Aug 2026· Journal of Paramedic Practice· 0 citations· 9 references
TL;DR
Which elements of paramedic training should remain inherently human, which are best delivered through hybrid human–AI models, and which tasks can be ethically and safely automated with oversight are examined.
Abstract
Paramedic education is entering a pivotal era as artificial intelligence (AI) rapidly evolves from decision-support tools to sophisticated learning and simulation technologies. This article examines which elements of paramedic training should remain inherently human, which are best delivered through hybrid human–AI models, and which tasks can be ethically and safely automated with oversight. Core relational competencies such as delivering bad news, communicating prognostic uncertainty and demonstrating empathy are identified as irreducibly human owing to their emotional, ethical and interpersonal complexity. Hybrid approaches show promise in simulation, assessment and clinical reasoning, where AI can enhance exposure, personalise feedback and support structured decision-making, while retaining expert supervision. Certain pattern-recognition or administrative tasks, including electrocardiogram (ECG) interpretation and teaching-material generation, can be responsibly automated within governance frameworks. The authors propose a curriculum-design framework and implementation strategy to guide educators in developing AI-literate, compassionate practitioners while ensuring transparency, safety and ongoing evaluation.
This curriculum offers a generalizable, no-cost model for closing the AI evaluation skills gap in healthcare education, combining interactive correction with a mandatory, learner-defined capstone project.
Hiba Hamdar· Journal of Medical Education...· 0 citations
Nuclear medicine (NM) is rapidly expanding with new radiopharmaceuticals, imaging equipment, and theranostic possibilities, necessitating a capable and competent workforce expansion. Consequently, NM education must undergo a transformation, powered in part by artificial intelligence (AI). AI is rapidly entering NM education through learner study materials, assessment design, simulation, feedback, and administrative workflows. AI’s value depends less on tool novelty than on ethical alignment with effective learning principles. NM educators and trainees need practical approaches for AI tool use that preserve human judgment, accountability, critical thought, creativity, privacy, and source verification. As the first article in a 3-part series on AI-empowered NM education, this practical guide summarizes and integrates selected literature from AI in health professions education, NM education, learning theories, and ethics. Building on a framework of established learning theories (transformative learning, self-regulated learning, experiential learning, connectivism, constructivism, and cognitive load theory) and ethical concerns about AI (e.g., accuracy, bias, transparency, privacy), we describe effective AI techniques (structured prompt design, retrieval-augmented generation, meta-prompting, AI customization, vibe coding) that can be applied for the NM educator and learner in parts 2 and 3 of the series, respectively. These benefits require explicit safeguards, including AI guardrail incorporation, privacy protection, expert review, AI output transparency, and healthy skepticism. AI should be integrated into NM education through theory-informed, source-grounded, and expert-reviewed workflows. Meeting the demands of increased NM volumes and expanded professional opportunities will require AI-empowered educators and trainees that are able to self-regulate and transform their learning through healthy patterns of human–AI interaction.
Keith M. Jacobs, J. Peacock· Journal of Nuclear Medicine...· 0 citations
It is argued that AI-focused CME should be designed as a longitudinal organizational learning system rather than a discrete instructional event, and implications are offered for technology integration, faculty development, and organizational learning in clinical education.
T. Murphy, Rob E. Carpenter· International Journal on Int...· 1 citation
Expanding clinical volumes, evolving radiopharmaceuticals, and new imaging technologies shape the training environment for nuclear medicine (NM) learners. Additionally, artificial intelligence (AI) tools are increasingly available. These tools can draft study guides, generate questions, simulate patient interactions, critique explanations, organize sources, and assist early research development. However, these benefits require disciplined use, because AI tools can also generate inaccurate, biased, unsupported, or overly fluent responses that interfere with durable learning. This learner-focused article provides practical AI workflows for NM technology students, residents, and fellows. It emphasizes preserving self-regulated learning, professional judgment, accountability, and source verification. Building on the theoretical foundations of part 1, this article treats AI as a structured learning partner rather than an educational authority. Direct evidence regarding NM learner outcomes remains limited. Nevertheless, broader health professions literature supports cautious AI exploration when learners prioritize verification, critical evaluation, and feedback over passive answer generation. Effective workflows require goal setting, initial independent effort, misconception identification, retrieval practice, self-explanation, source grounding, faculty review, and iterative revision. Furthermore, learners must avoid protected information, respect copyright, disclose AI assistance, and maintain skepticism toward generated output.
Keith M. Jacobs, J. Peacock· Journal of Nuclear Medicine...· 0 citations
: Artificial intelligence (AI) is increasingly transforming healthcare and creating new competency requirements for future nurses. This narrative review examines the applications, benefits, challenges, and curriculum implications of integrating AI into nursing education. Current evidence shows that AI can support personalised learning, intelligent tutoring, automated feedback, teaching preparation, virtual patients, and simulation-based training. These applications may improve students’ knowledge acquisition, engagement, communication, clinical reasoning, decision-making, and readiness for practice. However, AI integration also raises concerns about inaccurate or fabricated information, algorithmic bias, patient privacy, data security, academic integrity, accountability, and excessive dependence on automated tools. Differences in faculty readiness, institutional resources, and access to technology may further limit effective and equitable implementation. Preparing future nurses for AI-enabled healthcare therefore requires more than technical skills. Nursing students need AI literacy, critical evaluation skills, information verification ability, ethical awareness, data protection knowledge, and strong clinical judgement. AI content should be progressively integrated into nursing informatics, ethics, research, clinical, and simulation courses, supported by faculty development, clear institutional policies, and assessment methods that evaluate students’ reasoning processes. Future research should use longitudinal, experimental, and multicentre designs to examine the long-term effects of AI on clinical competence, patient safety, and professional practice. AI should ultimately enhance rather than replace nurses’ clinical judgement, ethical responsibility, communication, and compassionate care.
Zhang Nan· Advances in Vocational and T...· 0 citations
Evidence indicates a transition from isolated AI educational initiatives toward competency-based and longitudinal curriculum integration and effective AI education extends beyond technical literacy and increasingly incorporates ethical, clinical, and professional competencies necessary for responsible AI adoption in healthcare.
M. Morales-Cevallos, Diego Fabián Vique López, Catherine Hortensia Martínez Avalos et al.· Frontiers in Medicine· 0 citations
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