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Generative AI-Assisted Reflective Writing for Improving Students' Higher Order Thinking: Evidence from Quantitative and Epistemic Network Analysis

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Title: Generative AI-Assisted Reflective Writing for Improving Students' Higher Order Thinking: Evidence from Quantitative and Epistemic Network Analysis
Language: English
Authors: Ching-Yi Chang, Hui-Chen Lin, Chengjiu Yin, Kai-Hsiang Yang
Source: Educational Technology & Society. 2025 28(1):270-285.
Availability: International Forum of Educational Technology & Society. Available from: National Yunlin University of Science and Technology. No. 123, Section 3, Daxue Road, Douliu City, Yunlin County, Taiwan 64002. e-mail: journal.ets@gmail.com; Web site: https://www.j-ets.net/
Peer Reviewed: Y
Page Count: 16
Publication Date: 2025
Document Type: Journal Articles
Reports - Research
Education Level: Higher Education
Postsecondary Education
Descriptors: Artificial Intelligence, Thinking Skills, Skill Development, Reflection, Writing (Composition), Influence of Technology, Student Attitudes, Empathy, Allied Health Occupations Education, College Freshmen, Computer Software, Synchronous Communication, Technology Uses in Education
DOI: 10.30191/ETS.202501_28(1).TP03
ISSN: 1176-3647
1436-4522
Abstract: The emergence of Generative AI such as ChatGPT--a cutting-edge pre-trained language model--has garnered unprecedented attention from educational researchers worldwide. Its remarkable performance in knowledge generation and natural language dialogue has sparked global scholarly interest in its implications for education. Research on higher-order thinking skills (HOTS) has predominantly been conducted in K-12 education. However, despite our aging society, there has been scant investigation into students' HOTS regarding supportive caregiving for elderly adults, particularly from the perspective of Embodied Cognition Theory (ECT). This study examined the potential of ChatGPT to enhance students' reflective writing skills. We investigated how ChatGPT assists students in exploring "Conflicts of Roles in Home Care for the Elderly" through reflective writing to understand their perceptions of caregiving and analyze their HOTS. A quasi-experiment was conducted to compare learning attitudes and empathy between an experimental group and control group. Students' reflective writing was coded into six categories. The results indicated that students using the ChatGPT-integrated ECT learning method demonstrated significantly better learning attitudes and empathy. Although there was no significant difference in HOTS scores between the two groups, the Epistemic Network Analysis (ENA) results revealed that the experimental group exhibited a more diverse and interconnected conceptual understanding. This study integrated ECT with ChatGPT and employed ENA, contributing to theoretical and practical adjustments in learning environment frameworks based on an analysis of students' understanding of the material.
Abstractor: As Provided
Entry Date: 2025
Accession Number: EJ1462645
Database: ERIC
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  Data: Generative AI-Assisted Reflective Writing for Improving Students' Higher Order Thinking: Evidence from Quantitative and Epistemic Network Analysis
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  Data: <searchLink fieldCode="AR" term="%22Ching-Yi+Chang%22">Ching-Yi Chang</searchLink><br /><searchLink fieldCode="AR" term="%22Hui-Chen+Lin%22">Hui-Chen Lin</searchLink><br /><searchLink fieldCode="AR" term="%22Chengjiu+Yin%22">Chengjiu Yin</searchLink><br /><searchLink fieldCode="AR" term="%22Kai-Hsiang+Yang%22">Kai-Hsiang Yang</searchLink>
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  Data: <searchLink fieldCode="SO" term="%22Educational+Technology+%26+Society%22"><i>Educational Technology & Society</i></searchLink>. 2025 28(1):270-285.
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  Data: International Forum of Educational Technology & Society. Available from: National Yunlin University of Science and Technology. No. 123, Section 3, Daxue Road, Douliu City, Yunlin County, Taiwan 64002. e-mail: journal.ets@gmail.com; Web site: https://www.j-ets.net/
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  Data: 16
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  Data: 10.30191/ETS.202501_28(1).TP03
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  Data: 1176-3647<br />1436-4522
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  Label: Abstract
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  Data: The emergence of Generative AI such as ChatGPT--a cutting-edge pre-trained language model--has garnered unprecedented attention from educational researchers worldwide. Its remarkable performance in knowledge generation and natural language dialogue has sparked global scholarly interest in its implications for education. Research on higher-order thinking skills (HOTS) has predominantly been conducted in K-12 education. However, despite our aging society, there has been scant investigation into students' HOTS regarding supportive caregiving for elderly adults, particularly from the perspective of Embodied Cognition Theory (ECT). This study examined the potential of ChatGPT to enhance students' reflective writing skills. We investigated how ChatGPT assists students in exploring "Conflicts of Roles in Home Care for the Elderly" through reflective writing to understand their perceptions of caregiving and analyze their HOTS. A quasi-experiment was conducted to compare learning attitudes and empathy between an experimental group and control group. Students' reflective writing was coded into six categories. The results indicated that students using the ChatGPT-integrated ECT learning method demonstrated significantly better learning attitudes and empathy. Although there was no significant difference in HOTS scores between the two groups, the Epistemic Network Analysis (ENA) results revealed that the experimental group exhibited a more diverse and interconnected conceptual understanding. This study integrated ECT with ChatGPT and employed ENA, contributing to theoretical and practical adjustments in learning environment frameworks based on an analysis of students' understanding of the material.
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        Value: 10.30191/ETS.202501_28(1).TP03
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      – Text: English
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        StartPage: 270
    Subjects:
      – SubjectFull: Artificial Intelligence
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      – SubjectFull: Thinking Skills
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      – SubjectFull: Skill Development
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      – TitleFull: Generative AI-Assisted Reflective Writing for Improving Students' Higher Order Thinking: Evidence from Quantitative and Epistemic Network Analysis
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