ArticleMedical education online2026
Integrating ChatGPT into knowledge-retrieval tutorials in undergraduate medical education: a prospective evaluation of higher-order learning and feasibility.
Article in Medical education online, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
2 citing papers in PubMed.
- Article
- Artificial Intelligence Perceptions and Readiness Among Clinical Nurses: A Latent Profile Analysis With Implications for Nursing Management.Journal of nursing management · 2026Article
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundGenerative artificial intelligence (AI) platforms such as ChatGPT present new opportunities to strengthen competency-based medical education (CBME). While retrieval practice is a proven strategy for enhancing long-term retention, its application to higher-order domains of Bloom's taxonomy within CBME, particularly when scaffolded by AI, remains underexplored. To our knowledge, this is the first longitudinal CBME study worldwide to evaluate supervised ChatGPT-assisted retrieval practice and its durability over time.
methodsWe conducted a six-month, prospective, non-randomised delayed-intervention study and all 270 third-year MBBS students (including supplementary batch) at a private medical college in South India were invited to participate in a faculty-supervised ChatGPT-assisted retrieval-practice intervention. Participants were allocated into four tutorial clusters; two received the intervention immediately, while the remaining two received it after a delay. The intervention comprised four weekly, two-hour sessions featuring higher-order multiple-choice questions, structured faculty-supervised interactions with ChatGPT, and guided metacognitive reflections. Outcomes assessed included MCQ performance at baseline, immediately post-intervention, and at one- and three-month follow-up, as well as student perceptions. Data analysis employed repeated-measures ANOVA and mixed-effects modelling.
resultsOf 270 students invited, 253 (93.7 %) met inclusion criteria and completed all assessments, achieving 100% follow-up at immediate, one-month, and three-month evaluations. MCQ performance demonstrated significant improvement across time points (F[3,756] = 65.14,
conclusionSupervised ChatGPT-assisted retrieval practice produced sustained improvements in higher-order cognitive performance, with particularly strong benefits for Lower-achieving students. This scalable, standards-aligned model holds promise for advancing CBME globally and warrants further validation through multi-institutional trials incorporating performance-based and clinical outcomes.
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