify user experience friction points for future optimization. For this first test, we focused on a structured stress assessment. Following the chatbot interaction, participants completed a brief set of survey questions to share their reflections on the experience, providing essential feedback to guide the design and development of future surveys.
Data Handling and Privacy Framework
All conversational data is recorded as embedded data within the Qualtrics platform, ensuring integration with existing research data management protocols while maintaining participant privacy. The explicit purpose of incorporating AI-enabled chatbots is outcome measurement for the current study and exploration of survey methodology improvements, not AI model development or training.
Informed Consent and Transparency Framework
Effective implementation requires comprehensive participant consent that clearly explains the AI-enhanced survey experience while maintaining transparency about data handling and technical limitations. Our consent framework addresses several critical components: clear differentiation between chat-style and traditional survey formats, explicit explanation of AI involvement and data flow, voluntary participation with multiple exit options, and acknowledgment of potential technical issues with feedback mechanisms.
Recommended disclosure elements to set expectations include explaining that the AI model may reference earlier survey responses to personalize conversation flow and the retention window for security monitoring and what information is shared with model providers through the API, as relevant. The framework also acknowledges the experimental nature of the approach while encouraging feedback on technical issues or confusing interactions to improve future implementations.
Technical Foundation and Implementation Recommendations
Our pilot implementation used multi-layer fallbacks, comprehensive error logging, and prompt refinement to build a technically robust system. We want to understand the potential for adaptive surveys to maintain the standardization required for research validity while potentially improving participant experience and data richness.
Based on our experience so far (data collection is ongoing), we recommend several design principles for researchers implementing adaptive surveys. JSON-structured output is recommended for reliable flow control, with consistent fields enabling models to make progression decisions, though robust fallback mechanisms are necessary for occasional malformed responses that can occur even with careful prompting. The key opportunity lies in language models' ability to explore new conversational territory while maintaining focus—balancing hard requirements with flexibility is crucial for survey success.
For prompt engineering, we found success in maintaining consistent tone and flow with clear goals and example questions while avoiding extraneous instructions that could limit the LLM's natural flexibility and human-like interaction capabilities. Future implementations will explore more flexible prompting approaches, prioritizing conversational adaptability across domains over rigid constraints once analysis requirements and methodological considerations have been fully evaluated. This technical foundation provides a robust starting point for expanding adaptive survey methodologies across diverse research domains.
Initial Test Design Decisions: What we did
API and Model Selection
• OpenAI GPT-4o selection: Chosen after comparative testing specifically for conversational tone quality, cost efficiency, multilingual support capabilities, and reliable JSON adherence (total cost to date <$200, using gpt-4o-2024-11-20)
• Temperature 0.8: High temperature setting selected to maximize conversational flexibility in concert with careful prompt engineering and domain constraints to maintain survey focus.
• Token management: Implemented conversation length limits using a sliding window approach to balance preserving important chat history with cost control and response speed, preventing conversations from becoming too expensive or slow. This approach keeps responses fast and cost-effective while maintaining conversation continuity.
• Design and Data Quality
Platform Integration:
• Qualtrics integration approach: Direct API calls from Qualtrics JavaScript, enabling a quick and effective test within the survey platform participants were already familiar with.
Reliability:
• Comprehensive data storage and recovery: Multi-field embedded data storage within Qualtrics platform with automatic chunking for long conversations, preserving chat history, stressor tracking, conversation flow position, and turn counting. Includes frequent state saves and automatic restoration on page reloads to prevent data loss or interruptions to the participant experience, enable recovery from technical failures, and ensure seamless integration with survey analytics and data management protocols.
• Comprehensive error tracking: Systematic categorization and handling of technical failures (network errors, malformed JSON, API issues) with multi-layer fallback mechanisms to preserve data integrity and route participants to traditional survey flow when needed.
• JSON-structured outputs and validation: Standardized response format with specific fields ("reply", "signal") enabling reliable parsing and flow control, supported by robust extraction functions and multiple fallback mechanisms for malformed responses.
Conversation Management and Participant Experience:
• Prompt design and rotation: Three-prompt rotation strategy, balancing reasonable length and flexibility with examples and clear completion criteria. The prompts cover stress sources, responses, and impacts. Each includes instructions for tone, pacing, and content focus while avoiding unnecessary directions, preserving LLM flexibility and natural interaction quality. Shared conversational elements maintain consistency across all prompts.
