AI Qualitative Research Software with 90%+ Validated Accuracy
Independent university researchers and UN evaluation offices put AILYZE software to the test. The result: AI-powered coding that matched human experts, while cutting analysis time by 95%.
Explore the Case Studies
AI-Powered Coding of Elementary Students' Small-Group Discussions about Text
Firetto, C. M., Murphy, P. K., Yan, L., & Tang, Y. (2025)
Independent researchers from Arizona State University and Penn State University conducted a rigorous validation study comparing AILYZE's AI-powered qualitative coding against trained human experts. The study analyzed classroom discussion transcripts, coding for complex argumentative and collaborative reasoning patterns in elementary students' conversations about literary texts.
Large-scale validation dataset
Inter-rater reliability (Cohen's Kappa)
12 hours vs. nearly a year
Average processing time
“Findings provide evidence that AI may serve to accurately code discussion transcripts in ways that were not previously feasible with only human-produced coding.”— Firetto et al., Arizona State University & Penn State University
What the Researchers Found
“AILYZE processed all 371 transcripts within ~12 hours (i.e., 2 minutes per transcript), clearly illustrating the potential to save time.”
vs. nearly an academic year for human coders
“Once the AI prompt was refined with human review, it achieved strong alignment with human codes.”
Substantial to near-perfect agreement
“A further benefit of using AILYZE was its capacity to generate explanations and justifications for coding decisions.”
Every code comes with a rationale
“We selected AILYZE in part because of its security features, auditability, and policy not to train on user data.”
IRB-compliant, HECVAT-certified
Complex Patterns AI Accurately Identified
Complex Reasoning
Claims supported by evidence
“I think the character was brave because she went past her limits to help someone else. That's what heroes do—they take risks for others.”
Nuanced Dialogue
Meaning built across speakers
A: “I think it's realistic.”
B: “But that part couldn't actually happen.”
A: “Well, maybe in certain situations it could...”
“AI systems apply the same coding criteria uniformly across large corpora without a decline in performance over time.”
“AI augmented, not replaced, expert judgment.”— From the peer-reviewed study
RESEARCH INSTITUTIONS
Arizona State University • Penn State University
FUNDING
IES (R305A130031) • NSF (1912415)
Don't get left behind!
Get free license (first 200)
Enter email and use the same AI that achieved 90%+ accuracy in peer-reviewed university research and powered one of the UN's largest meta-synthesis.