Reflections on AI in Higher Education

The Potential of Personalised Learning, Intelligent Tutoring, and Advanced Analytics

Read on as Professor Claus Nygaard reflects on the use of AI in Higher Ed.


As we announce the call for chapters for the 25th symposium hosted by The Institute for Learning in Higher Education, I get to reflect on the theme for the symposium: “AI in Higher Education”. Throughout my journey as both an educator and technology enthusiast, I have recently been drawn to the potential of artificial intelligence (AI) to transform higher education. Reflecting on my experiences and observations, I believe that by integrating AI into teaching and learning practices, we can catalyse more meaningful experiences for our students.
In this article, I reflect on the potential of three AI-driven use cases that, I believe, hold great promise to enhance student motivation, engagement, and learning in higher education.

1. Personalised Learning Experiences

The first use case I want to reflect on is the potential of AI-driven personalised learning experiences for individual learners. I have always been fascinated by the diversity of interests, backgrounds, and learning styles that exist among students in higher education. Witnessing AI systems tailor educational pathways to cater to these different needs has underlined the potential of AI in creating custom-fit learning experiences.
The ability of AI-driven advanced data analytics to gather data related to students’ online behaviour, interaction with course materials, and assessment results is truly remarkable. Through the generation of personalised learning pathways, students can engage with educational content that is uniquely suited to their needs and aspirations. I can’t help but think about the immense opportunities AI offers in bridging the gap between students and their educational goals, creating compelling learning experiences customised just for them.

2. Intelligent Tutoring Systems

My next area of reflection is intelligent tutoring systems (ITSs), where AI comes to life as virtual tutors. These AI-powered systems, with their deep understanding of the subject matter and the capacity to provide personalised guidance to students, truly excite me.
The potential of ITSs to boost student motivation is vast, as they offer targeted support and feedback based on each individual’s strengths and weaknesses. What inspires me is the prospect of students, who may hesitate to seek help in traditional classroom settings, having convenient access to AI-driven guidance. This could result in fostering a sense of confidence and self-efficacy in learners, enabling them to become more invested in their own learning journey.

3. AI-driven Analytics for Monitoring Student Engagement

The third use case I would like to discuss is the potential of AI-driven analytics to monitor and assess student engagement. As an educator, I have always grappled with how to maintain the attention and interest of students throughout their educational journey. The digital era has indeed made an abundance of data available, but deriving trends and insights from this data for student engagement can be overwhelming.

It is with AI-driven analytics tools that I see the promise of gathering meaningful data on student interactions, online behaviour, and overall engagement with learning materials. Armed with these insights, educators can identify areas for improvement, detect early signs of disengagement, and effectively tailor their teaching strategies.

Through the implementation of such an AI-driven approach, there is immense potential for a significant shift in student enthusiasm and engagement levels, thanks to a learning process that is better aligned with their specific needs and preferences.


Looking at these three use cases, I am filled with enthusiasm about the potential of AI to positively impact student motivation, engagement, and learning in higher education. As educators, our ultimate goal is to empower our students and provide them with the best possible learning experiences. AI undoubtedly has the potential to be the conduit towards that goal.
That said, it is essential to view AI as a tool, not a miracle solution. As we increasingly incorporate AI technologies into the educational landscape, we must remain vigilant to ensure their ethical use while addressing concerns around privacy and biases. By engaging in thoughtful consideration and ongoing dialogue, we can collectively leverage the transformative power of AI to shape a brighter future for higher education and beyond.
I am looking forward to getting inspiration from the use cases described in the chapters submitted for the book and symposium.