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All Journal International Journal of Evaluation and Research in Education (IJERE) Emerging Science Journal
Viriyavejakul, Chantana
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Education Environmental Science

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Perceptions of the generative AI-enabled cognitive offload instruction in English writing Hong, Hui; Vate-U-Lan, Poonsri; Viriyavejakul, Chantana
International Journal of Evaluation and Research in Education (IJERE) Vol 14, No 3: June 2025
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.11591/ijere.v14i3.33138

Abstract

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.
Problem-Based Blended Training via Chatbot to Enhance the Problem-Solving Skill in the Workplace Saengrith, Waristha; Viriyavejakul, Chantana; Pimdee, Paitoon
Emerging Science Journal Vol. 6 (2022): Special Issue "Current Issues, Trends, and New Ideas in Education"
Publisher : Ital Publication

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.28991/ESJ-2022-SIED-01

Abstract

Problem-solving skill is one of the soft skills that has become essential for employees in various organizations. Training model and educational technology were considered key success factors in delivering knowledge for personnel in the workplace to develop this skill. Problem-Based Learning (PBL) is a key driver for learning activities, which has been increasingly adopted for workplace training and has proven to be one of the best approaches to helping learners improve their problem-solving skills in the organization. Hence, this research aims to synthesize problem-based blended training via chatbot to enhance problem-solving skills in the workplace. Literature review, document analysis, and focus group technique were used as the main procedures for the first phase of model synthesis. The effectiveness of the training model was examined in the second phase by applying it to 20 employees of the flexible lamination manufacturers in Thailand from purposive sampling. The training was held for four weeks and examined with a problem-solving skill test. In addition, a follow-up test has been conducted to monitor retention skills after a four-week training period. Data analysis used the repeated-measures ANOVA test with normality and homogeneity as a prerequisite test. This study shows that the problem-based blended training model via chatbot to enhance problem-solving skills in the workplace comprises six main steps: (1) Group identification; (2) Problem identification; (3) Idea creation; (4) Learning; (5) Implementation; (6) Evaluation. The results on the implemented training model showed that problem-solving skills after training were significantly higher than those before training, and the retention of skill remained higher than that before training and did not significantly change after finishing training at a statistical significance of 0.5. As a result, the developed model is highly appropriate for implementation, particularly because the chatbot platform is involved in almost every step of this training model to accommodate learners who can easily access the training platform, repeat the training content, and feel motivated to explore new information to improve their problem-solving skills. In a post-COVID-19 period with distancing required in the workplace, this model is applicable to deliver efficiency in workplace training. Doi: 10.28991/ESJ-2022-SIED-01 Full Text: PDF
Co-Authors Hong, Hui Pimdee, Paitoon Saengrith, Waristha Vate-U-Lan, Poonsri