Reframing the Digital Native Paradox: Strengthening Critical AI Literacy in Paediatric Medical Education

Dear Editor,

1.

We read with great interest the article ‘The Digital Native Paradox: A Framework for Critical Appraisal of Generative AI in Paediatric Medical Education, [1]’ which offers an insightful and much‐needed contribution to contemporary debates on AI integration in health professions education. The article is especially compelling because it questions the widespread belief that students who are accustomed to digital environments are automatically equipped to assess generative AI in a safe, reflective, and professionally responsible manner [2]. This is an important intervention in today's educational climate, where AI tools are increasingly normalized within learning and clinical preparation [1, 3]. We commend the authors for distinguishing routine technological use from genuine evaluative competence. Nevertheless, we suggest that the argument can be developed further by framing the issue as one of critical AI literacy rather than technological familiarity alone [3]. Our purpose in writing is to extend this discussion by considering its wider global, pedagogical, and practical significance.

From our perspective, critical AI literacy is best viewed as an integrated educational competence rather than a narrow technical skill [4]. It requires learners to examine the credibility of outputs, recognize embedded bias, reflect on uncertainty, interpret content within clinical context, and make ethically defensible judgements [5]. This issue has become increasingly important as generative AI tools influence not only how students obtain information, but also how they summarize evidence, generate explanations, and rehearse clinical reasoning [6]. Yet the development of such competence cannot be assumed to occur uniformly across settings. Educational institutions differ greatly in infrastructure, supervision, curriculum design, and cultural expectations regarding technology use. For this reason, frameworks developed in one context may not translate neatly into another [6]. A more adaptable approach is needed, one that acknowledges the limits of generalization and responds to the diverse realities of paediatric medical education across regions and systems [4].

The article makes meaningful progress by encouraging a more critical orientation toward AI, but further attention is needed to the mechanisms through which both benefit and risk are produced [7]. Generative AI can certainly expand access to explanations, offer immediate educational support, and assist learners in navigating complex topics. However, these advantages may be accompanied by unintended harms, including shallow knowledge construction, misplaced trust, and reduced willingness to verify information independently [8]. Such consequences are shaped not only by the learner, but also by the design of the platform itself. Systems that present responses in fluent, confident, and personalized language may encourage acceptance without sufficient scrutiny [9]. Accordingly, future inquiry should move beyond simple debates over usefulness and ask how design features, instructional environments, and assessment systems interact to influence judgement, reliance, and critical thinking in paediatric education.

Addressing these concerns requires a coordinated strategy that combines curricular reform, educator preparation, and institutional responsibility [2]. Critical AI literacy should be formally integrated into paediatric medical training through guided exercises, reflective evaluation tasks, and structured discussion of uncertainty, safety, and ethics [10]. Educators must also be supported through professional development so they can mentor students in the thoughtful use of AI rather than responding only with enthusiasm or prohibition. In parallel, assessment design should reward interpretation, reasoning, and contextual decision‐making instead of rote output reproduction. Human guidance remains indispensable throughout this process [5]. In paediatrics, decision‐making involves not only technical accuracy but also developmental sensitivity, interpersonal communication, and moral accountability. For these reasons, AI should be treated as a supplementary educational tool whose value depends on the quality of human judgement surrounding its use [5, 8].

In a broader sense, the conversation initiated by the original article points toward an important transformation in medical education. Its innovation lies in exposing the fragility of the digital native narrative and redirecting attention toward critical evaluation of generative AI [11]. Our contribution is to build on that insight by proposing a more context‐responsive model of critical AI literacy that better reflects global variation and the continuing necessity of human oversight. The original article is strong in its conceptual relevance and in its challenge to an assumption that has too often gone unquestioned. Still, its framework would be strengthened by more explicit engagement with differences across educational systems and resource conditions. Future work should therefore validate and refine this perspective across diverse settings so that AI adoption in paediatric medical education develops in a way that is equitable, reflective, and professionally sustainable [12].

Author Contributions

Veno Dwi Krisnanda: writing – review and editing, writing – original draft, data curation, conceptualisation.

Funding

The author has nothing to report.

Ethics Statement

The author has nothing to report.

Conflicts of Interest

The author declares no conflicts of interest.

Data Availability Statement

Data sharing is not applicable to this article as no datasets were generated or analysed for this editorial article.