What the study analysed
The study, published in the IEEE ACDSA 2026 proceedings, examined AI literacy, attitude toward AI, and perception of employment prospects among final-year undergraduates. A sequential mixed-methods approach ran qualitative interviews first, then a structured questionnaire measured the same three constructs on a six-point Likert scale.
Interviews opened with demographics and AI coursework, then moved to open-ended questions on experience with AI tools, perceived competence of those tools, applicability of AI knowledge in the job market, and optimism about employment prospects. All were audio recorded and transcribed verbatim.
Two methods, one model
The interview data went into Evidano for thematic interpretation. The survey data went into SmartPLS 4 for variance-based structural equation modelling, run as a pilot test of the conceptual model before the study expands to a larger sample.
The thematic analysis surfaced a pattern the quantitative model could then be read against: AI literacy shapes attitude toward AI, which in turn shapes how employment prospects are perceived. Respondents felt positive about AI enhancing their prospects while voicing uncertainty about job security.
How Evidano fit the workflow
A qualitative strand used to add richness to the numbers.
- 1Interviews recorded and transcribed verbatim
Open-ended questions covering AI experience, competence, applicability, and job-market outlook.
- 2Transcripts entered into Evidano
Thematic interpretation of the interview data.
- 3Survey modelled in SmartPLS 4
Measurement model checked for reliability and validity, with structural paths explored as a pilot.
- 4Strands triangulated
The qualitative themes were used as triangulation to add richness to the quantitative findings.
“The qualitative interview data were entered into AILYZE and were interpreted thematically, which was used as triangulation to add richness to the quantitative data.”
“The pilot test results show that the identified constructs exhibit reliability and validity, showing potential relationships on the path proposed in the conceptual framework, and the respondents felt positive about AI in enhancing employment prospects, yet expressed simultaneous uncertainty about job security.”
Mixed methods without the bottleneck
The qualitative strand is usually the one that slips, so mixed-methods designs often lean on the survey and treat interviews as colour.
Themes come back fast enough that the qualitative strand can actually triangulate the model rather than trail behind it.
Made for transcripts, focus groups, and open-ended responses.
Used alongside SmartPLS 4 in a sequential design, not in place of it.
The paper names the tool and the role it played.
Published by IEEE in the ACDSA 2026 proceedings, available on IEEE Xplore.
About Evidano (previously AILYZE)
Evidano is AI-assisted qualitative data analysis software for interviews, focus groups, open-ended survey responses, and documents. It supports AI-assisted thematic analysis with your own or an AI-suggested codebook, transcription and translation in 100+ languages, clickable quotes and citations, and exportable visual reports. End-to-end encryption, no third-party data sharing, and your data is not used to train AI. From USD 50/month for unlimited analysis.
Run the first pass in days, not weeks
Keep the judgment with you. Let Evidano do the heavy lifting on your interviews, focus groups, open-ended responses, and documents.
More case studies
AcademiaVolda and Melbourne researchers used AI interview analysis to triangulate manual coding
Two researchers coded 11 interviews by hand with over 90% agreement, then ran the anonymised transcripts through Evidano and compared the results for triangulation and validation.
AcademiaStanford and UC Davis researchers used AI to cross-check their qualitative coding
A published mixed-methods study uploaded deidentified transcripts into Evidano to cross-check its themes. Every AI code was reviewed by the researcher.
AcademiaCornell’s CLASP used AI thematic analysis on its program film. Adult educators concurred.
A published Cornell study used Evidano (previously AILYZE) for thematic analysis of its program film, then compared the AI’s reading with expert educators’ reactions at AAACE 2023.
Related published evidence
Researcher interviews and published citations covering comparable studies.
- Published research2026
Sharjah Education Academy: AI thematic analysis of 9 teacher interviews, human-verified
Published in Frontiers in Education (2026): encrypted data, human-verified themes, and a transparent AI coding workflow across English and Arabic interviews.
9 Teachers interviewed
Frontiers in Education, 11:1690181 (Frontiers Media, 2026), open access
- Published research2026
University of Glasgow used AI triangulation on a 16-interview thematic analysis
Inside a Springer-published University of Glasgow study that used Evidano to generate an independent thematic overview and triangulate its six themes.
16 semi-structured interviews, about 60 minutes each
Agile Processes in Software Engineering and Extreme Programming, XP 2026, Springer LNBIP 578, pp. 3–19, open access
Explore more evidence
Researcher interviews, the wording authors published when citing Evidano, and accuracy comparisons against manual coding.
- Resource
Research spotlights: researchers in their own words
Interviews with researchers at leading universities and institutes about how they actually used Evidano in work they went on to publish.
- Resource
How Evidano was used, in published methods sections
Verbatim excerpts from 20 published studies describing what Evidano did in their analysis — as a thought partner, a second coder, for initial coding, or for synthesis.
- Resource
Methodology guides and research writing
Practical guides to qualitative methods — thematic analysis, grounded theory, evidence synthesis and more — alongside the wider Evidano article library.
- Resource
Human vs AI: validated accuracy benchmarks
Three head-to-head comparisons against expert human analysis — 371 interview transcripts with Arizona State and Penn State, 298 evaluation reports for a UN evaluation group, and UNICEF’s manual-coding review.