Abstract
This paper presents a digital twin architecture that mirrors how real admission processes work and shows where AI can be safely introduced. The system reconstructs workflows from historical data and uses AI agents to analyze documents, video, and structural data. Instead of relying on black box LLM based predictions, the system uses simple decision trees at each step of the process. These models make decisions explicit and easy to audit. A state based orchestration layer connects AI outputs to clearly defined process stages and supports a transition from notebook based prototypes to enterprise systems. The key contribution of this work is a practical development approach for AI systems. Instead of building monolithic solutions, the business logic is decomposed into small AI agents implemented as executable notebooks that communicate through cloud storage. This prototype can then be automatically transformed into enterprise ready services using AI programming agents. The approach is evaluated on real applications from a university admission process. Results show that the digital twin closely reproduces historical decisions while remaining interpretable. This work addresses the challenge of deploying AI in high stakes systems where both performance and explainability are required.
Paper Type
Full Paper
DOI
10.62036/ISD.2026.34
A Digital Twin Architecture for AI Infused Information Systems Development: Process Mining, Multimodal Evidence Extraction, and Interpretable Decision Models
This paper presents a digital twin architecture that mirrors how real admission processes work and shows where AI can be safely introduced. The system reconstructs workflows from historical data and uses AI agents to analyze documents, video, and structural data. Instead of relying on black box LLM based predictions, the system uses simple decision trees at each step of the process. These models make decisions explicit and easy to audit. A state based orchestration layer connects AI outputs to clearly defined process stages and supports a transition from notebook based prototypes to enterprise systems. The key contribution of this work is a practical development approach for AI systems. Instead of building monolithic solutions, the business logic is decomposed into small AI agents implemented as executable notebooks that communicate through cloud storage. This prototype can then be automatically transformed into enterprise ready services using AI programming agents. The approach is evaluated on real applications from a university admission process. Results show that the digital twin closely reproduces historical decisions while remaining interpretable. This work addresses the challenge of deploying AI in high stakes systems where both performance and explainability are required.
Recommended Citation
Laukaitis, A., Kalibatienė, D., Serackis, A., Normantas, K., Jankauskas, M., Jancevičius, J. & Jodenytė, D.(2026). A Digital Twin Architecture for AI Infused Information Systems Development: Process Mining, Multimodal Evidence Extraction, and Interpretable Decision Models. In M. Valenta, B. Mannová, R. Pergl, A. Przybylek, M. Lang, H. Linger, C. Schneider, N. Iivari, & E. Insfran (Eds.), Making ISD Sustainable: Reloaded with AI and Automation (ISD2026 Proceedings). Prague, Czech Republic: Czech Technical University in Prague. ISBN: 978-80-01-07585-2. https://doi.org/10.62036/ISD.2026.34