Rapidly developing artificial intelligence (AI) promises greater efficiency and adaptability for business schools. It has the potential to automate repetitive tasks, streamline administrative processes and assist faculty in creating more interactive course materials, such as simulation-based assessments to test students’ knowledge.
However, widespread concerns remain that implementing new technologies in the classroom could take precedence over human interactions, eroding a crucial aspect of what makes business and management degrees so valuable. And many business school deans and faculty worldwide see generative AI (GenAI) as a double-edged sword, according to a report from the global accrediting body AACSB International.
AI’s ability to support more engaging learning activities has the potential to enhance students’ critical-thinking and creative problem-solving skills, but it also risks undermining those skills if students become reliant on quick-and-easy AI-generated content. If cognitively demanding learning tasks are simply handed over to an AI, the students’ products may look good at first glance, but the learning effects from the deeper engagement with the content may not materialise. In an analogy to the “ghost writer effect”, when the quality of an AI-written text is attributed to one’s own capabilities, this could be called a “ghost learner effect”, when students are able to pass an exam without having achieved the learning goals.
The scales will tip one way or the other depending on how students and faculty engage with this technology.
Using AI to monitor progress
If implemented correctly, however, AI tools can help faculty to create more personalised learning experiences for students.
Machine learning, which is a subset of AI that focuses on enabling software to learn from data without explicit programming, can be a highly useful resource for educators, for example. It can accurately discern which students show greater competency at certain tasks and which individuals need additional support, as shown in research I published in collaboration with Sabrina Ludwig, formerly at Mannheim Business School, Viola Deutscher of the University of Göttingen, and Jürgen Seifried, also from Mannheim Business School.
To test this in practice, we ran a computer-based business simulation for more than 200 trainees. The simulation presented them with information about a problem scenario, and they were given 55 minutes to solve it. We used a machine-learning model called the random forest to categorise students based on their level of ability.
By analysing behavioural data such as students’ mouse clicks and keystrokes in the first 10 to 20 minutes of the activity, the program predicted relatively well which students were more successful at solving the problem scenario, and which ones struggled.
So, how are these findings useful for educators at business schools?
Adapting and enriching course content
Critical thinking and creative problem-solving skills will be vital for the next generation of leaders across sectors. Open-ended simulations are an excellent way to foster these competencies because they require students to apply their theoretical knowledge in a realistic and complex, yet safe, scenario.
Harvesting data on which students show greater aptitude at these tasks enables business school faculty to share extra prompts and adjust the content in these scenarios.
Here is what this could look like in practice:
For students who need additional support, prompts should focus on areas they typically struggle with. For instance, if AI resources record that they open only one or two of the documents containing information about a problem scenario, this may suggest they are struggling with information overload. Faculty can add in prompts that appear on their computer screens that encourage them to use a notepad to store and organise information.
At the same time, tasks can be enriched to ensure they remain engaging for students who already show higher levels of competency. For example, if AI tracking tools show they are moving through the information documents at a faster pace, they could be prompted to review additional cases to ensure they are challenging their analysis of the scenario.
Best practice for integrating AI
While our study highlights a specific example of how machine learning models can be integrated to make learning more tailored and personal to each student, the findings carry implications for best practice more broadly.
In short, AI tools should be implemented in a way that enhances the “human element” of business education. This means using the added efficiency and data insights that AI offers to ensure higher-quality interactions between teaching faculty and students.
This is not restricted to analysing log data with machine learning algorithms. Other examples may include using GenAI to create a discussion point that could be debated in class or using AI to take over some administrative processes so teaching faculty can spend more time engaging with their students and updating course content.
In doing so, business schools align themselves with the World Economic Forum’s guiding principles for AI adoption in education:
- purpose: use AI to help all students achieve educational goals
- compliance: reaffirm adherence to existing policies
- knowledge: promote AI literacy
- balance: realise the benefits of AI and address the risks
- integrity: advance academic integrity
- agency: maintain human decision-making when using AI
- evaluation: regularly assess the impacts of AI.
As AI-enhanced tools play an increasingly important role in the corporate world, business schools must adapt to integrate this technology into courses and classrooms. Whether this technology will have a positive or negative effect on skills gain among our learners depends largely on the manner of implementation.
In this light, educators must apply digital resources in ways that promote, rather than reduce, human interactions as part of business and management degrees. By harnessing the analytical and generative power these tools offer, business school faculty can create a more tailored, personal and authentic learning experience.
Andreas Rausch is a professor of economic and business education at Mannheim Business School, Germany.
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