From answer engines to learning partners: A dual-ZPD design framework for AI-supported learning
Abstract
Generative AI’s function as a frictionless ‘answer engine’ creates a paradox in educational HCI: the very tools that can enhance intellect may also weaken it by allowing users to circumvent crucial cognitive processes. This risks creating a ‘hollowed mind’—knowledge that is broad but superficial, and a user experience that diminishes learner agency. The convenience of cognitive offloading introduces a motivational challenge that traditional cognitive scaffolding cannot address. We argue that designing genuine human-AI partnerships in learning requires moving beyond cognitive support to motivation-aware scaffolding. This paper provides a toolkit for building motivation-aware AI systems. At its core is the Dual Zone of Proximal Development (DZPD), a conceptual framework building on foundational work in educational psychology. We introduce an overarching design principle, concrete design principles, illustrative archetypes, and examples of measurable indicators. These conceptual tools offer essential guidance for the next wave of empirical HCI research in education.
