Project Activities
The proposed SkillTree will be an AI-based system that will deliver an automatically generated visual representation or ‘map’ of students’ knowledge, skills, and interest within a specific domain–specifically computer science for this project. Leveraging advancements in industry AI research from the last few months with novel approaches—such as knowledge graphs, network databases, Chain-of-Thought (CoT), and Retrieval Augmented Generation (RAG)—this system will provide a personalized snapshot of a students’ real-time knowledge and experience. Inspired by the design of skill trees commonly found in action-adventure and role-playing video games, as students perform new learning tasks their personal SkillTree will ‘light up’ or ‘level up’ new nodes in their network based on their demonstrated expertise.
SkillTree will solve a perennial problem in education by supporting how teachers know each individual students’ prior knowledge, expertise, interests, and experiences. With SkillTree’s assistance, a teacher can refine their teaching strategies. SkillTree will also contribute to persistent educational AI technology bottlenecks by forcing an LLM to use an independent underlying model of knowledge within a domain to draw its inferences. This underlying model can be readily validated by experts and educational standards in the field.
The research and development strategy will involve two activities: a comprehensive, audience Alignment Study and the development of a functional system prototype. These activities will result in three deliverables: a proof-of-concept report, a Phase Two R&D Plan, and an early-stage prototype.
People and institutions involved
IES program contact(s)
Project contributors
Questions about this project?
To answer additional questions about this project or provide feedback, please contact the program officer.
