KMITL

Permanent URI for this communityhttps://dspace.kmitl.ac.th/handle/123456789/1

Browse

Settings

Access type: UnknownAuthor: search.filters.author.Hong, Hui

Search Results

Now showing 1 - 4 of 4
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    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.
  • Loading...
    Thumbnail Image
    Some of the metrics are blocked by your 
    consent settings
    Item type:Item,
    Study on Learner Comprehensive Evaluation Model Driven by Multi-Source Process Data
    (2023-01-01)
    Hong, Hui
    ;
    Viriyavejakul, Chantana
    It is of great significance to develop an effective multimedia network education platform. This paper aims to build an analysis and evaluation model of online learning behavior based on formative evaluation. The system is divided into three modules: behavior acquisition, behavior statistical analysis and learning analysis. The study of online learning behavior is a part of online education research. It is an important basis for designing adaptive network learning platform and learning resources. By learning the characteristics of the analyst process, such as time management, interference control, attention retention and content processing strategies. The research shows that based on the analysis of common quantitative parameters of online learning behavior, the effectiveness of the model in supporting students’ reading evaluation and reflection has been verified. People have a clearer and deeper understanding of online learning behavior. This study is an effective evaluation of the development of effective educational resources, the organization of teaching and teaching courses, and the effective evaluation of learning resources and learners.