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Related Experiment Videos

Operational intelligence for institutional processes: dynamic modeling and state-based decision policies.

William Villegas-Ch1, Joselin Garcia-Ortiz1, Christian Aristizábal2

  • 1Escuela de Ingeniería en Ciberseguridad, Facultad de Ingeniería y Ciencias Aplicadas, Universidad de Las Américas, Quito, Ecuador.

Frontiers in Artificial Intelligence
|July 14, 2026
PubMed
Summary

This study introduces an operational intelligence framework for managing research processes in higher education. The new framework enhances adaptive capacity and operational efficiency through state-dependent interventions, improving system dynamics.

Keywords:
event-driven dynamic systemsoperational adaptability in institutional systemsoperational intelligenceprocess miningstate-based decision making

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Area of Science:

  • Higher Education Management
  • Operational Research
  • Artificial Intelligence

Background:

  • Research process management in higher education suffers from fragmented flows, manual validation, and static criteria, leading to delays and poor adaptability.
  • Current AI applications in institutional management focus on isolated automation or prediction, lacking a unified structure for events, states, and decisions.

Purpose of the Study:

  • To propose an operational intelligence framework for modeling institutional processes as a coupled dynamic system.
  • To evaluate a multivariable state-dependent decision policy against a deterministic baseline using system dynamics metrics.

Main Methods:

  • Institutional processes modeled as a coupled dynamic system: St+1 = T(St, Et, Dt).
  • Reconstruction of state trajectories from operational logs.
  • Evaluation of a deterministic rule-based policy and a multivariable state-dependent policy using metrics like decision rate, activation variance, state sensitivity, and transition behavior.

Main Results:

  • The proposed policy reduced the decision rate from 0.1427 to 0.1002 and activation variance from 0.3689 to 0.1903.
  • State sensitivity increased to 1.0, indicating improved responsiveness.
  • Under high load, the model showed significantly larger transitions (-777.79 vs. -60.17) and achieved stabilization rates of 1.0 in short trajectories.

Conclusions:

  • The operational intelligence framework enables a reconfiguration of the operational decision regime.
  • State-dependent interventions lead to more adaptive and efficient management of research processes in higher education.
  • The findings highlight the potential of integrated AI for optimizing complex institutional operations.