The study is notable for what it did not do. Rather than asking teachers to imagine how they might use AI someday, the researchers embedded a working tool into the messy reality of school-based teacher training. CReVAG was implemented as a Custom GPT within ChatGPT and configured as a visual-design assistant for culturally responsive teaching resources. Before generating any image, the system prompted users to specify contextual information: country, region or district, learner level, subject and topic. Where relevant, users could add language background, community setting, curriculum focus, cultural sensitivities and intended learning outcomes. The tool’s knowledge base contained curriculum materials from Ghana’s Education Service and National Council for Curriculum and Assessment, and only Canvas and Image Generation capabilities were activated for the study.

Participants received a one-week orientation covering contextual prompting, visual generation, refinement and critical evaluation before applying the tool during their twelve-week practicum. The workflow placed the teacher firmly in charge: context specification, visual generation, teacher review, refinement and instructional use. Teachers remained responsible for checking clarity, curriculum relevance, cultural appropriateness and possible stereotypes before using any generated resource in a practicum lesson. Survey data were collected once, after the sixth week of practicum, once participants had gained genuine hands-on experience with the system in their host schools.

The research team measured six constructs: self-efficacy, culturally responsive teaching self-efficacy, perceived system usability, AI tool acceptance, visual resource quality and lesson delivery effectiveness. Using Partial Least Squares Structural Equation Modelling, complemented by Necessary Condition Analysis, they tested twelve hypothesised relationships. The model explained 39.9 percent of the variance in AI tool acceptance, 39.3 percent in visual resource quality and a striking 61.6 percent in lesson delivery effectiveness, with an SRMR of 0.077 indicating acceptable fit. Eight of the twelve hypothesised associations reached statistical significance at the two-tailed 0.05 level.

Two relationships stood out in magnitude. Perceived system usability showed the largest direct association with AI tool acceptance (β = 0.330, p = .004), suggesting that in the pressured environment of a teaching practicum, where students juggle lesson planning, materials preparation and actual classroom teaching, an AI assistant that is cumbersome to operate becomes another burden rather than a help. Meanwhile, visual resource quality showed the largest direct association with lesson delivery effectiveness (β = 0.309, p < .001). In other words, what mattered most for teaching was not enthusiasm for the tool but the perceived clarity, relevance, cultural appropriateness and pedagogical suitability of the images it produced.

The Necessary Condition Analysis added a complementary perspective. While structural equation modelling describes average associations, NCA asks whether high levels of an outcome are simply unattainable when a condition falls below some empirical threshold. Here, visual resource quality emerged as the strongest statistical necessity condition for high lesson delivery effectiveness (d = 0.190, p = .002), followed by AI tool acceptance (d = 0.158). The bottleneck analysis indicated that at approximately 90 percent of the observed lesson delivery range, visual resource quality needed to reach roughly 3.0 on the five-point scale and system usability about 3.2. The authors stress these are sample-specific thresholds, not universal standards or causal cut-offs.

Perhaps the most theoretically intriguing finding is what failed to reach significance. AI tool acceptance was not significantly associated with visual resource quality (β = 0.167, p = .074), meaning that teachers who liked the tool did not necessarily judge its outputs as pedagogically sound. Similarly, culturally responsive teaching self-efficacy predicted evaluations of the generated resources but not acceptance of the system itself. The authors argue this reveals three conceptually distinct evaluative levels in AI-supported teaching: appraisal of the AI system, evaluation of the instructional artefact it produces, and perceived instructional usefulness during lesson delivery. A teacher can embrace an AI platform wholeheartedly while still, quite rightly, rejecting its images as culturally inaccurate or pedagogically weak.

The subgroup analyses were handled with statistical caution. Because programme groups were markedly unequal in size, with 77.6 percent of participants in Junior High School Education, the researchers used Welch’s ANOVA and Games-Howell comparisons where homogeneity assumptions failed. The most defensible programme differences concerned system usability and lesson delivery effectiveness, with JHS participants reporting higher scores than Primary Education participants. Gender differences were generally small, although males reported significantly higher AI tool acceptance (d = 0.40, p = .006) and somewhat higher visual resource quality ratings. The authors explicitly warn against deficit interpretations, noting these within-sample differences do not establish gender as a cause of AI-related beliefs.

