Architecture of AI-driven business model on a digital ecosystem

Katekeaw Pradit () and Pallop Piriyasurawong ()

International Journal of Innovative Research and Scientific Studies, 2025, vol. 8, issue 2, 3414-3427

Abstract: This research presents an artificial intelligence architecture framework that drives business models in a digital ecosystem using synthetic methods. This architecture focuses on integrating the potential of artificial intelligence to revolutionize the learning process and create new businesses. The system consists of four key components: 1) Recommendation System – analyzes behavior and learning progress to tailor content to individual understanding; 2) Adaptive Test System – adjusts the difficulty level of questions to suit individual learners; 3) Collaboration Tools – allow learners to exchange ideas and develop business models together; 4) Business Intelligence Tools – make practical learning easier and apply it to real-world situations. The system supports data analysis for business decision-making. The evaluation of the system indicates that it is very good (mean = 4.83, S.D. = 0.15). The proposed architecture is developed in a digital ecosystem with the function of facilitating learning and creating business plans for student entrepreneurs. This approach promotes strong governance within higher education institutions, optimizing entrepreneurial development within the various stages of education, testing, practice, and entrepreneurship assessment. This will lead to best practices in the effective use of artificial intelligence tools in such a way as to create an innovative and sustainable educational environment.

Keywords: Adaptive testing; BI tools; AI-driven business models; collaboration tools; recommendation systems. (search for similar items in EconPapers)
Date: 2025
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