In narrative AIED, we employ a library of narrative templates derived from classic literature, including Aesop’s fables, the Panchatantra, and Grimm's fairy tales.
These templates serve as FILL-IN-THE-SLOT FRAMEWORKS for story generation, allowing the LLM to customize narratives based on:
Pupil's Profile:: Personal interests, preferences, and learning needs.
Knowledge State:: Current mastery level in various subjects.
Learning Objectives:: Specific curricular goals targeted during the session.
The concept of Non-Player Educators (NPEs) extends the idea of AI agents serving as interactive educational companions. In the NP system:
Role Modeling: NPEs can embody various characters or personas within stories, modeling behaviors and conveying lessons.
Diversity of Perspectives: Multiple NPEs can provide different viewpoints, enriching the educational content.
Consistency and Patience: As AI-driven entities, NPEs offer consistent interactions and infinite patience, accommodating the child’s pace.
In technical terms, NPEs are realized by means of individual Low-Rank Adaptors (LoRA) which are loaded and unloaded atop the underlying language model.
A human-machine peer learning curriculum (i.e., a HMPL-C) is a planned sequence of educational instructions–i.e., a curriculum–which involves:
1. At least one human learner G, H, I, ... which gradually develops her/his/their skill Γ.
2. At least one artificial learner a, b, c, ... which gradually develops its/her/his/their skill σ.
3. Activities by means of which G (resp. H, I, etc.) develops her/his/their skill Γ, which directly involve knowledge and competence exhibited by a (resp. b, c, etc.).
4. Activities by means of which a (resp. b, c, etc.) develops her/his/their skill σ, which directly involve knowledge and competence exhibited by G (resp. H, I, etc.).