{"id":"5bb6b00b-8715-4106-8316-b25f9de1d834","arxiv_id":"2411.15710","paper_version":1,"verdict":"CONDITIONAL","confidence":"HIGH","novelty_score":4.0,"correctness_risk":"medium","formal_verification":"none","parameter_count":0,"one_line_summary":"Students accept and trust AI-generated images for educational use but worry about technical precision in detail-oriented tasks.","lead":"This paper reports a small exploratory study of 15 computer science undergraduates who used ChatGPT to generate images for academic tasks. It finds generally positive acceptance and trust, tempered by concerns about the AI's accuracy in following precise prompts.","discovery_kind":"extension","skeptic_critique":{"model":"deepseek-v4-flash","headline":"Reported trust-scale means (~3.3–3.7 on a 5-point scale) do not support the abstract's 'high trust'; the data support moderate trust at most.","rationale":"The paper is a modest exploratory study with useful qualitative themes, and the TAM/TAS results do show means around 4 on their respective scales, which supports positive acceptance and attitudes. However, the trust scale results in §4.3 are the weakest quantitative support for the abstract's central claim. The reader's weakest assumption concerned self-selection bias in the sample; that is an external validity issue. My concern is internal: even if the sample were perfectly representative, the reported trust means (M ≈ 3.3–3.7 on a 5-point scale) do not justify the word 'high.' This matters more because it directly affects the abstract's headline claim rather than its generalizability. The fix is straightforward—recalibrate the language to 'moderate trust' or provide significance tests against the scale midpoint. Since the acceptance and attitude components are better supported, and the paper can be revised without changing its overall direction, the conditional verdict remains appropriate. I therefore recommend no change to the reader's verdict.","tokens_in":9905,"tokens_out":3477,"duration_ms":33546,"concrete_test":"Obtain item-level responses for the six Trust Scale questions (or reconstruct from reported M and SD), then run one-sample t-tests and Wilcoxon signed-rank tests against the neutral midpoint 3, with n=15. Compute 95% confidence intervals and Cohen's d for each item and for the composite mean. If the composite trust mean (reported M=3.3, SD=0.7) yields a confidence interval that includes 3, or the test fails to reach significance (p<0.05), the abstract's 'high trust' claim should be downgraded to 'moderate' or 'slightly positive.' Apply the same midpoint test to the TAM and TAS means to verify which components actually exceed their scale midpoints.","verdict_should_be":"UNCHANGED","load_bearing_attack":"The abstract's central claim asserts 'high acceptance, trust, and positive attitudes' among students. The quantitative evidence in §4.3 undercuts the trust component: on the 5-point Trust Scale, overall trust M=3.3, SD=0.7; competence M=3.6; confidence M=3.4; dependability M=3.3; behavioral consistency M=3.7; reliability M=3.7. These means cluster near the scale midpoint, not the 'high' end, and no inferential test against a neutral baseline is reported. The discussion itself softens to 'moderate to high trust' (§5), and the conclusion reverts to 'positive attitudes and trust are prevalent' without acknowledging this qualification. The trust characterization is load-bearing because the paper's contribution is specifically to establish high trust empirically; if these means are not distinguishable from neutrality, the headline conclusion overstates what the data show. This is an internal data-claim mismatch, independent of the sampling concerns already raised.","agreement_with_reader":"disagree"},"referee_report":{"model":"deepseek-v4-flash","summary":"The manuscript reports an exploratory mixed-methods study of 15 undergraduate computer science and software engineering students at one Thai university. Participants used ChatGPT 4.0 to generate images from ten pre-defined prompts, then completed adapted Technology Acceptance Model (TAM), Trust Scale, and Technology Attitude Scale (TAS) questionnaires and a structured interview. The paper claims that students show high acceptance, trust, and positive attitudes toward AI-generated images for educational purposes, while concerns about realism and technical precision limit use in detail-oriented tasks.","tokens_in":10012,"tokens_out":3808,"duration_ms":33652,"significance":"The study addresses a genuine gap: student perspectives on AI-generated images in higher education are underrepresented relative to technical and instructor-focused work. The mixed-methods design, the use of adapted published instruments, and the thematically organized qualitative findings are strengths. If the central descriptive claims were properly calibrated, the study would provide a useful exploratory baseline for future research. However, the