ASEAN Journal of Education

Treats and Threats of Generative Artificial Intelligence (GenAI) in Education

2024-10-23T14:25:25+07:00

Generative artificial intelligence (GenAI) has developed rapidly and influenced many fields, including education. This technology presents both opportunities and challenges for teachers. While GenAI offers possibilities to enhance teaching through individualized learning and administrative efficiency, significant concerns remain. This study undertakes a thematic analysis of six relevant research papers, selected from a pool of nearly 18 based on criteria such as relevance to GenAI's impact on education, empirical foundation, and recency (2018–2023). The papers were sourced from reputable educational databases. Themes were identified by coding and categorizing data from each paper's findings to examine both the advantages GenAI provides to teachers and the drawbacks and potential risks it poses for educators. The analysis yielded key themes representing the benefits and risks of GenAI in education. Four primary advantages were identified: (1) increased productivity, (2) assistive instruction, (3) enhanced teaching strategies, and (4) effective teaching and learning. Conversely, four major risks emerged: (1) privacy and security challenges, (2) biases and discrimination, (3) issues of accuracy and quality, and (4) potential job displacement. Each theme carries significant implications for educators, students, and policymakers seeking to integrate GenAI responsibly into educational settings.

Bridging Perceptions through Relationship: Attitudes Towards Generative AI in Thailand’s International Schools

2024-11-19T11:24:12+07:00

This study examined perceptions and attitudes towards generative artificial intelligence among teachers and students in international schools in Thailand, addressing two primary objectives: (1) to explore perceptions and attitudes, and (2) to analyze relationships among key variables influencing AI acceptance. Using a quantitative methodology, self-administered online surveys comprising 36–37 items targeting core constructs were distributed to both groups. A total of 74 teachers and 112 students participated, yielding response rates of 96.10% and 92.56%, respectively. The research instrument demonstrated high reliability, with Cronbach’s α = .92. For the first objective, descriptive statistics indicated generally positive attitudes towards generative artificial intelligence. Teachers emphasized its potential to enhance learning efficiency, while students expressed greater confidence in its capacity to support learning outcomes. Notably, students reported higher scores for Perceived Ease of Use (PEOU) compared to teachers. Regarding the second objective, Pearson’s product-moment correlation revealed that Social Influence was the strongest predictor of Behavioral Intention (BI) (r = 0.699**), followed by Attitude (r = 0.678**) and Educational Compatibility (r = 0.663**). Perceived Usefulness (PU) and PEOU exhibited moderate correlations with BI (r = 0.509** and r = 0.500**, respectively). A strong relationship between PU and PEOU (r = 0.702**) highlighted their interdependence. These findings underscore the crucial roles of social influence, individual attitudes, and educational compatibility in promoting AI adoption within educational contexts, offering valuable insights for the integration of generative AI into international school curricula in Thailand.

Core Competencies of Early Childhood Education Administrators in Thailand

2026-06-23T10:52:57+07:00

This study examines the core competencies required of early childhood education administrators in Thailand within the broader context of strengthening leadership capacity and enhancing educational quality. While international scholarship consistently highlights the influence of leadership competencies on educational effectiveness and child development outcomes, Thailand currently lacks a competency-based framework for the preparation and selection of early childhood education leaders. Employing a multi-method design, the study integrated document analysis, regional focus group discussions, expert interviews, and third-order Confirmatory Factor Analysis (CFA) to identify competency domains, assess developmental needs, and propose leadership development guidelines aligned with national standards. Findings revealed three interrelated competency domains: adaptive management of early childhood development centers, academic leadership in early childhood education, and professional development toward excellence. Needs assessment results indicated that knowledge acquisition represented the highest developmental priority, particularly in relation to inclusive education management. The proposed competency model demonstrated strong empirical validity and model fit (χ²/df = 1.139, CFI = 0.997, RMSEA = 0.017). The study further suggests that competency development should emphasize practice-based professional learning, mentoring systems, and collaborative professional networks. These findings contribute to ongoing international discussions concerning leadership professionalization, administrator preparation, and competency-based development frameworks in early childhood education.