Understandably, similar fears exist within higher education. Machine learning has for several years been used for automated marking systems and to predict student performance through learner analytics. A fair bit has already been written on the potential and perils for students, staff and employers. However, as a sector we are still learning what the true impact might be on our current model of higher education.

I believe it will be transformative tool in widening participation to higher education. I envisage several ways in which machine learning can support alternative routes into higher education:

1. Outreach to the hard to reach: Traditional outreach has its limitations in reaching rural and isolated communities. It is currently far easier to deliver widening participation interventions to those close to the location of a university, often within our cities. AI will be able to help create dynamic extended reality environments to provide the opportunity for pupils to engage with stretching and challenging interventions from their home or school. We will be able to take pupils interested in medicine into an operating theatre, interact and explore. Future engineers will be able to visit some the most exciting building projects across the globe and interview a range of engineers whilst sitting in their lounge.
2. Taster courses: Machine learning will be able distil degree course materials into short and engaging taster sessions for future learners. Making the delivery of taster sessions more dynamic and economical to produce and distribute.
3. Personalised career support: Machine learning will be able to provide personalised careers advice based on the profile of the individual and the talent needs of various industries. Students will be able to converse with sophisticated chatbots to help navigate the complexity of course options and the attributes they require from an institution.
4. Developing talent: Employers will utilise their staff data to identify skills gaps and better understand where to target their resources. Predictive analytics will be used custom-build career plans. Qualifications will be co-designed between employers and providers. AI will support greater flexibility in the delivery of work based higher education, helping to facilitate learning communities across organisations.
5. Admissions: Students will be able to gain bespoke support through the entire admissions process. This will include AI mentoring to help students without support from school or parents to present their potential to universities, including interview preparation and support with relevant entrance exams.
6. Pre-entry preparation: Returners to learning will be able to access high-quality targeted resources to help them prepare for university level study. Learners will be able to develop their skills and confidence by having a personal digital assistant helping to answer questions and provide complex feedback.
7. Accessibility: Students will be able to use AI applications to help identify learning need and support with any disabilities.
8. Transitions: A digital transitions mentor will be able to support widening participation students transition into higher education. They will be able to get timely answers to their concerns, gain vital information and pastoral support. Universities will be able to match students to potential employer and alumni mentors to help them progress and build social capital.

I am sure there are many other ways this technology will help provide additional routes into higher education and widen access. We are keen to get your views on this subject and perhaps this can for part of a session at the next FACE conference.

I asked ChatGPT to help with this blog, it stated “machine learning can contribute to a more inclusive, efficient, and supportive higher education environment, making it easier for individuals from diverse backgrounds to access and succeed in their educational pursuits”. I agree.