current quantitative evidence does not support the 'high trust' headline, and the sample is too narrow and self-selected to support broad generalizations about 'students'.","major_comments":[{"comment":"The abstract's claim of 'high trust' is not supported by the data reported in Section 4.3. The overall trust mean is 3.3 (SD=0.7), with dimension means of 3.3 to 3.7 on a 5-point scale, i.e., close to the neutral midpoint. No inferential test against a neutral baseline, confidence interval, or comparison standard is provided, so the data are equally consistent with moderate or neutral trust. The Discussion correctly softens to 'moderate to high trust' (Section 5), but the Abstract and Conclusion (Section 6) revert to unqualified positive claims. This is load-bearing because the paper's stated contribution is the empirical demonstration of high acceptance, trust, and positive attitudes. I recommend either reporting baseline comparisons (e.g., one-sample tests against the midpoint, with appropriate caution for n=15) or systematically replacing 'high' with 'moderate' or 'mixed' throughout the abstract, discussion, and conclusion.","section":"Section 4.3 and Abstract"},{"comment":"The study's external validity claims are not supported by the sampling procedure. Fifteen volunteers recruited via one university's social media student groups from two programs cannot support statements about 'students' in general, as made in the Abstract and Conclusion. If the sample overrepresents students already interested in AI, all reported means could be inflated. The paper should reframe all general conclusions as specific to this sample and treat the study as hypothesis-generating, or provide a sampling strategy and justification of representativeness if broader claims are intended.","section":"Section 3 (Method), participant recruitment"},{"comment":"The measurement of 'positive attitudes' rests on the 'Technology Attitude Scale' attributed to Rosen et al. (2013), but that reference describes the Media and Technology Usage and Attitudes Scale, which is a broader instrument; the paper does not specify which items or subscales were adapted, nor does it include the actual items. Additionally, the text states that the entire study required about 45 minutes per participant, while Table 2 sums to 70 minutes (10 pre-study + 30 in-study + 30 post-study). Both issues affect reproducibility: readers cannot tell what was measured or how long the procedure actually took.","section":"Section 3 (Method), instruments and procedure"}],"minor_comments":[{"comment":"The phrase 'The Participants emphasized' contains an unnecessary capital 'P' in 'Participants' and should be corrected.","section":"Section 4.5.6"},{"comment":"Creswell (2014) is listed in the references but never cited in the text; Zhu et al. (2021) is cited in Section 1 for ethical challenges, but the listed reference is the CycleGAN paper, which does not address ethics; Epstein et al. (2023) is missing its title in the reference list.","section":"References"},{"comment":"The theme names in Table 1 use inconsistent punctuation, with some themes ending in a colon; please format the table consistently.","section":"Table 1"},{"comment":"The combined reporting 'TA7, TA8: M = 3.9' obscures which items are being combined; report each item separately or explain the aggregation.","section":"Section 4.2"},{"comment":"Figure 1 is not described in the text beyond its caption; add a sentence in Section 4.1 referring to it so readers understand what is shown.","section":"Section 4.1 and Figure 1"}],"recommendation":"major_revision","confidential_remarks":"The paper is a competent exploratory study, but the gap between the reported means and the 'high trust' language is a calibration problem that the authors should fix. The small, single-institution, self-selected sample makes the broad generalizations risky. If the authors revise the claims to match the evidence and clearly frame the study as exploratory, it could become a contribution to the CS-education or HCI literature; otherwise, the overstatements undermine the central contribution."},"author_rebuttal":null,"desk_editor":{"model":"deepseek-v4-flash","letter":"Short version: this is a modest exploratory survey with a method that mostly fits the question, but the abstract's 'high trust' doesn't survive contact with its own numbers.\n\nWhat's new: first data I've seen on Thai CS/SE undergraduates using AI image generators for coursework. The instruments (TAM, Trust Scale, TAS) are established, and the authors adapted them reasonably. The method section is clear enough to replicate: ten themes, randomized prompt order, ~30 minutes of generation plus questionnaires and interviews. The qualitative themes (perceived quality, realism concerns, technical precision limits) line up with the quantitative patterns, which is a good sign.