Qualitative Analysis: Gen Z AI Attitudes

Evidano is an AI-powered qualitative data analysis platform that helps teams map Gen Z's mixed AI use and skepticism by analyzing speeches, interviews, social posts, and usage data.

A reproducible workflow that integrates mixed-format sources and cross-segment comparisons reveals where utility and concern coexist and supports hiring, onboarding, and product recommendations.

• The Boston Globe reported on June 21, 2026 that many Gen Zers are frequent generative-AI users and vocal critics, a split that requires nuanced analysis.
• Run a two-week pilot: ingest a mixed corpus, transcribe and auto-code, then run cross-segment matrices to surface ambivalence and actionable themes.
• Use Evidano features for transcription with PII redaction, cross-segment matrices, clickable quotes, co-occurrence networks, and encrypted storage to protect sensitive data.
• Collect 20–50 interviews and 500–2, 000 social comments for a pilot, per the workflow steps in this post.

The Boston Globe reported on June 21, 2026 that many recent graduates are both savvy generative-AI users and vocal opponents of its social and privacy effects.

The article quotes students and experts describing employer demand for AI fluency as a driving pressure (see source: Boston Globe).

• Why it matters: Employers increasingly list AI fluency as a hiring requirement; researchers need to map attitudes, usage patterns, and trade-offs across cohorts.
• What you’ll get here: a research-ready workflow and concrete Evidano features to analyze transcripts, social posts, and surveys for theme, frequency, and cross-segment signals.

The Globe's reporting frames a common, research-friendly paradox: usage does not equal endorsement.

For teams studying attitudes, single metrics such as use frequency will mislead, so integrate what people say (attitudinal interviews and speeches) with how people act (usage logs or self-reported behaviors) and where people voice concerns (social posts and forums).

• Sampling: prioritize mixed-mode sources, commencement speeches, campus interviews, Reddit/TikTok comments, and employer job listings.
• Coding goal: surface conflicting themes, such as practical utility versus ethical concern, and map those themes by segment (students by major, recent grads versus job seekers).
• Outcome: actionable recommendations for hiring, onboarding, and product messaging that respect ambivalence and signal trust.

This seven-step workflow reproduces the insights in a two-week pilot.

• 1) Collect: pull commencement speeches, 20–50 interviews, and 500–2, 000 social comments using Evidano's website and social scraping.
• 2) Clean & transcribe: run Evidano transcription with custom dictionary and PII redaction settings.
• 3) Initial auto-coding: generate themes and frequency tables, then import or refine a codebook.
• 4) Cross-segment analysis: compare themes across segments (major, job status, geography) to spot loyalty or backlash patterns.
• 5) Visualize: export co-occurrence networks, hierarchy charts, and representative quotes.
• 6) Validate: run targeted AI-avatar follow-ups for ambiguous clusters or to test interpretive hypotheses.
• 7) Report: produce an executive brief and stakeholder slide pack directly from Evidano exports.

Gen Z's split between heavy AI use and vocal skepticism, reported by the Boston Globe, is an analytic opportunity to map where utility and concern coexist.

• Next step: run a two-week pilot by ingesting a mixed corpus and running a cross-segment thematic analysis in Evidano.
• Get started: Try Evidano for free and use the pilot checklist above to deliver stakeholder-ready insight quickly.