International Journal of Educational Communications and Technology

Student Relationship Management with Adaptive AI

Kanyamaat Sangounsak — 2025-03-22

The integration of Adaptive Artificial Intelligence (AI) into Student Relationship Management (SRM) has emerged as a transformative strategy for enhancing student engagement, retention, and academic success. By leveraging AI-driven technologies, educational institutions can personalize student interactions, anticipate academic challenges, and optimize decision-making processes. This study examines the role of Adaptive AI in SRM, utilizing a systematic review and bibliometric analysis to synthesize existing research on AI-driven SRM frameworks. A conceptual structure for the integration of Adaptive AI into student management strategies is proposed, highlighting its potential to create a more responsive and student-centric educational environment.

The findings emphasize the advantages of data-driven decision-making, predictive analytics, and personalized support mechanisms in improving institutional efficiency and student outcomes. Furthermore, this study explores key challenges associated with the implementation of AI in SRM, including technological constraints, ethical considerations, and data privacy concerns. As AI continues to shape the future of higher education, understanding its implications is critical for educators, policymakers, and institutional leaders. This research contributes to the growing body of knowledge on AI-enhanced SRM and provides valuable insights into best practices for integrating Adaptive AI into student support services. The study underscores the need for a strategic, data-informed approach to SRM, ensuring that AI-driven solutions align with institutional goals and student needs.

Design of a Workshop Training Course based on the Sufficiency Economy Philosophy for Sustainable Electricity use for Provincial Electricity Authority Employees

Thanachai Rungruang — 2025-03-22

The study aimed to assess the effectiveness of a workshop training course on social innovation or the sufficiency economy (SEP) principles for sustainable innovation (SI) of sustainable electricity use among provincial electricity authority personnel. The results revealed that the quality of the content (M = 4.78, SD = 0.58) and the media (M = 4.50, SD = 0.58) of a workshop training course based on the SEP for sustainable electricity use of provincial electricity authority personnel by the 9 experts was at the greatest level. The design of a workshop training course based on SEP for SI of sustainable electricity use for provincial electricity authority personnel to enhance learning achievement was efficient by E₁/E₂ (82.40/81.33) by the specified hypothesis 80/80. The significant difference between pretest (M = 14.44, SD = 0.88) and posttest (M =16.11, SD = 1.05) scores of a workshop training course based on SEP for SI of sustainable electricity use for provincial electricity authority personnel were at the 0.01 level. Then, the provincial electricity authority personnel's satisfaction with a workshop training course based on the SEP for sustainable electricity use was at the highest level (M =4.90, SD = 0.12)

Development of a Challenge based Learning Platform to Enhance Artificial Intelligence Competencies for Vocational Education Students

Kanitta Hinon — 2025-03-31

Thailand's education system, in general, Vocational education is an aspect of Thailand's educational pathway that aims to practice vocational skills, which plays an important role in driving the development of technical skills. In recent years, the rapid development of artificial intelligence technologies has raised a significant challenge for vocational students regarding knowledge development and technology adoption. To strengthen the artificial intelligence competencies of vocational students, this research proposed a learning platform for vocational students and studied the suitability and competency from the following objectives: (1) Study the synthesize the challenging-based learning process of vocational students. (2) Develop a challenging-based learning platform to strengthen vocational students' competency in artificial intelligence technology with experts from various institutions in the design, development, teaching, and learning process. (3) Study the suitability of the development of a challenging-based learning platform to enhance the AI competencies of vocational students. The assessment of the platform consists of (1) A challenging learning platform architecture to enhance the competency of artificial intelligence of vocational students and (2) A suitability assessment of the development of a challenging-based learning platform to strengthen the artificial intelligence competencies of vocational students. The results show significant suitability of the learning process and procedures and appropriateness of the development of a challenging-based learning platform to enhance the AI competencies of vocational students.