The AI revolution is transforming the student experience in higher education

The extraordinary power of artificial intelligence (AI) systems were thrust into the public spotlight with the release of ChatGPT in 2022. It prompted many people to reflect on the profound impact AI is having on the way peo­­ple learn, communicate and work.

But many universities’ first response was to raise concerns it would enable students to cheat on essays. While the potential for academic misconduct is undoubtedly important, this ignores the strategic opportunities AI presents for universities.

AI tools are causing universities to reflect on the way they operate, with implications for core aspects of university life: teaching, research, administration and community engagement.

Like any new technology, AI presents opportunities and risks, and creates winners and losers. The pace of progress means the stakes are high.

To remain competitive, ensure financial sustainability, and enhance the quality of education and research, universities must prepare for the change AI will bring. Universities’ challenge is to work out the pace and strategies for adoption that set them up for long-term success.

In this article, we analyse the effects of AI on student experience, a vital lever of university performance. We consider AI’s potential benefits and risks, and identify 11 strategies to support universities on their AI journey.

In the competitive higher education market, universities must provide a high-quality student experience. And as the inaugural Nous Group Student Experience Study found, university leaders see technology – including AI tools – as a cornerstone for translating ambitions for student experience into practice.

AI systems can add value to the student experience in three ways:

1. They can enable a differentiated and highly personalised approach to a university’s engagement with its students. This includes tailoring educational content to different learning styles, identifying early warning signs when a student needs additional support, and providing recommendations for extra-curricular opportunities based on a student’s unique interests.
2. They can free up academic and administrative staff time for higher-value activities by enabling the automation of traditionally time-consuming tasks.
3. They can enhance access to university education by reducing costs and enabling development of educational products tailored to different local contexts. This will support universities to better reach non-traditional students and markets.

While ChatGPT and other natural language processing (NLP) tools are high the public consciousness, they are just one component of the broader landscape of AI applications that could enhance the student experience:

Each dimension offers significant potential benefits:

• Attraction and admissions: In the competitive world of student acquisition, AI can drive a more personalised and consistent enrolment process. AI tools enable universities to adopt more targeted approaches to attract prospective students, process applications quickly, and reduce dropout rates. AI-powered academic advisers can offer personalised guidance helping students select courses and study paths, in different languages and with context-specific advice.
• Induction and onboarding: In the critical first six weeks of a student’s time at university, AI can make induction and onboarding activities more engaging and streamlined. Recommender systems can help students understand the curricular and extracurricular options available and make choices tailored to their interests. Virtual reality (VR) and augmented reality (AR) tools allow students to familiarise themselves with their university before they even set foot on campus.
• Teaching and research: In the core domains of universities, AI is revolutionising how students learn and academics research. AI-powered tools provide more personalised learning platforms that can tailor content to students’ diverse needs and provide virtual teaching assistants to answer students’ questions. Increasingly, natural language processing tools (NLPs) are being incorporated by academics into course content and being used alongside predictive analytics tools to power research.
• Student support: Student demand for support is greater than ever. Whether students are seeking career guidance, advice on extra-curricular activities, or financial and mental health support, AI tools can help universities provide targeted support, increase access to self-service options, and allocate in-person contact time efficiently. These supports can help students to thrive at university and also enable early interventions to identify and address issues for students that are struggling. This could include plugging language and/or skills gaps for students in non-traditional markets.
• Belonging and engagement: Belonging, a key ingredient for student success, can benefit from AI tools that promote greater community engagement, which is particularly important when many students are learning online. Whether it is recommending extra-curricular activities for current students or promoting greater alumni engagement, these tools can strengthen community and a sense of belonging.

This is just the tip of the iceberg. While some universities are early adopters of AI tools, others are taking a wait-and-see approach, so have not yet deployed AI systems or embedded them in operations. Even for the early adopters, the pace of technological progress is fast.

Areas where there is likely to be significant short-term developments include personalised tutors, AI-generated coursework, AI grading and student feedback, and predictive analytics to identify early warning signs and interventions for students.

Notwithstanding the potential benefits of AI tools to enhance the student experience, universities must navigate the risks these technologies present. There are six primary risks universities must mitigate:

1. Privacy concerns: AI systems often rely on vast amounts of personal data, such as academic records and behaviour patterns, which raise concerns about data privacy and security.
2. Bias and fairness: AI systems can inherit biases present in training data, potentially leading to unfair and discriminatory outcomes. For example, if historical admissions data is used to train an AI model on identifying issues to tailor support service recommendations, it may provide worse support for historically underrepresented groups.
3. Impersonal education: While AI can personalise learning experiences, an overreliance on these technologies can lead to a depersonalisation of education, where students feel disconnected from instructors and peers. This could reduce student engagement, motivation and satisfaction.
4. Overreliance on technology: Universities and students may become overly dependent on AI-driven tools, diminishing the role of human educators, increasing academic misconduct, and reducing opportunities to develop critical thinking and writing skills. This may limit holistic development.
5. Inequitable access: AI systems can represent a significant upfront cost to institutions, potentially widening existing inequalities in access to high-quality higher education.
6. Transparency and accountability: AI algorithms can be complex and opaque, making it challenging to understand how decisions are reached. Lack of transparency can reduce accountability when issues arise, particularly if senior university leaders lack AI literacy.

We have identified 11 strategies, organised under three themes, for universities to use AI to improve student experience:

AI is not just a technological trend; it is a catalyst for transforming the student experience in higher education. Universities must urgently think about how to use AI to maintain and enhance student experience, while preparing students to enter workplaces that are also adopting AI.

Given the pace of technological growth, there will be significant benefits – for reputation, performance, and financial outcomes – for universities that think hard, smart and fast about how to use AI tools.

As industry races to unlock the value from AI systems, and prospective students get accustomed to using AI as standard practice, it is important that universities are not left behind.

Get in touch to explore how we can help you to best harness the potential of AI in universities.

Connect with Darren Leafe, Dan Benwell and Julie Mercer on LinkedIn.

Prepared with input from Zac Ashkanasy and Jessica Weereratne during her time at Nous.