published on 15.06.26
PROFESSOR OF MANAGEMENT CONTROL, ACCOUNTING AND AUDITING
Artificial intelligence is no longer a distant technological trend. It is rapidly becoming the operating system of the global economy, reshaping professions, organizations, decision-making, and the very meaning of human work. For universities, this creates an uncomfortable but unavoidable question: are we preparing students for the world that is emerging, or for one that is already disappearing?
These simulators are not conventional educational software. Nor are they simple chatbots added to a course for novelty. They are immersive, web-based learning environments where students confront complex decision-making situations that integrate financial, strategic, ethical, social, environmental, and human dimensions at the same time.
In one simulation, students may act as CFOs of a multinational company facing tensions between profitability, liquidity, investor pressure, and ESG commitments. In another, they may manage a city mobility system and balance emissions reduction, citizen satisfaction, budget constraints, and political resistance.
Students interact with AI-generated stakeholders through text and voice, negotiate trade-offs, justify decisions, and experience the consequences of their choices in real time.
For decades, higher education has relied heavily on fragmented teaching models: isolated disciplines, static case studies, standardized exams, and assessment systems still largely based on repetition and content reproduction. Yet the world awaiting our students is not fragmented, static, or predictable. It is complex, uncertain, interdisciplinary, and increasingly mediated by artificial intelligence.
They force students to think systemically rather than mechanically. They reward judgment rather than memorization. They encourage reflexivity by exposing the assumptions behind managerial and societal decisions.
Most importantly, they make visible something traditional teaching often struggles to show: the interconnected consequences of decisions across stakeholders, institutions, and time horizons.
If AI can generate essays, summaries, financial analyses, presentations, code, and even research reports within seconds, then assessment systems based primarily on producing standard written outputs are rapidly losing credibility. The question is no longer whether students will use AI. They already do. The real question is whether universities are capable of redesigning learning and assessment around this new reality.
In an AI-dominated job market, graduates will not compete with machines on speed, memory, or technical execution alone. Their value will increasingly depend on their ability to supervise AI, ask meaningful questions, evaluate outputs, detect flawed reasoning, integrate multiple perspectives, and make responsible decisions under uncertainty.
They move students from passive knowledge consumption to active decision-making. They transform the classroom into a space where students must reason, negotiate, justify, adapt, and reflect. They also allow assessment to evolve from “What do you know?” to “How do you think, decide, and act when facing complexity?”
The professor is no longer only a transmitter of information. In a world where knowledge is instantly accessible, the educator becomes an architect of learning experiences: someone who designs intelligent environments where students learn how to think critically, collaborate with AI, and exercise human judgment in contexts where there is rarely a single correct answer.
This transformation is also connected to the rise of “vibe coding”: the ability to create digital tools and applications through natural language instructions rather than traditional programming. Popularized by Andrej Karpathy, the concept reflects a broader shift from manual technical execution to AI-assisted orchestration.
For higher education, this is revolutionary. Professors in finance, strategy, sustainability, marketing, law, public policy, or management can now prototype interactive learningenvironments without being professional software developers.The distance between pedagogical imagination and technological implementation is collapsing.
AI simulators can help position the institution at the forefront of pedagogical innovation, not by using AI as a superficial addon, but by rethinking the very architecture of learning. They can foster interdisciplinarity, strengthen student engagement, connect teaching to societal impact, and prepare graduates for a professional world where human-AI collaboration will be the norm.
Universities that continue to rely exclusively on static lectures, standardized exams, and traditional assignments risk becoming increasingly disconnected from the realities students will face after graduation. By contrast, institutions capable of developing AI-native pedagogies may shape a new generation of graduates: not merely users of AI, but critical supervisors, ethical decisionmakers, and responsible architects of AI-assisted work.
The real challenge is whether academia is willing to reinvent itself before its traditional teaching and assessment models become obsolete. AI simulators are not just another educational technology. They are an invitation to redesign higher education for a world where intelligence is no longer scarce, but judgment, responsibility, and imagination are more necessary than ever.
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