\n\nThe soft spots are real but not fatal. The biggest is the trust claim. On a 5-point scale, overall trust M=3.3, competence 3.6, confidence 3.4, dependability 3.3, behavioral consistency 3.7, reliability 3.7. Those are around the midpoint. Calling that 'high trust' is a stretch; 'moderate' is defensible, and the discussion actually says 'moderate to high trust' before the abstract and conclusion re-overstate. Second, the sample is 15 self-selected volunteers from one university's social media groups. That cannot support generalizations to 'students' writ large. Third, there's no inferential statistics at all – no CIs or tests against the scale midpoint, so we can't tell if those means differ from neutral. Fourth, the Zhu et al. (2021) citation is wrong: that's the CycleGAN paper, not a reference on AI image ethics. That's sloppy but fixable.\n\nThe paper is for people working on AI acceptance in education, especially regionally specific studies. It's a small data point, not a definitive answer. I'd send it out for review because the method is transparent and the topic is timely, but I'd expect major revisions: reword the abstract to match the data, report some inferential stats or explicitly justify the descriptive approach, and tone down the generalisation. With those changes it could be a useful descriptive record.\n\nRecommendation: engage with it. Desk rejection would be premature, but it needs serious referee time.","headline":"A modest, methodically described exploratory study whose abstract overstates trust: the reported means are moderate, not high.","tokens_in":58,"tokens_out":2228,"would_cite":false,"duration_ms":83075,"reading_group":"maybe","serious_thinker":"yes","would_accept_peer_review":true},"rs_alignment":null,"lean_confirmation":null,"pith_extraction":{"msc":[],"pacs":[],"model":"deepseek-v4-flash","headline":"This paper claims that undergraduate computer science and software engineering students readily accept and trust AI-generated images for educational tasks, with failures in realism and text rendering as the main limit on detail-oriented…","keywords":["AI-generated images","technology acceptance","student trust","attitudes toward AI","educational technology","usability testing","human-computer interaction"],"falsifier":"A replication with a larger, randomly selected sample of undergraduate computer science and software engineering students at several universities would settle the claim: if the mean acceptance, trust, and attitude scores fall at or below the neutral midpoints of their scales, the reported high acceptance, trust, and positive attitudes do not generalize.","tokens_in":9655,"feed_emoji":"🖼️","tokens_out":7283,"duration_ms":65751,"temperature":0.7,"pith_summary":"The paper sets out to show that undergraduate computer science and software engineering students accept AI-generated images for educational tasks such as presentations, reports, and web design, and that their trust and attitudes are positive enough to support classroom use. Using questionnaires and interviews with fifteen students who generated images with ChatGPT 4.0, it reports high self-efficacy, ease of use, perceived usefulness, enjoyment, and behavioral intention, with moderate-to-high trust scores clustered around 3.3 to 3.7 on a 5-point scale. At the same time, participants consistently flagged realism problems in human figures and misspelled or illegible text, which the study says moderately limits the images' use in detail-oriented technical work. The author concludes that wider educational adoption depends on better prompt fidelity, realism, affordability, and clear ethical and quality guidelines.","feed_headline":"Students accept AI images for classwork, flag precision gaps","feed_subtitle":"CS/SE undergrads show high acceptance and moderate trust; realism and prompt-fidelity limits block technical use.","key_machinery":"The argument is carried by a task-based usability protocol combined with three standardized instruments. Fifteen undergraduate participants used ChatGPT 4.0 through a desktop browser to generate images from ten pre-defined prompts spanning themes such as technology, culture, nature, and health; they then completed the Technology Acceptance Model questionnaire (adapted to measure self-efficacy, social norms, perceived usefulness, and behavioral intention), the Trust Scale, and the Technology Attitude Scale, followed by open-ended interviews. The Likert-scale scores provide the quantitative picture of acceptance, trust, and attitudes, while thematic analysis of the interviews supplies the realism and accuracy concerns that explain why trust stays only moderate.","core_discovery":"The central claim, stated the way the author would state it, is that AI-generated images are already an accepted and trusted aid for many undergraduate educational tasks, but they are not yet a precision tool. Students rated perceived usefulness and ease of use well above the scale midpoint (for