AI Qualitative Analysis of Digital Childhood

UX and product researchers should not treat screen-time as a single metric.

Researchers should code interactions where screens trigger offline making, role-play, or cross-device routines to surface design opportunities such as co-viewing modes and sibling-aware profiles.

Researchers should look for transition triggers in transcripts and video, for example countdown timers or Bluetooth microphones, and tag them as interaction affordances in the codebook.

Policy and early-years researchers should not rely on minutes alone to understand media use.

Policy teams should translate parent narratives into actionable levers, for example community spaces, evidence-based guidance at childcare centres, and targeted support for multilingual families.

Policy prioritisation should use qualitative cross-segment analysis across single parents, immigrant households, and sibling-gap households to identify infrastructure deficits such as affordable indoor play spaces.

Qualitative teams scaling insights should capture multimodal data and link units of analysis.

Teams should capture audio transcripts, short home videos, and parental surveys and link units of analysis (child, household, session) to run cross-segment comparisons such as immigrant versus non-immigrant households.

Teams should validate child-centered inferences by triangulating child play transcripts with parental explanations and short video clips.

Multimodal inputs create fragmentation, researchers juggle transcripts, interview audio, parent surveys, and short home videos often in multiple languages.

Evidano ingests transcripts, interview audio, video captions, and survey CSVs into one workspace for consistent analysis.

Evidano provides transcription with custom dictionaries and PII redaction and translation so multilingual clips (Korean, Chinese, English) are analyzed consistently.

Evidano encrypts all data and never uses research data to train third-party models, a critical safeguard for child-related research.

Hand-coding hundreds of clips and transcripts creates a bottleneck, manual coding is slow and inconsistent across coders.

Evidano auto-generates themes, frequency counts, and co-occurrence networks from transcripts, then supports human-in-the-loop codebook import.

Evidano enables cross-segment comparisons in a click, for example households with or without older siblings and immigrant versus non-immigrant households.

Evidano provides an AI chat over documents to surface child-perspective quotes, extract transition triggers, or compile a stakeholder-ready evidence brief.

Stakeholder handoffs fail when outputs are not concise and verifiable.

Evidano produces word clouds, hierarchical theme to subcode trees, and co-occurrence networks that make central claims visible and defensible for stakeholders.

Evidano helps stakeholders see study claims such as the learning-gates-play loop and sibling spillover through shareable visuals and reports.

The study found that screens often act as catalysts for offline play, with app flows gating access to fun and sibling spillover and multilingual households shaping media diets.

The study observed screens feeding into crafts, acting, and co-play, showing that counting minutes alone misses how screens become materials for play.

The study used play-based group sessions, individual parent interviews, and short home video clips, with a sample of 19 children ages 3–5 across four early childhood centres in 2024.

The primary qualitative dataset included play sessions, parent interviews, and short home videos that researchers used to observe practice rather than produce diagnostic measures.

The implications are that researchers should code for interaction types and transition triggers, and policy teams should use cross-segment qualitative analysis to identify infrastructure needs such as affordable indoor play spaces.

The study suggests translating parent narratives into community-focused policy levers and targeted support for multilingual families.

Teams can reproduce and scale these insights by capturing multimodal data, transcribing and translating, running AI-assisted auto-coding, and triangulating child transcripts with parental explanations and video clips.

The checklist in this post provides a seven-step workflow that includes importing a codebook, running AI-assisted analysis, refining codes with humans, and exporting stakeholder-ready outputs.