DEGRO consensus framework for undergraduate radiation therapy teaching in Germany: a white paper with integrated...

Undergraduate medical education is increasingly competency-based, digital, and interprofessional in accordance with the National Competence-Based Learning Objectives Catalogue for Medicine (Nationaler Kompetenzbasierter Lernzielkatalog Medizin, NKLM). Against this background, a nationally relevant, consensus-based framework for undergraduate radiation therapy education in Germany was developed by an expert panel of faculty representatives, aligned with the competencies expected at the end of the practical year (PJ) and in the final state examination context (M3), and complemented by guidance on the responsible educational use of artificial intelligence (AI).

The framework was prepared by the German Society of Radiation Oncology (Deutsche Gesellschaft für Radioonkologie, DEGRO) working group "Medical Education" (AG Lehre), including a consolidated master document and 16 teaching portfolios that informed 15 candidate competencies. In a second step, 25 formally delegated representatives from 22 of the 36 university radiation oncology departments in Germany participated in a structured expert consensus process incorporating individual prioritization, small-group refinement, plenary consolidation, predefined voting thresholds, and postworkshop editorial integration.

The resulting framework comprised four components: (1) nine prioritized core competencies defining a national minimum standard at the PJ/M3 level; (2) implementation bandwidths (minimal, recommended, best practice) anchored in anonymized site-profile data; (3) a staged A/B/C assessment model aligned with the Miller pyramid; and (4) integrated AI guidance defining permitted use cases, nonnegotiable boundaries, AI literacy goals, and a practical educator-facing implementation logic. The tumor board was identified as a particularly suitable integrative teaching format.

This white paper presents a nationally coordinated, competency-oriented framework for undergraduate radiation therapy education that combines a stable core with scalable local implementation options. By linking essential competencies, teaching bandwidths, staged assessment, and responsible AI guidance, greater consistency, coherence, and practical implementation of radiation therapy teaching across heterogeneous faculties is facilitated.