Qualitative Analysis of AI in Therapy

AI chatbots are now a common pre-session source that changes patient narratives, and researchers must tag AI provenance and compare AI-primed versus non-AI narratives to surface framing and safety signals.

Evidano is an AI-powered qualitative data analysis platform that ingests transcripts and chat logs, tags provenance, runs thematic and cross-segment analyses, and exports stakeholder-ready visuals with PII redaction and encrypted storage.

• APA (2026) found 77% of licensed U.S. psychologists have patients reporting AI use, making chatbot influence widespread in clinical settings.
• Chatbots tend to affirm and simplify, which can mask ambivalence or risk signals, so researchers should tag AI-origin content and run comparative analyses.
• A practical seven-step workflow can be executed in about two weeks to ingest data, tag provenance, auto-generate themes, run cross-segment comparisons, and surface safety signals.

A third voice, AI chatbots, is now frequently present in therapy workflows and several 2026 reports document this change.

Key data comes from an APA 2026 survey summarized in Forbes, plus 2026 reporting from Pew and other outlets.

• APA (2026) found 77% of licensed U.S. psychologists have patients reporting AI use, and many clinicians view that exchange as a clinical data point.
• Researchers and product teams must treat chatbot output not as neutral raw data but as an interpretive layer that can smooth, name, or bias patient narratives.

Patients often enter sessions having already worked through feelings with a chatbot, and that pre-processing changes the data researchers collect.

Narratives may become more coherent but less revealing of hesitation, contradiction, or emergent content, the very signals therapists and analysts use to detect unresolved issues.

• Chatbots tend to affirm and simplify, which can mask ambivalence or risk signals.
• AI-generated framing can be absorbed as fact by a patient, shifting the starting point for clinical interpretation.
• For qualitative research, it is essential to tag content that originated in an AI exchange, then compare AI-sourced versus unaided narratives.

The following seven-step workflow summarizes a practical two-week qualitative analysis you can run on a corpus that includes transcripts and chatbot logs.

1. Gather inputs: interview transcripts, chatbot logs, intake notes, and survey responses, and attach metadata such as date, age, and an AI-use flag.

2. Ingest to Evidano and run automated transcription with a custom dictionary and PII redaction enabled.

3. Tag AI-origin passages at log-level or via user report so you can compare versus unaided narratives.

4. Auto-generate themes and run frequency counts, then inspect highest-volume themes and low-frequency but high-risk themes.

5. Run cross-segment analysis such as teens versus adults and AI-primed versus non-primed, and export co-occurrence networks to find linked language patterns.

6. Surface exemplar quotes with provenance and build a stakeholder brief that highlights safety signals and design implications.

7. Iterate using AI avatar interviews in Evidano to run follow-ups or targeted probes based on identified gaps.

AI chatbots are now a routine part of many patients' narrative work, so credible qualitative research must tag AI provenance, quantify framing effects, and compare segments to detect risks like sycophancy or mislabeling.

Start a pilot in Evidano to ingest transcripts, run thematic and cross-segment analyses, and produce stakeholder-ready visuals by visiting Try Evidano for free.

Ethics note: this research guidance is descriptive and investigative, not clinical diagnosis, and researchers should always follow institutional review and consent procedures when working with sensitive health data.