Scaling Complex Thinking: A Conceptual Framework for AI-Supported Inquiry-Based Learning
TL;DR
A holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning is proposed, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills.
Abstract
As higher education faces the realities of technological advancements, institutions face the challenge of fostering high-level cognitive competencies while maintaining scalability. This paper proposes a holistic framework that integrates inquiry-based learning (IBL) with artificial intelligence (AI) to support learning. While traditional IBL is pedagogical and resource-intensive, the proposed model utilizes AI as a scaffolding layer, transitioning its use from an output generator to a metacognitive coach. Utilizing a conceptual framework methodology, the study maps the approaches between the stages of inquiry and AI interactions. In addition, the paper explores the institutional implications for teacher training and technological governance, arguing that the sustainability of such a system depends on shifting assessment from content mastery to measurable complex thinking skills. This contribution provides a cohesive foundation for administrators and educators seeking to implement active pedagogies in this era of AI.