Using Generative AI to Improve Process Management Maturity in Education and Consulting
Cristóbal Toro & Pedro Robledo from the International University of La Rioja (UNIR) explain how they use GenAI to enhance students' understanding of BPM teaching and consulting
What is the challenge that you are addressing in relation to AI?
This example shows how GenAI can be integrated into BPM teaching and consulting to improve process understanding, generate simulation scenarios and personalise feedback to students and organisations.
The challenge was to explore how we could combine academic rigour with emerging AI tools, for positive student outcomes.
How specifically have you addressed this challenge?
We have developed practical exercises where students use GenAI to model processes in Business Process Model and Notation (BPMN) and Decision Model and Notation (DMN), to analyse bottlenecks and propose improvements.
In parallel, we used AI to perform process maturity analysis in simulated and real companies, generating customised recommendations. We implemented an iterative approach with BPMM where AI results are critically reviewed by humans, ensuring that decisions and learnings maintain academic and strategic validity.
This approach combines automation, learning by doing and expert supervision.
Why did you address this challenge in this way?
We chose this methodology because it maximises the efficiency of learning and decision making in BPM, combining AI processing power with human expertise.
The evidence comes from more than 25 years of teaching and consulting, including the implementation of the Business Process Maturity Model in organisations and master exercises at our university (UNIR).
The observations of performance, the quality of deliverables and student feedback show that guided use of GenAI enhances their understanding of complex processes and improves their critical analysis skills.
Were your actions/approaches effective/successful or not?
Our evidence of the effectiveness of our intervention includes measurable improvements in the quality of student work, faster and more accurate assessments of simulated and real business processes, and positive feedback from students and participating companies.
The recommendations generated by GenAI, when critically reviewed, showed greater strategic value and applicability, confirming the value of taking a hybrid approach.
What have you learned from the experience?
GenAI can significantly enhance BPM education and consultancy if integrated in a guided and ethical manner. It does not replace human expertise; its true value lies in complementing analysis, generating scenarios and personalising learning.
Combining GenAI with human supervision produces better results than using GenAI alone.
Expert supervision and critical validation are essential to ensure academic rigour and practical relevance, guaranteeing that the results are useful and accountable.
Do you have examples of how you have used AI in your education, research or leadership roles within higher education? Please do submit your short case-studies to us