- To develop a pedagogical framework that is adapted to social media, which includes a set of pedagogical principles that work in open-ended collaborative learning contexts in which the distinction between teacher and learner is blurred and where we think instead of an ecology of knowledge, practice and learning
- To develop sophisticated tools for the real-time analysis of performance practice and compositional work through audio and gesture analysis, musical analysis, and corpus analysis.
- To develop community-building tools that place users at the center of a supportive network of learners, which includes novel interfaces for communities of users to share media, tagging and analysis of each other's content.
- To develop natural interfaces for learning agents and natural language description of music analysis that can be used for talking about music and providing text based praise and feedback.
- To develop computational mechanisms for representing learning strategies, for configuring and editing them, and for enacting these strategies embedded in specific learning contexts.
- To integrate all these components into a music practice agent that provides a model of the experience and the knowledge of the learner as well as the learner's goals and a learning strategy. To develop an architecture with capabilities and diagnostics that can monitor the performance of a learner in real time. To develop an underlying architecture that models how best an agent learns, which can be interrogated by the learner and by other PRAISE users.
- To test evaluation criteria for competences acquired in open learning environments in relation to standard curricula.