Language learning is fundamentally about decomposing communicative intent into teachable components. But what if we could use LLMs not just to teach, but to understand what learners actually know, and automatically generate personalised learning paths from that understanding? This talk takes you behind the scenes of a large-scale knowledge graph project that uses carefully engineered prompts to extract granular learning objectives from content, map their dependencies, infer learner mastery from free-form production, and generate bespoke learning pathways. In 30 minutes, we'll cover: - What a Knowledge Graph is (and why does an EdTech company like Busuu need one?) - How we tagged our extensive course library - How we map connections between separate knowledge components - How we measure learners' abilities from both course exercises and free-form production - How we create bespoke Learning Pathways Prompts in EdTech are not just for content generation. The most valuable prompts in this pipeline are ones that teach LLMs to reason like domain experts (linguists, educators, assessment specialists), not ones that ask them to produce content.
Hannah originally joined Busuu in 2019 as an education expert creating English learning content, but as the product as developed she now oversees the development of AI features across languages. She leads a team of Computational Linguists and Learning Designers who work collaboratively with Busuu's product team and AI engineers to develop prompts for features to support language learners in their journey.
