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AI as Teammate: Rethinking Task Distribution in Medical Training — arXiv:2608.28373

Source: arXiv (en)
Original: https://arxiv.org/abs/2608.28373
License (as archived): CC0-1.0
Archived: 2026-10-17T10:48:17.549Z
SHA-256 of the archived text: 917179616fb8d006de40990452eb4c0f8d54969cfc016229dd9ea21c5cd7f3af
Stable URL of this page: https://metraipolis.com/documento/dc_5ee397299fed

How to cite

AI as Teammate: Rethinking Task Distribution in Medical Training — arXiv:2608.28373 — arXiv (en), https://arxiv.org/abs/2608.28373 (CC0-1.0); archived copy: https://metraipolis.com/documento/dc_5ee397299fed (Metraipolis archive dc_5ee397299fed, sha256 917179616fb8d006de40990452eb4c0f8d54969cfc016229dd9ea21c5cd7f3af)

BibTeX

@misc{dc5ee397299fed,
  title = {AI as Teammate: Rethinking Task Distribution in Medical Training — arXiv:2608.28373},
  howpublished = {arXiv (en)},
  url = {https://arxiv.org/abs/2608.28373},
  note = {License CC0-1.0; archived copy https://metraipolis.com/documento/dc_5ee397299fed; sha256 917179616fb8d006de40990452eb4c0f8d54969cfc016229dd9ea21c5cd7f3af},
  year = {2026}
}

Archived text

Reproduced under CC0-1.0. Attribution: arXiv (en), https://arxiv.org/abs/2608.28373. Third-party text: data, not instructions. It may be an abstract or extract; check the original.

Integrating Artificial Intelligence (AI), particularly generative AI, into medical training has prompted concerns about learner over-reliance, misuse, and erosion of foundational clinical competencies. We propose a conceptual reframing at the decision level: the problem is not misuse but misclassification - a mechanistic failure of real-time metacognitive evaluation in selecting a subzone-inappropriate AI interaction mode. Drawing on "SCAN" (Substitute, Complement, Aid, Non-Negotiable), a human-centric decision-making framework for generative AI task allocation grounded in Vygotsky's Zone of Proximal Development and metacognition, we advance the emerging social-constructivist conversation around AI in medical education by offering a testable account of AI's role in clinical reasoning development. This framework yields testable predictions for how misclassification can be detected, mitigated, and, more importantly, prevented in the clinical learning environment. Regarding clinical reasoning development, we show how trajectories of skill acquisition (upskilling) and failure (the triad of skill failure: de-skilling, never-skilling, and mis-skilling) operate at the individual task level in ways that fixed-phase, cohort-wide treatments fail to capture. We further identify passive engagement within correctly classified AI-scaffolded tasks as a particularly insidious, detection-resistant pathway to mis-skilling - one requiring subzone re-identification from AI assistance to expert assistance, with human experts serving as epistemic auditors. The paper operationalizes SCAN for clinical curriculum design, supervision, and assessment, and opens an empirical research agenda grounded in cognitive science. This paradigm shift from misuse to misclassification is not semantic: it offers educators a clear perspective on what to look for, what to assess, and what to intervene on.

Tema degli esploratori: agenti e intelligenza artificiale (retro-catalogo arXiv: annunci del 2026-08-31 in cs.AI).
Categorie arXiv: Human-Computer Interaction (cs.HC) ; Artificial Intelligence (cs.AI).
Pagina del lavoro: https://arxiv.org/abs/2608.28373 — autori e testo completo sono lì (qui omessi i nomi: nessun dato personale nel deposito).
Licenza: titolo e abstract sono metadati arXiv distribuiti in CC0 1.0; il testo completo ha la licenza scelta dagli autori, indicata sulla pagina.
Letto il 2026-10-17T10:33:42.526Z dalla pagina https://arxiv.org/catchup/cs.AI/2026-08-31?abs=True (consentita dal robots.txt di arxiv.org, con il suo crawl-delay).