example, TA11 M=4.1, TA13 M=4.5), reported strong confidence and enjoyment (TAS2 M=4.6, TAS3 M=4.5), and expressed clear intention to keep using the images in future coursework. Trust was more guarded, with competence, dependability, and reliability ratings in the 3.3-3.7 range. Interview responses explain the gap: images are praised as beautiful and immediately usable for creative and presentation work, but criticized for unrealistic human depictions, incorrect text, and failure to follow detailed prompts, making them unsuitable for technical diagrams and other accuracy-critical assignments. The paper takes this combination as evidence that students will adopt AI images now for creative and illustrative purposes, while more demanding educational uses await improvements in realism, prompt fidelity, and access.","pith_inferences":["A testable extension beyond the paper is to compare disciplines: the precision complaint would predict lower usefulness ratings in engineering, medicine, and science than in design and humanities courses.","Because trust scores lag behind attitude scores, one visible high-stakes failure—a technical diagram with wrong labels in a graded report—could suppress trust more sharply than the overall positive attitudes imply.","The realism and text-rendering limitations point to a curricular use the paper leaves implicit: treating AI image generators as draft tools whose outputs students must verify and edit rather than adopt as finished."],"forward_implications":["AI-generated images can serve as ready-to-use visual content for presentations, reports, and creative design tasks, where students judge them as immediately usable.","Realism and text-rendering failures will keep AI-generated images out of technical diagrams and other accuracy-critical assignments until generators can follow prompts with higher fidelity.","Cost and access are practical adoption barriers, so affordable or institutionally provided access to AI image tools should increase their educational use.","The coexistence of high acceptance with moderate trust suggests that educational guidelines addressing accuracy, intellectual property, and quality standards are needed before AI images become routine in coursework."],"supporting_citations":[{"why":"Provides the Technology Acceptance Model's core constructs linking perceived usefulness and ease of use to acceptance.","marker":"Davis (1989)"},{"why":"Supplies the adapted TAM questionnaire items used to measure self-efficacy, social norms, and perceived usefulness.","marker":"Aburbeian et al. (2022)"},{"why":"Supplies the Trust Scale used to measure trust in AI-generated images.","marker":"Merritt (2011)"},{"why":"Supplies the Technology Attitude Scale used to assess confidence, enjoyment, and perceived benefits.","marker":"Rosen et al. (2013)"},{"why":"Documents accuracy failures of AI images in medical education, the comparison point for the paper's precision concerns.","marker":"Temsah et al. (2024)"}],"fun_headline_variants":["AI images get student thumbs-up for class, but precision lags","Students trust AI images for creative work, not technical accuracy","Undergrads welcome AI images, but demand better prompt fidelity","For class tasks, AI images win acceptance but not precision"],"cache_read_input_tokens":3200,"weakest_assumption_plain":"The load-bearing assumption is that fifteen self-selected volunteers recruited through university social media groups represent undergraduate computer science and software engineering students broadly enough for the study's general statements about student acceptance, trust, and attitudes.","fun_headline_variants_meta":{"raw":{"variants":["AI images get student thumbs-up for class, but precision lags","Students trust AI images for creative work, not technical accuracy","Undergrads welcome AI images, but demand better prompt fidelity","For class tasks, AI images win acceptance but not precision"]},"model":"deepseek-v4-flash","effort":"low","cost_usd":0.00064,"raw_usage":{"total_tokens":2944,"prompt_tokens":943,"completion_tokens":2001,"prompt_tokens_details":{"cached_tokens":384},"prompt_cache_hit_tokens":384,"prompt_cache_miss_tokens":559,"completion_tokens_details":{"reasoning_tokens":1930}},"tokens_in":559,"tokens_out":2001,"duration_ms":13529,"temperature":1.0,"reasoning_tokens":1930,"cache_read_input_tokens":384,"cache_creation_input_tokens":0},"cache_creation_input_tokens":0},"created_at":"2026-08-12T13:59:07.514559+00:00","model_set":{"reader":"deepseek-v4-flash"},"falsifier":"A replication with a larger, randomly selected sample of undergraduate computer science and software engineering students at several universities would settle the claim: if the mean acceptance, trust, and attitude scores fall at or below the neutral midpoints of their scales, the reported high acceptance, trust, and positive attitudes do not generalize.","supporting_citations":[],"review_version":1}