Qualitative Analysis of Chatbot Interactions

Evidano is an AI-powered qualitative data analysis platform that ingests transcripts, automates thematic coding, and secures sensitive participant data for reproducible research. Multiple 2026 papers converge on two practical signals: people are speaking to one another less, and rising chatbot use is associated with increases in emotional isolation months later. Use a reproducible, modality-aware qualitative workflow to compare spoken, typed, and AI-mediated interactions across time and cohorts.

• Reanalysis of audio-sampler data estimated ~338 fewer spoken words per person per year from 2005–2019 (22 studies reanalyzed).
• A four-wave, year-long survey of 2, 000 adults found rising chatbot use predicted higher emotional isolation about four months later.
• Practical workflow: ingest transcripts/audio, run automated transcription and PII redaction, apply a shared codebook, compare modality × cohort × loneliness scores, and pre-register analysis decisions.

Multiple 2026 papers converge on two practical signals: people are speaking to one another less and many are substituting chatbot interactions for social contact. Pfeifer reanalyzed audio-sampler data from 22 studies and estimated a steady decline of ~338 spoken words per person each year from 2005–2019, roughly 120, 000 fewer words per person per year. Separately, a four-wave, year-long survey of 2, 000 adults found that rising chatbot use predicted increases in emotional isolation months later, even when short-term interactions felt pleasant.

• The loss of spoken conversation is hypothesized to weaken trust-building cues such as tone and gestures, and continuous attention.
• Chatbot interactions give immediate positive affect but can reduce vulnerability and the emotional depth of human exchanges.
• Researchers recommend updating assessment instruments to measure online human-to-human and human-to-AI social reward.

This seven-step workflow runs a practical pilot for qualitative analysis of chatbot interactions. Step 1: Define research question, for example, does increased chatbot use predict rising emotional isolation over six months?

• Step 2: Gather inputs, collect recent chat transcripts, periodic surveys, and a one-week audio sample subset.
• Step 3: Import to Evidano, upload transcripts and audio, and run automated transcription and PII redaction.
• Step 4: Build a codebook that includes modality tags, vulnerability markers, and emotional-valence codes; import the codebook to Evidano.
• Step 5: Run thematic and frequency analyses, compare themes across modality and time, and run cross-segment tests (age, region, baseline loneliness).
• Step 6: Validate by sample-checking AI codes, adjust thresholds, and pre-register analysis decisions.
• Step 7: Report and iterate, export visualizations and stakeholder-ready quotes, and plan a second wave with AI-avatar follow-ups if needed.

The 2026 findings make a clear case: conversational habits and AI companions are reshaping social reward, and qualitative researchers must adapt tools and instruments accordingly. If your team needs to run rigorous qualitative analysis of chatbot interactions with reproducible coding, cross-segment tests, and secure handling of sensitive transcripts, start a pilot that mirrors the seven-step workflow above.

• Ready to try it? Try Evidano for free.
• For technical questions about setting up codebooks, PII redaction, or AI-avatar follow-ups, contact Evidano support from the dashboard.