AI in Business Schools: What university leaders are looking at

AI has become an omnipresent subject within the Education and EdTech sector. As an accelerator and innovation partner  working closely with top tier business schools globally, we are always keeping a keen eye on new technological developments and how it’ll impact the space. 

This time, we're digging into what is the perception that top-tier business schools leaders have in terms of the impact that AI can have for their institutions, the market and related risks.

Here's how we did it:

We first broke down the world into five big categories: Teaching & Learning, Student Help, Skills and Jobs, New Business, and Administration.

Then, in each of these categories, we selected the most important functions and areas of interest for business schools deans and senior leaders. For example, in Skills and Jobs, we zeroed in on categories such as Career Services & Placement, Skills Training, and Alumni.

After going through all of these, we gave them scores based on three pillars:

• Perceived Impact: This criterion delves into the potential game-changing role that AI could play within a specific area. 
• Perceived Urgency: This aspect focuses on the pressing need for business schools to embrace AI within a specific domain. 
• Risk Tolerance: This factor involves a thorough examination of the level of risk propensity associated with integrating AI into a specific area, and it revolves around carefully weighing the potential benefits against the drawbacks.

Based on our research, we  identified a few areas of opportunities for university leaders globally:

Curriculum development

Implementing AI for curriculum development in universities holds the potential to revolutionise higher education. AI can analyse vast amounts of data to identify emerging trends in industries and job markets, ensuring that curricula remain relevant and aligned with the evolving needs of students and employers. 

AI can also streamline administrative tasks related to curriculum planning, allowing professors and teaching staff to allocate more time and resources to enhancing the overall educational experience. By harnessing the power of AI, universities can create more dynamic, adaptable, and skills-based and industry-focused curricula, ultimately preparing graduates to thrive in an ever-changing world.

Arguably, a key point remains outstanding: Technologically-speaking, AI is already at a satisfactory level to allow business schools to map skills and outcomes. However, how to define effective skills frameworks that are adopted and accepted by the employers? University leaders are calling for a unified approach.

‍Personalised learning

Business schools are working on leveraging AI algorithms to assess student's unique learning style, strengths, and areas that need improvement, tailoring coursework and assignments to match their individual needs. AI can also provide real-time feedback and adaptive content, helping students grasp complex concepts more effectively.

University leaders can utilise AI to track students' progress, offering timely interventions when necessary to ensure no one falls behind. By implementing personalised learning through AI, universities can foster a more inclusive and student-centric educational environment, enhancing the overall quality of education and better preparing graduates for their future endeavours.

Going forward, a major area of focus for university leaders will be implementing best practices to allow support staff and students to make the most of what automations and AI-driven insights can offer. Specifically, how can we implement easy-to-understand dashboards and metrics to support usability of such solutions? A number of leaders also remain sceptical around the explicability of some AI solutions.

New models &  products‍

AI and new data have the potential not only to revolutionise the learning experience but also to provide business schools with new revenue streams and product development opportunities. This is happening in a number of ways. 

First, we are witnessing universities leveraging data analytics capabilities to map market needs and demand and therefore launch new learning solutions that better fit these needs. Second, a number of institutions are leveraging AI technologies to provide more accessible products, therefore targeting a greater audience, either in the form of cheaper or shorter learning experiences developed and launched at a fraction of the cost. Third, we are also seeing an increase in university-startup partnerships to launch new propositions to market.

Assessment & evaluation

AI-driven assessment tools are an exciting development as they are able to provide instant feedback to students, allowing them to gauge their understanding and make necessary improvements in real time. These systems can also help educators analyse student performance on a granular level, identifying areas where additional support or modifications to teaching methods may be required. AI can also assist in automating the grading process, reducing the administrative burden on instructors and ensuring consistency in evaluations. From a university perspective, this has the potential to dramatically improve productive and student experience.

On the other hand, the use of AI in exams and assessments remains a concern for many as the risk of plagiarism increases without easy tools to capture misbehaviours. An increasing number of business schools are quickly shifting to modes of exams that are less AI-sensitive or are incorporating AI and Conversational Models as an active component. A key role will be played by regulators that are required to swiftly respond with new guidelines and standards on how to validate exams and credentials keeping AI in the loop.

To conclude...

The potential applications of AI in higher education and executive learning are vast and promising. Whether it's revolutionising curriculum development, fostering personalised learning experiences, or optimising assessment and evaluation processes, AI has the capacity to transform how universities operate and how students learn.

As we continue to explore these possibilities, a couple of open-ended questions arise:

• How can universities and business schools build the right internal capabilities to handle correct implementation and use of AI?
• And how can we design shared and trusted standards for AI implementation?
• How can business school leaders ensure that everyone in the institution is involved in the transformation?

These questions invite ongoing reflection and exploration as we navigate the exciting intersection of artificial intelligence and higher education.

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