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