Topics
What is becoming more concrete in the project – from user needs and requirements to prototypes, evaluation, governance and services.
This topic explains not merely that work happened, but which substantive results, decisions and open questions emerged from it.
Guiding questions
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Editorially highlighted posts that provide useful entry points into the central questions of this topic.
At the start of the funded project phase, the public IncluLearn.AI portal is available at inclulearn.owli-ai.com. It deliberately separates public project information, the synthetic demo and the future private pilot path.
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.
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At the start of the funded project phase, the public IncluLearn.AI portal is available at inclulearn.owli-ai.com. It deliberately separates public project information, the synthetic demo and the future private pilot path.
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.
The TTS path has moved well beyond the first September state: the engine-neutral API now runs with Qwen3-TTS-1.7B, six neutral preset profiles and VoiceDesign on the target VM and is reachable through the public test demo. This is real integration, but not yet an audio-quality or production approval.
The public prototype is not the finished AI pipeline. It is an early working model for materials, review, release and accessible use. The important foundations are user goals, accessibility-first design, DE/EN and a clear distinction between demo and real functionality.
Public descriptions of media-centre workflows already provide concrete evidence about effort, bottlenecks and quality requirements. IncluLearn.AI nevertheless treats the resulting needs as hypotheses until they have been validated with practice partners and users.
IncluLearn.AI deliberately separates AI involvement, subject review and approval. Human ownership, risk-based review, provenance and cautious data classification are intended to make AI useful without transferring responsibility to the system.
IncluLearn.AI's technical foundation is being built reproducibly and with clear responsibility boundaries. The public test demo can be deployed automatically, while remaining deliberately separate from later production operation with real data and stronger security requirements.
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.