Abstract

The increasing integration of artificial intelligence (AI) in education has raised significant questions about its pedagogical value, especially in language learning. This meta-analysis examines the extent to which AI contributes to the development of English-speaking and listening skills. A systematic review of the literature was conducted by the preferred reporting items for systematic reviews and meta-analyses guidelines, utilizing peer-reviewed studies indexed in Scopus, ERIC, and EBSCOhost from 2017 to 2024. Nineteen studies met the inclusion criteria, all of which utilized experimental or quasi-experimental designs with measurable learning outcomes. The analysis reveals a substantial overall effect of AI-enhanced instruction (standardized mean difference [SMD] = 0.981, 95% confidence interval [0.571, 1.391], p < .001), with particularly notable improvements in speaking proficiency (SMD = 1.033). Although listening outcomes showed a positive trend (SMD = 0.714), the effect did not attain statistical significance. Considerable heterogeneity was noted across the studies, reflecting variations in learner populations, instructional contexts, and AI applications. Quality appraisal using the risk of bias in non-randomized studies of interventions framework indicated a predominantly low to moderate risk of bias. Publication bias analysis, including funnel plot symmetry and fail-safe N, further confirmed the reliability of the results. These findings highlight the advantages of AI in enhancing speaking skills within English instruction and underscore the need for further empirical studies to investigate its impact on listening comprehension. Collectively, the results provide timely, evidence-based guidance for educators and policymakers aiming to integrate AI effectively into language education. Highlight the advantages of AI in enhancing speaking skills within English instruction and underscore the need.

Keywords:artificial intelligence, English instruction, speaking skill, listening skill, meta-analysis

Jantakoon, T., Jantakun, T., Jantakun, K., Pongpanich, W., Pasmala, R., Wannapiroon, P., & Nilsook, P. (2025). The effectiveness of artificial intelligence in English instruction for speaking and listening skills: A meta-analysis. Contemporary Educational Technology, 17(4), ep596. https://doi.org/10.30935/cedtech/17310

Abstract—Information technology has significantly impacted education, especially computer and telecommunications technology, enhancing educational development and increasing the efficiency of the teaching and learning process. Combining information technology with traditional classroom learning through electronic media interaction enhances knowledge and understanding for learners. Additionally, it fosters new experiences, creativity, and analytical thinking, enabling learners to use acquired knowledge for personal and societal development. This study focuses on the learning process through technology that fosters knowledge creation. The research objectives are: 1) To study the process of creating immersive learning experiences for the metaverse. 2) To design a learning process through the metaverse using Motivation, Information, Application, Progress (MIAP) to promote immersive experiences.

Keywords—metaverse learning process, MIAP, immersive experience

Sittisak Chomjan, Panita Wannapiroon, and Prachyanun Nilsook, “Metaverse Learning Process Using MIAP to Enhance Immersive Experience,” International Journal of Information and Education Technology, vol. 15, no. 8, pp. 1680-1686, 2025.
https://doi.org/10.18178/ijiet.2025.15.8.2369

Abstract

This research aims to 1) synthesize the conceptual framework and 2) develop the architecture and evaluate its applicability. This paper outlines the architectural framework for the digital transformation of enterprises, specifically focusing on an intelligent financial management system. The research is synthesized, and a systematic review employs the PRISMA flow diagram. This system will utilize a financial management database that includes salary management, accounting management, fixed asset management, risk control, report management, financial analysis, and system administration. This framework will integrate advanced artificial intelligence techniques to improve operational efficiency, accuracy, and security in financial operations. It can improve risk assessment, elevate client contacts, and optimize economic decision-making processes, therefore aiding in the formation of organizational support, management supervision, operational plans, and administrative decisions, among other elements. The results showed that this architecture has an excellent level of suitability (mean = 4.63, standard deviation = 0.44). It demonstrates that entities employing advanced financial management systems to facilitate data storage mitigate inaccuracies and assist in the rapid, precise, and efficient analysis of data, which is an outcome of implementing digital transformation. This shift enhances decision-making processes and fosters a culture of accountability and transparency within organizations, ultimately driving sustainable growth and innovation. © 2025 by the authors.

Keywords

Digital transformation; Financial management system; Intelligence financial management system

Pinyaphat Tasatanattakool, Katekeaw Pradit, Prachyanun Nilsook and Panita Wannapiroon (2025) Digital transformation of organizations: Intelligence financial management system. International Journal of Innovative Research and Scientific Studies, 8(1) 2025, pages: 773-783.
https://doi.org/10.53894/ijirss.v8i1.4422