The study’s limitations are candidly acknowledged. Because all constructs were measured once, after exposure to CReVAG, the findings represent cross-sectional explanatory associations rather than causal effects. The sample came from a single institution, the data relied on self-reports, and the saved CReVAG configuration did not lock users to a single underlying ChatGPT model, meaning participants may not have experienced completely standardised technological exposure. Prior AI experience, reported by 77.6 percent of participants, was not part of the prespecified model, though sensitivity analyses showed the focal coefficients were broadly stable when it was added as a control. Notably, prior AI experience was positively associated with visual resource quality, hinting that the depth and character of earlier AI use may shape how teachers evaluate generated materials.

The implications reach well beyond Ghana. The authors argue that teacher-education programmes should move beyond one-off AI demonstrations toward sustained practice in prompting, evaluating outputs, correcting inaccuracies and integrating suitable visuals into actual lessons. Culturally responsive prompting and evaluation deserve explicit attention, with clear institutional criteria for accuracy, clarity, curriculum alignment, cultural appropriateness and accessibility. AI-generated materials, they contend, should be treated as drafts requiring professional evaluation rather than ready-made teaching resources. For developers, CReVAG illustrates the value of systems that request contextual information before generating educational visuals instead of relying on generic prompts. And for everyone watching generative AI reshape education, the message is clear: the technology’s real promise lies not in what it can generate, but in the professional judgement teachers bring to judging, refining and using what it makes.

Subject of Research: Pre-service teachers' use of a culturally responsive generative AI tool for creating visual teaching resources in Ghana

Article Title: Pre-service teachers’ interactions with generative AI for creating culturally grounded visual teaching resources

Article References: Nabang, M., Essel, H. B., Tachie-Menson, A., Setsoafia, P., Kodua, C. A., Kubi, J. A., Acheampong, E. B., Aboagye, G. A., & Nyaaba, M. (2026). Pre-service teachers’ interactions with generative AI for creating culturally grounded visual teaching resources. Discover Education, 5(1), Article 1085. https://doi.org/10.1007/s44217-026-02241-y

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02241-y

Keywords: generative AI, pre-service teachers, culturally responsive teaching, teacher education, Ghana, visual teaching resources, AI tool acceptance, system usability, PLS-SEM, Necessary Condition Analysis, lesson delivery, self-efficacy

The study, led by researchers affiliated with the Second Xiangya Hospital of Central South University, was conducted between August and December 2025. Teachers at 16 medical colleges across central, eastern, and western China voluntarily forwarded a standardized anonymous survey link to nursing students. Eligible participants were full-time associate degree, undergraduate, or master’s students who had used an adaptive virtual simulation platform at least twice in their current-semester courses. After applying strict quality controls—including a minimum completion time of 120 seconds, patterned-response detection, and an embedded attention-check item—2,016 valid questionnaires remained from 2,030 returned.

The platform under study, developed by Beijing Oubeier Software Technology Development Co. Ltd., included more than 50 nursing-skill modules covering fundamentals of nursing and medical-surgical nursing, with each simulation task typically lasting 20 to 30 minutes. Importantly, the researchers describe its adaptive functions as rule-based rather than genuinely intelligent: the system compared students’ recorded operational steps, decision pathways, and response times against predefined task rules and performance thresholds to trigger branch-specific feedback and adjust scenario difficulty. No individual platform logs were linked to the survey responses.

The sample was predominantly female (83.48 percent) and aged 19 to 24 (88.39 percent), comprising 1,580 undergraduate students, 380 associate degree students, and 56 postgraduates. The researchers measured four core constructs using validated instruments: the System Usability Scale (SUS), the extraneous cognitive load subscale of Leppink’s Cognitive Load Scale, an adapted nine-item version of the Flow in Education Scale (EduFlow-2), and the nine-item Utrecht Work Engagement Scale for Students (UWES-S). All scales showed acceptable to excellent reliability, with Cronbach’s alpha values ranging from 0.749 to 0.899.

