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Author: search.filters.author.Hong, HuiSubject: search.filters.subject.Generative AI

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    Cognitive Offload Instruction with Generative AI: A Quasi-Experimental Study on Critical Thinking Gains in English Writing
    (2025-07-01)
    Hong, Hui
    ;
    Vate-U-Lan, Poonsri
    ;
    Viriyavejakul, Chantana
    This study explores the impact of generative AI-enabled cognitive offload instruction on the development of critical thinking skills in English essay writing among first-year university students. A quasi-experimental design was employed, comparing traditional instruction with an AI-augmented pedagogy that delegated lower-order writing tasks to generative AI tools, allowing students to focus on analysis, evaluation, and reflection. Over 12 weeks, 240 participants engaged in structured writing cycles involving AI brainstorming, individual critique, peer-AI co-revision, and reflective journaling. Results revealed that the AI-enabled cognitive offload group demonstrated significantly greater improvements in standardized critical thinking assessments and produced higher-quality essays in terms of logical coherence, evidence use, and originality. Mediation analysis indicated that cognitive offloading behavior partially explained the relationship between AI use and critical thinking gains. The findings suggest that when generative AI is integrated into pedagogy through deliberate scaffolding, it can enhance rather than hinder higher-order thinking. This study highlights the importance of balancing technological efficiency with instructional strategies that promote active engagement, metacognitive reflection, and collaborative learning. It offers practical implications for educators seeking to incorporate AI tools without compromising the development of essential cognitive skills, proposing that structured cognitive offload instruction can serve as an effective approach to fostering critical thinking in second-language writing contexts.
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    Generative AI–mediated scaffolds for enhanced critical thinking in EFL writing
    (2025-01-01)
    Hong, Hui
    ;
    Vate-U-lan, Poonsri
    ;
    Viriyavejakul, Chantana
    Generative AI tools present new opportunities for enhancing critical thinking (CT) in English as a Foreign Language (EFL) writing instruction. This study investigates how the structured integration of these technologies could support the development of CT skills, particularly in vocational education contexts. An eight-week multiple-case action research design was conducted across three vocational colleges, involving 92 students engaged in iterative writing and revision cycles guided by the GenAI-CT framework. This pedagogical model draws on Bloom’s taxonomy, Vygotsky’s Zone of Proximal Development, and cognitive apprenticeship theory to scaffold learners through increasingly complex reasoning tasks. Data were collected from student essays, AI interaction logs, reflective journals, and classroom observations. Mixed-methods analysis revealed statistically significant gains across all CT dimensions (p<.001). Thematic findings indicated notable increases in analytical depth, metacognitive reflection, and evaluative judgment. Variations across cases underscored the influence of disciplinary focus and instructional mediation styles. These results demonstrate that generative AI, when embedded in intentional pedagogical structures, can foster cognitive engagement rather than superficial automation. The GenAI-CT framework offers a replicable model for integrating AI in applied language and communication instruction, supporting educators in cultivating critical thinking through technology-enhanced learning environments.