
Why Neuro-Symbolic Approaches Lead to Better AI Probing
By inca
- article
- Conversational AI
- Verbatim Response Coding
- Qualitative Research
- Generative AI
- Artificial Intelligence
- AI Interviews
- AI Moderation
- Survey Research
- Agile Quantitative Research
- Chatbots
A version of this post first appeared on the Nexx Intelligence – inca blog.
Understanding Conversational AI in Market Research
There are now several Conversational AI products for market research, variously known as ‘Qual At Scale’ or ‘Qualitative Surveys.’ It might seem straightforward to create such an AI product, as it involves using large language models (LLMs) like GPT to generate probing questions. However, LLMs are predictive engines trained on extensive internet data and may exhibit issues such as biases and hallucinations.
They may produce probing questions that are inappropriate or do not align well with market research principles, highlighting challenges in relying solely on their output.
In cognitive terms, LLMs operate as ‘System One,’ characterized by reflexive and instinctive responses that sometimes lack appropriateness. This requires a more balanced approach to ensure that questions are both insightful and ethical.
Leveraging Neuro-symbolic AI
inca SmartProbe employs an approach that integrates Neuro-symbolic AI, which enhances GPT’s capabilities by incorporating both intuitive System 1 outputs and the more deliberate reasoning of System 2.
This method combines neural and symbolic AI to improve reasoning, learning, and cognitive modeling. Such integration is essential for developing AI systems capable of effectively interacting with humans and soliciting meaningful insights.
Practical Applications and Benefits
The use of Neuro-symbolic AI helps to achieve several key outcomes:
Quality Assurance:
The inca SmartProbe uses in-depth training from market researchers to ensure the probing questions adhere to established market research principles. It operates by generating multiple candidate probes for each open-ended response, drawing from a comprehensive knowledge base of best practices.
An extensive classification system meticulously filters out undesirable question types categorized as leading, unethical, or unprofessional. This dual-layer approach—bridging the creative outputs of intuitive System 1 with the structured reasoning of System 2—ensures that the probes maintain both efficacy and ethical standards in real-time.
Customization and Interpretability:
Researchers can tailor inca’s AI probing process and enhance conversations by embedding relevant research contexts. Just as a human moderator would be briefed in qualitative settings, researchers guide the AI to focus on specific themes and objectives. Researchers can incorporate ideal question examples, allowing the trained models to emulate the desired questioning style and type.
Conversational targets provide structure to the design and analysis of AI-generated conversations. These targets can double as quantitative variables, enabling researchers to track the prevalence of key terms in participant responses and use this data for in-depth analysis. Custom probing frameworks are available to accommodate specific client needs, ensuring that the generated inquiries and collected data align with diverse research objectives.
Gathering Actionable Insights:
The core of inca SmartProbe’s design is the elicitation of structured, relevant information that drives actionable insights and understanding humans at scale. Conversational AI principles used in inca SmartProbe enhance the ability of researchers to extract nuanced and rich information from interactions. Through the use of conversational targets, researchers can quantitatively measure the usage of specific terms.
inca AI Coding complements these efforts by automating the coding of verbatim data, which researchers can then edit and optimize. This approach not only automates a previously time-consuming task but allows researchers to focus on higher-level analysis. Custom probing frameworks extend these capabilities, helping to capture and analyze information pertinent to clients’ specific methodological needs, be it in understanding emotional associations or extracting concrete product details.
A final note
The integration of Neuro-symbolic AI in market research through tools like inca SmartProbe represents a transformative approach that balances the instinctive outputs of large language models with deliberate reasoning. This blend ensures that probing questions remain both insightful and ethically sound, aligning with established market research principles.
Researchers can guide the AI to focus on specific themes, enhancing the quality of interactions and facilitating the gathering of actionable insights. The automation of coding and custom probing frameworks streamlines the research process, thus allowing for deeper analysis and a better understanding of human responses at scale.