What is personalised learning?

Personalised learning shapes pathways and support around each student’s needs and circumstances, rather than applying a single experience to all learners.

In practice, institutions use enrolment, progression, and engagement insights to adapt support while meeting academic and regulatory requirements.

Why personalised learning matters

Essentially, personalised learning is the understanding that each student is different, and that technology and curriculum can be altered to meet the diverse learning styles of the individual,

This matters in modern education, where students are increasingly balancing study with work, caring responsibilities, longer travel, and wellbeing pressures.  The TechnologyOne supported Minimum Income Standard for Students 2025 (MISS25) report in the UK found that first-year students may need to work more than 19 hours per week at minimum wage to cover costs, which can affect study time.

As a result, institutions need to offer greater flexibility and more responsive support.

By enabling earlier intervention and more relevant engagement, personalised learning helps institutions improve student outcomes while supporting the overall student experience.

What is a student management system ERP?

A student management system manages the end-to-end student lifecycle, from application through to alumni engagement. Within an Enterprise Resource Planning (ERP) environment, it provides a central source of student data for academic and administrative processes.

By consolidating student records, enrolment information, academic results, and engagement data in one place, a student management system gives institutions a consistent and reliable view of each student.

How student management systems enable personalised learning

Student management systems enable personalised learning by giving institutions the insights needed to understand individual student circumstances and respond in a timely manner. With access to real-time information on enrolment status, academic progression, engagement patterns, and support interactions, staff can easily identify students who may need extra guidance or flexibility.

By replacing manual tracking and disconnected tools, student management systems bring information together, so institutions can tailor communication and support within formal academic structures.

In practice, personalised learning becomes less about customising teaching content and more about delivering the right support at the right time, based on accurate, trusted student data.

Benefits of personalised learning from student management software

Evidence suggests that when personalised learning is supported by a student management system, institutions are better equipped to deliver targeted support at scale.

Challenges and considerations of personalised learning

While the benefits of personalised learning are well established, they are not guaranteed. Without the right foundations in place, attempts to personalise learning can increase complexity rather than reduce it.

You need accurate data, responsible use across teams, and change management that supports adoption. Without this, personalisation becomes fragmented and manual.

When implemented thoughtfully, student management systems can help institutions manage complexity and deliver meaningful personalisation at scale.

Clear institutional policies are also critical, particularly given the ongoing challenges around data protection and responsible use of student data within higher education institutions.

The future of personalised learning in education

What role will AI play?

Personalised learning is evolving as institutions seek to support diverse student needs without adding complexity. Its future depends on connected systems that help identify risk earlier and support timely action at scale.

AI will play an important role in enabling this shift, but its value lies in supporting decision-making rather than directing it. By analysing patterns across engagement, progression, and outcomes, AI can help institutions intervene earlier and target support more effectively, particularly in complex education environments where personalised learning is difficult to implement consistently.

At the same time, global education bodies caution against using AI to prescribe learning pathways or limit student choice. UNESCO argues that learning in the age of AI should remain exploratory rather than prescriptive, supporting autonomy rather than optimisation. As Bianca Farthing, Founder of AI Edify, notes:

“AI should not dictate learning; it should support the learner’s agency, creativity, and humanity.”

According to Mark Jones, Executive Vice President, Education at TechnologyOne, AI is most effective when it supports educators and professional staff by surfacing insight and reducing administrative burden.

“AI will only deliver on its promise if it is put to work with people, not instead of them,” he said.

“AI amplifies collective intelligence, but humans bring the ethics and empathy.”

Looking ahead, the institutions that succeed with personalised learning will be those that adopt AI responsibly, with clear governance, transparency, and a strong focus on student agency.