Cognitive Amplification: Harnessing Artificial Intelligence to Augment Metacognitive Learning Strategies

Pang Yifan; Harwati Hashim; Nur Ehsan Mohd Said

doi:10.35516/Hum.2025.7967

Authors

Pang Yifan Faculty of Education, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia https://orcid.org/0009-0008-0788-5952
Harwati Hashim Faculty of Education, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia https://orcid.org/0000-0002-8817-427X
Nur Ehsan Mohd Said Faculty of Education, Universiti Kebangsaan Malaysia, Kuala Lumpur, Malaysia https://orcid.org/0000-0002-2891-327X

DOI:

https://doi.org/10.35516/Hum.2025.7967

Keywords:

Artificial Intelligence‎, metacognitive strategy‎, EFL‎, learning strategy.‎

Abstract

Objectives: In the realm of education, the integration of Artificial Intelligence (AI) holds promise for enhancing metacognitive learning strategies, which are vital for fostering self-regulated and reflective learning behaviors. This paper explores how Artificial Intelligence (AI) can enhance metacognitive learning strategies, crucial for self-regulated learning. Through ‘Cognitive Amplification,’ AI offers personalized feedback, schedules, and self-assessment tools to address learners' challenges in deploying these strategies effectively. Understanding AI's role in amplifying cognitive processes is vital for tailored educational interventions, improving engagement and outcomes.

Methods: Using qualitative methods, this study examines participants' experiences with AI tools for metacognitive learning, aiming to identify facilitators and barriers to AI integration in education.

Results: The study reveals a nuanced relationship between digital literacy levels and the impact of AI tools on learning. Low digital literacy participants initially faced frustrations but showed increased motivation with familiarity, while moderate and high literacy participants experienced significant benefits, particularly in self-regulated learning. Addressing diverse digital literacy needs is crucial for optimizing AI tools, with future research focusing on tailored interventions and user interfaces.

Conclusions: Key recommendations emphasize creating user-friendly interfaces, offering thorough onboarding for low digital literacy users, and implementing personalized feedback and structured study schedules, while addressing ethical concerns like data privacy and algorithmic bias to enhance AI-driven learning experiences.

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