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Author: search.filters.author.Vate-U-lan, Poonsri

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    Perceptions of the generative AI-enabled cognitive offload instruction in English writing
    (2025-06-01)
    Hong, Hui
    ;
    Vate-U-lan, Poonsri
    ;
    Viriyavejakul, Chantana
    This study examines the students’ perceptions of the generative artificial intelligence (AI)-enabled cognitive offload instruction and its effectiveness in improving their critical thinking skills in writing English essays. This qualitative research collects data from 120 students through focus group discussions and is analyzed by Word Clouds to generate a visual representation of the word frequencies. The findings reveal that generative AI-enabled cognitive offload instruction had: i) an impact on critical thinking and writing skills; ii) effective features of Skywork, ability to generate relevant prompts and provide constructive feedback; iii) use of Skywork in developing stronger arguments; iv) promoting critical examination of different perspectives; v) interactive nature and motivation; vi) enhanced analytical skills; vii) impact on essay structuring and organization; viii) feedback and revision process; and ix) transferability of critical thinking skills. This study concludes that the highest frequency was Skywork, ability, writing, feedback, evidence, skills, thinking, arguments, essays, and peers. Students recommend in-depth explanations for complex topics, advanced tutorials, regular updates, collaboration features, advanced modules, and personalized learning paces to enhance Skyworks’s integration into instruction.
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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.