AInterviewer: A Platform for Designing and Conducting AI-led Qualitative Interviews

Researchers introduce AInterviewer, an open-source platform that combines large language models with traditional survey software to conduct automated qualitative interviews while maintaining data security and reproducibility. Unlike proprietary solutions, the system runs on locally hosted models and enforces standardized question administration, addressing concerns about privacy and scientific rigor in AI-driven research.

AInterviewer represents a meaningful shift in how artificial intelligence can be applied to academic research infrastructure. The platform addresses a genuine friction point: while LLMs have demonstrated capability in conducting interviews, existing solutions lock users into proprietary systems that create data security liabilities and compromise scientific reproducibility—critical concerns for institutional research. By combining a multi-agent pipeline architecture with traditional survey controls, the platform enables researchers to maintain strict experimental rigor while leveraging LLM flexibility for nuanced follow-up questioning.

The open-source approach signals growing recognition that enterprise AI tools often fail to meet the requirements of academic institutions and privacy-sensitive organizations. Locally hosted model support removes dependency on third-party APIs, directly addressing GDPR compliance and institutional data governance policies that many universities face. This addresses a broader trend where general-purpose AI platforms prove inadequate for specialized domains requiring both flexibility and control.

For the broader AI infrastructure ecosystem, this demonstrates market demand for modular AI systems that combine parametric tools (traditional survey software) with generative capabilities. Developers building domain-specific AI applications increasingly recognize that pure LLM solutions oversimplify complex workflows. The web-based GUI supporting the entire research lifecycle—from design through monitoring—shows how thoughtful UX design can make AI systems accessible to non-technical researchers.

The platform's emergence highlights emerging opportunities in vertical AI applications, particularly where reproducibility and data governance demand local control. As institutional adoption of AI accelerates, demand for tools that balance capability with compliance will grow.

• →Open-source approach eliminates proprietary LLM dependency, enabling local model hosting for enhanced data security and reproducibility
• →Multi-agent architecture combines controlled question administration with LLM flexibility, maintaining research standardization while enabling conversational depth
• →Web-based interface supporting full research lifecycle suggests growing market for specialized AI tools addressing domain-specific workflows
• →Platform addresses institutional data governance requirements that general-purpose AI solutions fail to meet
• →Design demonstrates demand for modular AI systems balancing parametric controls with generative capabilities