From Theory to Practice: Integrating Generative <scp>AI</scp> ‐Powered Microlearning Into <scp>TESOL</scp>

ABSTRACT This article offers practical strategies for integrating microlearning and generative artificial intelligence (GenAI) into TESOL instruction. It positions GenAI‐powered microlearning as a promising theory‐informed approach to meet the evolving demands of language teaching and learning in diverse global contexts. It also advances a conceptual framework that links second‐language acquisition (SLA), cognitive load theory, multimodal learning, and responsible implementation conditions such as teacher mediation, accessibility, transparency, and data protection. It argues that AI‐enhanced interactive tasks can support engagement, autonomy, and equitable access to knowledge when grounded in an integrated conceptual foundation of SLA, cognitive load theory, and multimodal learning. It introduces a design framework that stresses concision, interactivity, accessibility, and feedback and treats critical AI literacy and teacher–learner agency as constitutive design concerns rather than afterthoughts. In this framework, critical AI literacy functions as the evaluative mechanism through which teachers and learners question, revise, or reject AI‐generated input and feedback. Examples demonstrate how AI tools can support vocabulary, grammar, and pragmatics.

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Publication Details

Journal
TESOL Journal
Published
2026-07-28
DOI
https://doi.org/10.1002/tesj.70153
Primary Topic
E-Learning and COVID-19
Type
article
Field-Weighted Citation Impact
0.00
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article

From Theory to Practice: Integrating Generative AI ‐Powered Microlearning Into TESOL

Lucas Kohnke
TESOL Journal
E-Learning and COVID-19
article

From Theory to Practice: Integrating Generative AI ‐Powered Microlearning Into TESOL

Lucas Kohnke
article en

Abstract

ABSTRACT This article offers practical strategies for integrating microlearning and generative artificial intelligence (GenAI) into TESOL instruction. It positions GenAI‐powered microlearning as a promising theory‐informed approach to meet the evolving demands of language teaching and learning in diverse global contexts. It also advances a conceptual framework that links second‐language acquisition (SLA), cognitive load theory, multimodal learning, and responsible implementation conditions such as teacher mediation, accessibility, transparency, and data protection. It argues that AI‐enhanced interactive tasks can support engagement, autonomy, and equitable access to knowledge when grounded in an integrated conceptual foundation of SLA, cognitive load theory, and multimodal learning. It introduces a design framework that stresses concision, interactivity, accessibility, and feedback and treats critical AI literacy and teacher–learner agency as constitutive design concerns rather than afterthoughts. In this framework, critical AI literacy functions as the evaluative mechanism through which teachers and learners question, revise, or reject AI‐generated input and feedback. Examples demonstrate how AI tools can support vocabulary, grammar, and pragmatics.

TESOL JournalVol. 17(3)
Education University of Hong Kong (HK)
Quality Education
Openalex Percentile: Top 2%
E-Learning and COVID-19
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