Qualitative approach: You can use Qualia any way you want, but we prefer a conversational approach which has just a few loosely-defined high-level research questions, and leaves Qualia to manage the discussion. This way, the interviewer approaches the stakeholders and their stories as far as possible without preconceived templates, to remain open to emerging and unexpected changes in respondents’ causal landscapes.

Scalability and reach: The AI’s ability to communicate in many languages and even to switch languages presents an opportunity to reach more places and people, subject to internet access and the AI’s fluency in less common languages, and to include representative samples of populations.

Consistency: The interview and coding processes are machine-driven and use zero temperature. An AI interviewer can be made to be more consistent than any individual human could manage, let alone a team of humans. This means the interview behaviour should be mostly reproducible. Reproducibility opens the possibility of comparing results across groups, places and timepoints.

Reporting: The Qualia platform has its own automated reporting. This helps you analyze your interview transcripts using AI to generate different types of insights, from identifying typical "Tribes" within your respondents to generating social network and Power-Influence diagrams.

Qualitative causality: We like to use Qualia to help researchers and evaluators answer evaluation questions which are often causal in nature, like: understanding stakeholders' mental models; judging whether "their" ToC matches "ours"; investigating “how things work” for different subgroups of stakeholders; tracing impact from mentions of "our" intervention to outcomes of interest; triaging the key outcomes in stakeholders’ perspectives.

When Qualia uses a causal focus, we can using causal mapping (and our own Causal Map app) to rapidly make sense of stakeholder world views.