enproject-progress2026-09Project organisationWP1CollaborationBefore the project start, central rules for collaboration, project steering, accessibility and AI use were structured, together with a concrete start plan for the first work package.Project progress9 September 2026 – 16 September 2026
Project progressArticlePreparation phase
Project organisation and working structure prepared
Before the project start, central rules for collaboration, project steering, accessibility and AI use were structured, together with a concrete start plan for the first work package.
Event
9 September 2026 – 16 September 2026 · documented retrospectively
Published
Evidence type
Own project report
This English version was machine-prepared and has not yet received language/subject review.
What was prepared?
IncluLearn.AI received a central, versioned working and knowledge base that brings together project goals, work packages, milestones, roles, decisions and project-wide rules.
Principles for accessible collaboration, AI assistance and the handling of sensitive data were prepared in parallel. Important project information is intended to be documented text-first wherever possible, while subject-matter and accessibility review remain human responsibilities.
Preparing WP1
A concrete start plan for the first weeks of the first work package was also prepared and linked to central project steering. This makes it possible to distinguish preparatory work from activities that will take place during the funded project period.
Context
These activities took place before the funded project period. They are organisational and methodological preparations and are not presented as completed project milestones.
From the needs hypotheses, we derived a first solution hypothesis: five core components with high strategic value, five subsequent production and integration services, and three specialised later options.
From the researched workflows, we derived twelve concrete needs hypotheses: from semantic preparation and expert review to reuse, examinations, provenance, output profiles and inclusive collaboration.
Public workflow descriptions point to high manual effort, long lead times, subject-specific transformation, established reuse practices and special examination requirements. For IncluLearn.AI, these are concrete signals of where semantic AI support may be useful.
Transparency about AI assistance
This post was researched or editorially prepared with AI assistance and reviewed by a human before publication.