The Algorithmic Co-Thinker: A Generative AI Framework for Persona-Aware Computational Mathematics
Allan M. Canonigo
Source abstract
The integration of Computational Thinking (CT) into mathematics education frequently encounters challenges within Digital Mathematics Learning Environments (DMLEs), which often adopt static, one-size-fits-all approaches that fail to adequately support learner diversity. While prior studies have identified distinct learner personas—such as the Frustrated Perfectionist and the Conceptually Stalled Learner—a critical barrier remains in scaling personalized, real-time pedagogical support. This study proposes a novel framework utilizing Generative AI (GenAI) to transform DMLEs into dynamic “co-thinkers.” Employing a two-phase design-based research methodology, we first empirically validated a four-persona typology through qualitative analysis and quantitative behavioral signatures. Subsequently, we developed and expert-validated a conceptual GenAI scaffolding prototype, comprising structured prompt templates engineered to deliver metacognitive and affective support tailored to individual persona needs. Preliminary expert validation confirms pedagogical soundness and highlights key ethical considerations. The paper presents this integrative framework alongside a detailed discussion of its design principles, operative AI cycle, and embedded ethical safeguards, positioning persona-aware AI as a transformative, yet responsible, pathway for computational mathematics education.
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