A pedagogically guided LLM framework for scaffolding human-AI cocreation and preserving learners' agency and creativity

arXiv:2610.33051v1 Announce Type: new Abstract: Large language models (LLMs) can generate alternatives, elaborate ideas and supply rationales, overlapping with cognitive work learners are expected to perform. However, human-AI cocreation still lacks a mechanism for determining how AI can scaffold learners' creative decision-making. This study develops a cognitive-pedagogical framework for deriving the behaviour of a pedagogically constrained LLM from an educator-approved specification of each activity in a cocreative process in project-based learning. The creative-process phase supplies the activity's purpose, typical processes and pedagogical conditions; the active section of a creative technique specifies how the group works on its ideas. For each task, an educator approves four fields: a cognitive and metacognitive objective, a pedagogical objective, the task and group artefact as context, and the learner-owned product. The objective identifies the creative operations and decisions about progress, focus, criteria and next steps that remain with the group. Four translation rules derive permitted support, timing, feedback scope and prohibitions. The framework is illustrated with Viacocrea's six phases and two activities: Plan Brainstorming and a constructed Build activity using PPCo (Pluses, Potentials, Concerns, Overcoming concerns). In the proposed workflow, a controller applies the approved specification to student requests and feedback offers. Prompt engineering and approved pedagogical guidance retrieved through retrieval-augmented generation (RAG) inform a draft, whi

Framework preserves learners' agency and creativity

SOURCE     arXiv cs.CY (Computers and Society) (tier 1)
   ↓
DOCUMENT   f7d90c25-bc12-4871-9e67-ce1683cad535
           https://arxiv.org/abs/2610.33051
   ↓
EVIDENCE   2 extracted excerpts
   ↓
MODEL RUN  1 run, methodology 1.0.1
   ↓
SCORE      +50  (Favourable)
   ↓
CONFIDENCE 80%