S2831
RTT - RTT education, training, and advanced practice
ESTRO 2026
translates from concept to practice, shaping a workforce prepared for scientific and clinical change. Keywords: RTT, Education, AI References: 1 Jones S, Thompson K, Porter BF, Shepherd M, Sapkaroski D, Grimshaw AS, Hargrave C.. Automation and artificial intelligence in radiation therapy treatment planning. Journal of Medical Radiation Sciences. 2023; doi: 10.1002/jmrs.7292 Vandewinckele L, Claessens M, Dinkla A, Brouwer C, Crijns W, Verellen D, et al. Overview of artificial intelligence-based applications in radiotherapy: Recommendations for implementation and quality assurance. Radiotherapy and Oncology. 2020;153:55–66. doi: 10.1016/j.radonc.2020.09.008 Digital Poster 2854 Enhancing Proton Therapy Education: Curriculum Revisions to Improve Workload Balance, Engagement, and Learning Outcomes for RTT Students Berit Bø 1,2 , Grete May Engeseth 1,3 1 Department of Life Sciences and Health, Oslo Metropolitan University, Oslo, Norway. 2 Department og Oncology, Oslo University Hospital, Oslo, Norway. 3 Cancer Clinic, Haukeland University Hospital, Bergen, Norway Purpose/Objective: Highly skilled RTTs are essential for proton therapy practice (1, 2) and in preparation for the introduction of proton therapy in Norway (2025), Oslo Metropolitan University established a Master’s-level module tailored to the competencies required for proton therapy (10 ECTS). This study aimed to apply and evaluate necessary revisions following the firstimplementation of the module. Material/Methods: Modifications were made to the original curriculum based on evaluations collected through questionnaires and feedback from focus groups. These were limited to structural changes, with the curriculum content and learning outcome requirements remaining the same. The course format was changed from session-based over three months to a continuous six-week teaching. The continuous teaching period made the portfolio assessment impractical, as such an assessment requires time for students to revise and improve their work progressively as part of a continuous learning process (3). The assessment was therefore changed to a coursework requirement consisting of an oral group presentation, followed by a written school exam. To promote deeper preparation and higher-quality laboratory performance, mandatory pre-lab
assurance processes. 1,2In response, the Australian Society of Medical Imaging and Radiation Therapy (ASMIRT) established a working group in RT who then established a multi-year workshop series to build AI capacity across the RT workforce.We evaluate outcomes and lessons learned from a three year national education initiative equipping radiation therapists (RTT) with skills in AI, emphasising applied learning, moving beyond theory toward practical, ethically grounded competence. The program aimed to foster innovation and readiness for tomorrow’s practice through collaboration between academia, industry and professional bodies. Material/Methods: We implemented an iterative program development approach across three years (2023-2025), refining curriculum based on participant feedback, facilitator insights, ambient AI, industry collaboration and emerging technological developments.1 Each iteration incorporated enhanced interactivity, practical applications through multi-disciplinary national and international partnerships, underpinned by Medical Radiation Practice Board of Australia (MRPBA), Therapeutic Goods Administration (TGA) and ASMIRT regulatory guidance. Results: Program participation increased yearly, doubling from 2024 to 2025 (n=50). The program evolved from foundational awareness sessions with embedded engagement to highly specialised, forward focussed workshops with expert multidisciplinary professional delivery, case study participation and Q&A with clinical and industry leaders. Key discoveries include: interactive formats consistently outperformed traditional lectureshands-on technology exposure was essential for realistic expectation settingpeer-to-peer learning created valuable professional insights and networksindustry partnerships enhanced practical relevance without compromising educational objectivityImplications: AI in radiotherapy requires constant course correction; listening to participant voices (including consumers), adapting to technological advances and discovering effective pedagogical approaches through experimentation. This adaptive model successfully transformed initial fear and skepticism into active engagement, creating a sustainable framework for emerging technology education. Conclusion: Iterative, collaborative education design produced measurable improvements in capability and confidence for RTTs engaging with AI in RT. By embedding participant feedback, cross sector partnerships, industry experts and aligning with professional standards, the program created a scalable model for future ready learning. This initiative shows how innovating radiation oncology together
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