Abstract

As Thailand navigates the challenges of the digital economy, higher education institutions are increasingly compelled to undertake systemic transformation to prepare graduates for employment in AI-integrated environments. This study proposes an AI-driven institutional transformation model specifically tailored to the context of Thai universities. The study was conducted with three main objectives: (1) to synthesize the core components of the model, (2) to develop and validate the proposed model, and (3) to assess its applicability level. A mixed-methods research design was employed to ensure both conceptual rigor and contextual relevance. The research procedure comprised three phases: (1) model synthesis, (2) model development and validation, and (3) model evaluation. The findings revealed that the 3D-POD Model integrates three dimensions: 1) Strategic domains—People, Pedagogy, Process, Platform, and Pathway; 2) Operational phases—Origin, Operation, Output, Outcome, and Optimization; and 3) Digital maturity—Digital Passive, Digitization, Digitalization, Digital Transition, and Digital Transformation. Collectively, these dimensions form a structured matrix (5 Strategic × 5 Operational × 5 Maturity Levels) that provides a practical lens for diagnosing, developing, and enhancing AI-driven transformation in higher education. Quantitative validation confirmed the model’s high content validity and practical applicability. The 3D-POD Model offers a strategic pathway for AI-driven institutional transformation and enhanced graduate employability in Thailand. The model may also serve as a reference for future digital intervention initiatives led by key stakeholders.

Keywords

• AI
• Generative AI
• Data-Driven Decision Making
• Digital Intervention
• Digital Leadership
• Higher Education
• Graduate Employability

Sararuch, S., Buabangplu, P., Nittayathammakul, V., Wannapiroon, P., and Nilsook, P. (2025) The 3D-POD Model for AI-Driven Institutional Transformation and Graduate Employment Readiness in Thailand in Digital Era. Lecture Notes in Computer Science Conference Paper2025.
https://doi.org/10.1007/978-981-96-8430-4_3

Abstract—Emerging technologies, such as the Artificial Intelligence of Things (AIoT), pose challenges in education, particularly when students struggle to connect theoretical concepts with practical applications. This gap limits their ability to engage with AIoT and develop computational thinking competencies, such as Critical Thinking, Algorithmic design, Problem-solving, Creativity, and Cooperativity. To address this issue, the Advance Organizer Integrating Visual-Based Programming for Artificial Intelligence of Things (AOVP-AIoT) model, was developed. The model combines structured scaffolding with visual programming to make AIoT concepts more accessible and engaging, fostering computational thinking skills applicable in formal and informal learning settings, including university courses, online training, and professional workshops. The study was conducted in two phases. Phase I involved designing the AOVP-AIoT model by synthesizing data from research publications (2003–2023). Expert review rated the model highly (mean = 4.39, SD = 0.69) across input components, learning processes, and computational thinking competencies. Phase II involved constructing the AOVP-AIoT platform, following the AIoT System Development Life Cycle (AIoT-SDLC) across eight iterative stages. Unlike existing approaches, the platform emphasizes on personalized learning pathways and interactive AI assistance, enchancing adaptability and real-time support. Evaluation results indicated very high quality in infrastructure, intelligence organizer-based management, learning tracking, and performance assessment (mean = 4.69, SD = 0.43). By equipping learners with transferable computational thinking skills, the AOVP-AIoT model addresses educational challenges in AIoT and prepares students for success in industries increasingly shaped by AI and IoT innovations.

Keywords—advance organizer, visual programming, artificial intelligence of things, computational thinking

Sant Phanichsiti, Prachyanun Nilsook, and Pallop Piriyasurawong,
“Advance Organizer Integrating Visual-Based Programming via Artificial Intelligence of Things to Enhance Advanced Computational Thinking Competency,” International Journal of Information and Education Technology, vol. 15, no. 7, pp. 1355-1367, 2025.
https://doi.org/10.18178/ijiet.2025.15.7.2337

Thananan Areepong, Prachyanun Nilsook and Panita Wannapiroon (2025) Interdisciplinary Virtual Learning Community Model for Social Engineer. Journal of Theoretical and Applied Information Technology
15th May 2025. Vol. 103. No. 9, 2025.
https://www.jatit.org/volumes/Vol103No9/7Vol103No9.pdf

Abstract—This research aims to use artificial intelligence technology to create micro-learning approach. The goal is to develop students’ skills in designing character movements to look more realistic and interesting, as well as in using Mixed Reality (MR). This research was conducted in the form of a cross-sectional study and a literature review. During October–December 2024, the sample population were recruited from among undergraduate students at the Faculty of Mass Communication Technology, Multimedia Technology, and Rajamangala University of Technology Thanyaburi. The research data were collected from a total sample of 30 students through a questionnaire to assess whether micro-learning approach with artificial intelligence technology can be used to develop skills in character movement design in game design and utilizing mixed reality. The research results found that this learning format is very effective. In terms of the quality of the content and learning media, it has an average score of 4.85 ± 0.03 (the highest quality). As a result, learners can effectively develop important skills and engage strongly in the learning process. The use of mixed reality technology enhances the immersive and engaging learning experience for learners. This learning style is extremely effective and suitable for developing movement design skills for in-game characters.

Keywords—micro-learning, Artificial Intelligence (AI), skills in designing character movements, Mixed Reality (MR)

Vipusit Piankarnka, Prachyanun Nilsook, and Panita Wannapiroon,
“A Micro-Learning Approach with Artificial Intelligence for Improving Skills in Designing the Movement of In-Game Characters and Using Mixed Reality,” International Journal of Information and Education Technology, vol. 15, no. 4, pp. 847-857, 2025.
https://doi.org/10.18178/ijiet.2025.15.4.2291