The Dual Faces of Deep Learning in ELT: Meaningful and AI-Driven Approaches

Hasbi, Muhamad (2025) The Dual Faces of Deep Learning in ELT: Meaningful and AI-Driven Approaches. UNSPECIFIED.

	Text Chapter 1 Muhamad Hasbi.pdf Download (365kB)
	Text Chapter 1 Muhamad Hasbi.pdf Download (365kB)

Abstract

Deep Learning in ELT has evolved in recent years to embody a duality of meaning. The first type is the in-depth or meaningful learning grounded in pedagogy emphasizing the importance of active engagement, critical analysis, and meaningful application, advocating for mindful, meaningful, and joyful learning to foster deeper understanding and motivation in students (Feriyanto & Anjariyah, 2024; Marton & Säljö, 1976). The second one is AI/machine-driven learning which uses artificial intelligence tools, virtual reality (VR), adaptive learning, personalized feedback, and so forth to create immersive, student-centered environments that enhance language proficiency and critical thinking through the intervention of technology advancement (Jawaid et al., 2025; Sujinem, 2025). On the meaningful side, ELT scholars have called for moving beyond shallow tasks (rote memorization, mechanical drills) to tasks that engage students critically, link to real life, and foster reflection (Ricita et al., 2025). On the AI/machine side, the ELT field is increasingly incorporating technologies such as chatbots, grammar‐correction algorithms, and speech recognition, repositioning teacher roles and student interactions in AI-assisted environments (Thadphoothon, 2022). Thus, deep learning in ELT now refers to both meaningful pedagogical engagement and driving technological (AI/machine) supports, and the tension and opportunities between them shape current research and practices.

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