HG-MAQG: Heterogeneous Graph Constrained Multi-Agent System For Personalized Question Generation

High-quality educational questions are essential for diagnosing students’ knowledge states and facilitating personalized instruction, making question generation a crucial task in intelligent education systems and pedagogical agents. However, existing methods primarily emphasize linguistic fluency and template reproduction, limiting their ability to jointly support question novelty, learner-state adaptation, and cognitive progression. Recent multi-agent approaches designed to address these limitations lack a shared structured evidence base, resulting in inconsistent reasoning and error propagation across agents. To address these challenges, we propose HG-MAQG, a heterogeneous graph constrained multi-agent system for personalized question generation. HG-MAQG encodes learner states, concept prerequisites and question relations in a heterogeneous graph to ground agent reasoning in structured, shared and traceable evidence. Building on this representation, a diagnosis-driven cognitive progression mechanism uses concept-specific mastery estimates from knowledge tracing to adjust target Bloom levels, lowering cognitive demands to address mastery gaps, maintaining them to consolidate current-level competence, or raising them to introduce the next level of reasoning. The resulting cognitive targets and retrieved graph evidence jointly constrain question generation, while validation of concept coverage, cognitive alignment and novelty guides iterative refinement, with validated questions incorporated into the graph to provide additional references for subsequent generation. To evaluate cognitive alignment and learner-specific differentiation, we introduce Bloom Accuracy and Personalization Gap beyond conventional question generation metrics. Evaluation across six metrics on Eedi and XES3G5M demonstrates proposed HG-MAQG effectiveness in personalized question generation.