ASM 2025 in Focus: AI, Assessment and Agency in Health Professions Education
Notice bibliographique
Résumé
In this editorial, we reflect on the successes and lessons learned from ASM 2025. Over 3 days, we explored the three A's of ASME: AI, Assessment and Agency. Delegates came together to explore the transformative potential of artificial intelligence, the development of innovative assessment strategies and ways to maximise learners' agency throughout healthcare training. Our keynote speakers challenged us to think critically about the evolving roles of educators and learners, while encouraging bold, future-focused approaches to teaching and assessment. Beyond these keynotes, the meeting celebrated the breadth and creativity of contributions from across the field, reminding us that the future of health professions education goes far beyond the ‘A's, embracing a whole alphabet of possibilities. ASME ASM 2025 was our biggest meeting yet, with 4 inspiring keynote lectures, 9 thought-provoking symposia and 46 intra-conference sessions. The breadth of scholarship on display was remarkable: 347 e-posters, 193 oral presentations and 22 thought-provoking ‘What's Your Point?’ presentations. Together, these sessions reflected the energy, creativity and collaborative spirit that continue to drive innovation in health professions education. ASME ASM 2025 reflected the truly global nature of the health professions education community. Alongside our biggest number of delegates ever, from the United Kingdom, we were delighted to welcome contributions from across Europe, North America, the Middle East, Asia and the Pacific, as well as Africa. From Canada to New Zealand, India to Ireland and Singapore to Switzerland, the breadth of participation underscored the international collaborations and diverse perspectives that continue to enrich our field. We are especially proud to showcase this year's award-winning abstracts in this supplement. Among them, Dr. James Fisher (Newcastle University) received the Research Paper Award 2025 for his study “Onus or bonus? A qualitative analysis of the impact of speaking with a regional accent on the experiences of UK medical students”—a rigorous and nuanced piece of scholarship that sheds light on how language and identity shape the journey through medical education. Dr. Mark Glasbey (University of Birmingham) was awarded the TASME Teaching, Innovation and Excellence Award for his work ‘Ensuring Equitable Access to Cutting-Edge Medical Education: An Innovative, Open-Access AI-Driven Adaptive Platform’, showcasing how AI-driven, open-access tools can widen opportunities for learners. This supplement also features reports from our wider group of ASME Award Winners over the past year including recipients of the ASME Board Award, New Leaders Award, the Sir John Ellis Student Prize, two Education Innovation Awards and eight Small Grants. These showcase funded and award-winning research projects, which represent the breadth of scholarship across the health professions, which we are delighted to support through our portfolio of awards and research grants. In addition to our four keynotes, who have each contributed their perspectives to this supplement, we were energised by the breadth of abstracts showcased here. The quality and diversity of work shared at ASME ASM 2025 underline the creativity, rigour and collaborative spirit that characterise our field. We hope that this supplement offers not only a record of the ideas discussed at this meeting but also practical insights, new ideas and moments of inspiration. As you explore the abstracts and reflections from our keynote speakers, we invite you to consider how these themes might shape your own practice. Looking ahead, we look forward to continuing the conversation and celebrating more scholarly achievements at next year's ASM in Birmingham. ASM 2026, ‘Beyond Boundaries: Innovation, Inclusion and Impact in Health Professions Education’, promises to showcase bold ideas, inclusive practices and work that drives impact across the field of health professions education. For now, enjoy the supplement—and get thinking about stretching your boundaries for next year's ASM!
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,004 | 0,002 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,001 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».