The field of neuroeducation, which integrates neuroscience findings into educational practice, has gained significant attention in recent years. Establishing research priorities in neuroeducation is crucial for guiding future studies and ensuring that the field benefits both neuroscience and education. This study aimed to address the need for collaboration between neuroscientists and educators by conducting a priority-setting exercise with early career professionals from both fields. Using the nominal group technique (NGT) with interquartile range (IQR) analysis, we identified seven key priorities in neuroeducation and assessed the level of consensus on these priorities. The top-ranked priorities were “Emotional and Mental Well-being”, “Neurodiversity and Special Education Needs”, and “Active and Inclusive Teaching Methods”, though IQR analysis revealed varying levels of consensus. Lower-ranked priorities, such as “Role of Technology on Learning and the Brain”, showed a higher consensus. This discrepancy between ranking and consensus highlights the complex nature of neuroeducation, reflecting differing perspectives between neuroscientists and educators. These findings suggest the need for interdisciplinary collaboration to bridge these gaps and foster evidence-based practices. We recommend that future research focuses on the specific neural mechanisms underlying emotional well-being, strategies for supporting neurodivergent learners, and practical approaches to integrating inclusive teaching methods in diverse educational contexts.

The growing scale of neuroimaging datasets in contemporary neuroscience has intensified challenges in data management, processing standardisation, and reproducible workflows. DevOps practices from software engineering offer promising solutions to enhance reproducibility as neuroimaging research scales. This narrative synthesis examined DevOps integration in structural neuroimaging workflows through systematic searches of IEEE, Scopus, PubMed, and ArXiv (2016 - 2025), following PRISMA guidelines with AI-assisted data extraction. Analysis of 38 studies identified four key themes: automated pipelines and containerisation; quality control frameworks; deep learning integration with scalability challenges; and multi-site harmonisation. Studies demonstrated technical advances in reproducible, version-controlled workflows, yet highlighted significant adoption barriers, including requirements for computational expertise, institutional infrastructure limitations, and cultural reluctance towards transparent automated practices. Based on this evidence, we propose a four-domain implementation framework: (1) Technical Readiness (containerisation, version control), (2) Methodological Rigour (automated quality control and validation), (3) Open Code Transparency, including reproducible deep-learning pipelines and community-driven maintenance, and (4) Cultural and Institutional Support (training, collaborative practices, sustained funding). Successful DevOps adoption requires continuous improvement across all domains with commitment and support from researchers, institutions, and funding bodies. This synthesis provides evidence-based guidance for modernising neuroimaging workflows while strengthening scientific rigour.