Data-driven Learning Meets Generative AI: Introducing the Framework of Metacognitive Resource Use

Abstract

This paper explores the intersection of data-driven learning (DDL) and generative AI (GenAI), represented by technologies like ChatGPT, in the realm of language learning and teaching. It presents two complementary perspectives on how to integrate these approaches. The first viewpoint advocates for a blended methodology that synergizes DDL and GenAI, capitalizing on their complementary strengths while offsetting their individual limitations. The second introduces the Metacognitive Resource Use (MRU) framework, a novel paradigm that positions DDL within an expansive ecosystem of language resources, which also includes GenAI tools. Anchored in the foundational principles of metacognition, the MRU framework centers on two pivotal dimensions: metacognitive knowledge and metacognitive regulation. The paper proposes pedagogical recommendations designed to enable learners to strategically utilize a wide range of language resources, from corpora to GenAI technologies, guided by their self-awareness, the specifics of the task, and relevant strategies. The paper concludes by highlighting promising avenues for future research, notably the empirical assessment of both the integrated DDL-GenAI approach and the MRU framework.