The descriptive picture was sobering for platform designers. The mean usability score translated to roughly 56.4 on the conventional 0-to-100 SUS scale—below the widely used benchmark of 68 that signals above-average usability. In other words, students on average perceived the system as only moderately usable, even as they reported moderate levels of load, flow, and engagement.

Structural equation modeling revealed a clear pattern of statistical associations. Greater perceived usability was associated with lower extraneous cognitive load (standardized coefficient β = −0.497), the load imposed by confusing interfaces and unclear instructions rather than by the learning material itself. Higher extraneous load was in turn associated with lower flow experience (β = −0.481)—the absorbing state in which learners concentrate fully under balanced challenge—and greater flow was strongly associated with higher learning engagement (β = 0.492). Usability also showed a direct positive association with engagement (β = 0.213).

Bootstrap mediation analysis with 5,000 resamples supported a sequential indirect association: usability → lower extraneous load → greater flow → higher engagement, with a standardized indirect estimate of 0.116 and a 95 percent confidence interval excluding zero. Notably, neither of the two simpler two-step pathways—usability through load alone, or usability through flow alone—reached significance, suggesting that load and flow operate as a linked sequence rather than as independent routes. Extraneous load showed no direct association with engagement once flow was accounted for, implying that its influence on engagement is realized through learners’ experiential state.

The study also examined whether students’ digital readiness—an exploratory index combining prior AI tool use and self-perceived digital competence—changed these relationships. Multi-group analysis, backed by a continuous-variable interaction test, showed that the negative association between extraneous load and flow was stronger among students with lower digital readiness (β = −0.519) than among those with higher readiness (β = −0.374). For less digitally prepared students, a clumsy interface appears to be a more serious barrier to achieving immersive, engaged learning. The authors suggest practical remedies: pre-training orientation sessions, step-by-step guided tutorials, simplified navigation, minimal operational steps, and graduated technical support during initial system use.

The researchers are careful about the limits of their design. All variables were measured concurrently, so the findings describe cross-sectional statistical associations rather than causal effects; temporal ordering and causation cannot be established from this data. Common method bias was formally tested with Harman’s single-factor test and an unmeasured latent method construct approach, both of which suggested it was not a serious concern, but the data remain self-reported. The study also lacked objective learning outcome measures such as Objective Structured Clinical Examination scores, and the purposive dissemination through 16 colleges means the sample cannot be considered nationally representative of Chinese nursing students.

Even so, the implications for nursing education are concrete. As AI-based simulation spreads—publications on AI in nursing grew from 38 in 2014 to 563 in 2024 by one count—institutions should prioritize usability testing and iterative design improvement when procuring educational technology, treat clear navigation and concise instructions as core pedagogical features rather than cosmetic details, and build differentiated support for students with varying digital readiness. The study’s message is that engagement with AI simulation is not simply a matter of supplying advanced technology; it depends on whether the interface lets students spend their limited working memory on nursing knowledge instead of on wrestling with the system itself.

Subject of Research: Cross-sectional associations among AI system usability, extraneous cognitive load, flow experience, and learning engagement in nursing simulation education.

Article Title: AI System Usability and Learning Engagement in Nursing Simulation: Cross‐Sectional Statistical Indirect Associations Through Extraneous Cognitive Load and Flow Experience

Article References: Liu, M., Zhang, P., Wu, Y., Pan, L., & Li, L. (2026). AI System Usability and Learning Engagement in Nursing Simulation: Cross‐Sectional Statistical Indirect Associations Through Extraneous Cognitive Load and Flow Experience. Nursing Open, 13(9), Article e70855. https://doi.org/10.1002/nop2.70855

Image Credits: AI Generated

DOI: 10.1002/nop2.70855

Keywords: artificial intelligence, virtual simulation, nursing education, system usability, cognitive load, flow experience, learning engagement, digital readiness, structural equation modeling, cross-sectional survey, nursing students, educational technology