Qualitative Analysis of Admissions: Design Thinking

UX and qualitative researchers should digitize and standardize DT artifacts to create an analyzable corpus.

DT workshops produce dense, multi-format artifacts including handwritten notes, charts, and votes, so turn these into transcripts, images, and CSVs.

Use inductive coding with double-coding and percent-agreement checks, the UNC study reports 90.5% agreement, to ensure trustworthiness and reproducibility.

Admissions directors should capture reproducible evidence for later evaluation and pilot testing.

Prioritize reproducibility by capturing raw prompts, vote counts, and constraints (cost, geography, equity) so later evaluation ties back to prototype rationale.

Track candidate-facing policy ideas, for example real-time writing stations or GenAI critique tasks, as discrete interventions to pilot and measure.

Ethics and limitations require proactive assessment before operational pilots.

This study is exploratory and non-diagnostic; any operational pilots should include equity impact assessment and ADA and legal review before deployment.

Evidano is an AI-powered qualitative data analysis platform that ingests multimodal DT artifacts, supports built-in transcription and PII redaction, and unifies noisy text into a single analyzable corpus.

Upload photos of chart paper, handwritten notes, and typed worksheets and use built-in transcription with a custom dictionary to convert noisy text into a single corpus for analysis.

Solution: Seed your codebook and run AI-assisted coding to standardize application and measure agreement.

Import your codebook or let Evidano generate a seed codebook from samples, run AI-assisted coding, and export percent-agreement and per-code frequencies to reproduce the study’s 90.5% check quickly.

Solution: Use cross-segment analytics to reveal stakeholder differences at a glance.

Produce thematic frequency tables, co-occurrence networks, and hierarchical code to subcode visualizations so differences (faculty versus students) are visible quickly.

Solution: Generate auditable, exportable reports that link quotes to original artifacts for member-checking and IRB-ready documentation.

Generate shareable summaries and export CSVs of quotes linked to original artifacts for member-checking and documentation; Evidano does not train third-party models and stores data encrypted.

Solution: Run scalable follow-ups without hiring additional interviewers.

Run autonomous avatar interviews to collect participant feedback or run pilot probes on prototype stations at scale.

A: Detection is noisy and not definitive.

The UNC workshop suggested real-time tasks (for example on-stage writing) to reduce GenAI use, and for archival materials combine stylometrics with manual review and policy disclosures.

A: Capture vote counts as structured fields and run weighted-priority analyses.

Capture vote counts as structured fields and run frequency plus weighted-priority analyses to show which ideas had traction across stakeholder groups.

A: Use platforms with end-to-end encryption and clear model-training policies.

Choose platforms with end-to-end encryption and explicit policies on model training; Evidano stores data encrypted and does not use customer data to train third-party models.