All prompts specify role definition, interaction boundaries, and engagement monitoring. For example: "You are an AI assistant embedded in an adaptive survey. Stay participant-led and non-linear. Ask one question per turn. Never combine topics. Mirror the participant’s words. Avoid advice or clinical labels. Validate effort where appropriate (e.g., 'That sounds like a lot to carry'). Rephrase a skipped probe once, then respect the boundary. Pivot when answers shorten or repeat." The first prompt then incorporates a probing framework—domain, specificity, underlying causes, timeframe, trajectory, and severity—for stressors, combining example questions with flexibility to ensure comprehensive coverage. Completion criteria establish when exploration is sufficient, supported by monitoring for participant engagement and fatigue signals. Additional design elements include allowing natural tangents with transitions back to survey goals, defining graceful transitions and exit conditions, and specifying structured JSON outputs and signaling to ensure smooth interaction and consistent data capture.
• Voluntary participation and control mechanisms: Opt-in design with explicit choice between traditional and AI-enhanced formats, command-based controls allowing "SKIP" to bypass questions and "STOP" for immediate termination, and JavaScript-level safety mechanisms for immediate participant exit.
• Post-interaction reflection assessment: Structured questions capturing participant perceptions and comparisons to traditional survey experiences.
• Future Considerations
Conversational Approach and Experience:
• Adaptive prompting and interaction systems: Develop context-aware prompt adaptation that adjusts conversation style, depth, and approach based on participant responses, engagement patterns, and conversation effectiveness. Include hybrid data collection that strategically combines open-ended conversational responses with structured questions (multiple choice, rating scales) based on participant preferences and research needs, with expanded integration of speech-to-text and text-to-speech for accessibility and alternative interaction modalities. Enhanced command structures and control mechanisms that balance participant autonomy with research data collection needs, moving beyond fixed structures toward flexible, participant-responsive interactions.
• Evolved context management: Test implementing summarization techniques to preserve essential conversation context while optimizing token usage and API costs, enabling longer, more naturalistic conversations without performance degradation.
• Expanded model comparison framework: Building on initial selection criteria, systematic evaluation of existing models and future releases for survey-specific performance metrics.
Open Research Questions
Research Ethics and Implementation:
• Optimal context window management: What is the optimal amount of chat history to retain in order to preserve conversational continuity without overloading the context window or introducing bias?
• Generalizability across survey types: Which elements of this approach transfer well to different research domains and survey objectives?
• Long-term participant effects: How does extended AI interaction in surveys impact response quality, fatigue, and completion rates compared to traditional methods?
• Conversation quality measurement: What metrics best capture the effectiveness of AI-enhanced surveys beyond completion rates and basic satisfaction scores?
• Scalability considerations: How do costs, latency, and reliability change as this approach scales to larger participant populations and more complex survey designs?
• Data retention and compliance: How should conversational survey data be stored, processed, and shared while maintaining participant privacy and aligning with research ethics frameworks, IRB requirements, and data protection regulations?
• Informed consent for AI interaction: What specific consent language and procedures should be standard when participants engage with AI systems in research contexts? (Example framework: explicit AI disclosure, voluntary participation, multiple exit options, technical transparency about data flow)
• Cross-survey data integration: How should researchers handle AI personalization that references earlier survey responses?
• Transparency and disclosure standards: What level of detail about prompt engineering, model configuration, conversation management, and potential technical limitations should be disclosed to participants and IRB for ethical compliance and maintaining participant confidence?
• Bias implications and mitigation: What are the long-term effects of AI-mediated data collection on research validity and participant representation, and what systematic approaches can identify and address potential biases?
• Implementation planning and readiness: What decision criteria and technical resources do researchers need to evaluate, design, and successfully deploy adaptive surveys for their specific research contexts and participant populations?
Following completion of data collection, we will share insights on analyzing conversational stress data and how adaptive survey responses compare to traditional survey instruments. We plan to share details on our technical approach to the next adaptive survey in terms of design, implementation, reasons for updates, new model considerations, and analytical frameworks for processing conversational survey data compared to traditional survey analysis methods.