Comment on “Postgraduate students' perceptions of artificial intelligence integration in research: A cross-sectional study”

Continuous-staged autonomous run: OA full-text critique engaging the coverage blind spots (identification, sample_data, reproducibility); span-grounded to the gold-OA full text.

This is a single-university survey of 267 nursing/health master's students in Saudi Arabia about how they perceive using AI in research. It is honestly framed: the authors say upfront it cannot prove cause and effect, admit their volunteer sample skews toward tech-savvy students, and limit their conclusions to this one institution. After cleaning up an over-fired draft critique that was full of details not actually in the paper, three real problems remain. First, the paper interprets a statistical finding — that people more worried about privacy also reported more intention to adopt AI — as evidence of sophisticated "critical literacy," which reads more into a single correlation than it can bear. Second, there's a logical snag: having used AI before was supposedly required to take part, yet "prior AI use: yes/no" is used as a predictor and only 85% report prior use, so the entry rule and the data don't line up. Third, only summary tables are shared, so no one can independently re-run the analysis. The descriptive numbers are useful context; the bigger interpretive claims are overstated.

A single-institution cross-sectional survey of 267 nursing/health-profession master's students in Saudi Arabia on perceptions of AI in research, using a TAM-based validated instrument; the authors explicitly disclaim causal inference and name their convenience-sampling bias.

A positive cross-sectional association between privacy concerns and adoption intention is recast as a psychological disposition ('critical literacy rather than barriers to adoption'). This is a causal-interpretive leap from a single correlation in a one-time-point, self-report design; given the paper's own reported VIF up to 4.8, a collinearity/suppression artifact is at least as plausible as a substantive trait, yet no such alternative is considered and the framing is asserted in the conclusion as a population-level characteristic.

The abstract uses predictor/strength language ('strongest predictor', 'positively associated ... suggesting informed and critical engagement') that imports a directional, mechanistic reading the design cannot support: benefits, privacy concerns and adoption intention are all measured simultaneously by the same respondent on the same instrument, so common-method variance and reverse causation (intending to adopt inflates perceived benefits) are confounded with the reported associations. The Methods section does correctly disclaim causality, so this is an inconsistency between hedged methods and causal-flavored framing rather than a uniform error.

Internal contradiction between eligibility and analysis. Prior experience using or evaluating AI tools is stated as an explicit inclusion criterion, yet the paper reports only 85.0% prior use and enters 'Prior AI Tool Use (Yes vs. No)' as a regression predictor in Table 4. If the inclusion filter were applied, there could be no 'No' category and no ~15% non-users; the coexistence of the filter, the 85% figure, and the Yes/No predictor is logically incoherent and signals either inconsistent screening or mis-specification. (The draft's specific '40 participants (15.0%)' / 'Table 1' figures are not in the text and are not relied upon here.)

This is a competently executed, unusually self-aware descriptive survey for its genre. The authors explicitly disclaim causal inference ("this design precludes causal inference and cannot establish temporal precedence"), name the convenience-sampling bias and its likely direction, restrict generalization to a single regional case study rather than a population, and ground the work in an established framework (TAM) using a previously validated published instrument administered in its original English to an English-instruction health-sciences cohort. The methods exceed many comparable perception studies: an a priori G*Power sample-size calculation, HC3 robust standard errors, reported VIF/outlier diagnostics, recomputed subscale reliabilities, and — notably — a full Table 4 with B, SE, 95% confidence intervals, standardized betas and p-values (so the draft's "no confidence intervals" charge is simply wrong). As context-specific, exploratory evidence from an under-studied non-Western health-professional population, which the paper repeatedly and accurately frames it as, the descriptive findings are informative and modestly stated.