The AI shift: how student behaviour is changing

Student behaviour is shifting faster than some institutions can adapt. Nearly nine in ten UK undergraduates reported using generative AI for assessments in 2025, up from just over half the year before. Many students now use AI to explain concepts, summarise material, or suggest ideas, often before speaking with peers or instructors.

These changes reflect broader expectations for immediacy and accessibility in learning. At the same time, they introduce new challenges for engagement, assessment design, and academic integrity. Universities, TAFEs, and further education colleges must adapt teaching and systems, so AI enhances learning rather than replaces critical interaction.

Rethinking assessment design for authentic engagement

If AI can generate competent first drafts, assessment must move beyond surface output. The focus shifts from what students produce to how they think. Applied tasks grounded in real scenarios, staged submissions that show progression, and reflective elements that require students to explain their reasoning encourage deeper engagement.

These approaches reduce the incentive to outsource thinking and reward interpretation and judgement. Well-designed assessments strengthen participation rather than policing it.

How a student engagement system supports assessment integrity

Assessment redesign is most effective when supported by visibility across the student lifecycle. A student engagement system tracks milestone completion, submission timing, and shifts in academic performance.

This insight helps academic leaders distinguish between normal variation and disengagement.

Integrated within a student management system, this visibility supports early, informed conversations with students. Rather than relying on isolated tools, institutions can understand behaviour in context and respond proportionately.

Strengthening feedback loops to support engagement

In an AI-enabled environment where students expect immediacy, timely and structured feedback becomes even more critical. Real-time grade visibility, automated milestone reminders and triggered advisor notifications help ensure students understand where they stand and what comes next.

Effective feedback loops do more than correct performance. They reinforce belonging, clarify expectations, and encourage persistence.

When AI enhances responsiveness rather than replacing academic judgement, it supports a continuous learning experience that keeps students connected to their progress and institution.

Lifecycle visibility enables coordinated response

Sustained engagement depends on coordination across academic and support teams. Lifecycle visibility enables institutions to:

• Connect attendance, assessment performance, and progression data.
• Identify patterns across units, modules, and courses rather than isolated results.
• Share insights between academics, advisors, and support services.
• Trigger consistent communication based on risk indicators.

When data sits in one connected environment, staff gain clarity without navigating multiple systems, and students experience consistent support.

In the age of AI, coordinated visibility ensures engagement strategies are deliberate rather than reactive.

Early alerts and proactive intervention

By the time a student formally withdraws, the signals have usually been visible for weeks or months. Missed assessments, declining attendance, or reduced engagement with core course milestones often precede attrition. In an AI-enabled learning environment, these shifts can be subtle and distributed across systems.

Early alert capabilities bring those signals together.

When trends are connected, institutions can intervene with targeted outreach before disengagement becomes entrenched.

Proactive intervention protects academic standards and student wellbeing. It reinforces that engagement is monitored to support persistence and success.

Communication cadence in a digital-first environment

Students now experience their institution primarily through digital touchpoints. The rhythm and clarity of communication shape engagement as much as curriculum design.

A consistent communication cadence should include:

• Clear guidance on responsible AI use.
• Regular progression updates tied to the lifecycle stage.
• Personalised prompts aligned to assessment milestones.
• Transparent expectations around academic integrity.

In a digital-first environment, predictable and purposeful messaging builds trust. It ensures AI-enabled learning sits within clear institutional expectations, reducing ambiguity and strengthening engagement.

AI-aware academic integrity and support

Academic integrity in the age of AI requires explicit guidance on responsible AI use within each discipline, alongside opportunities to build AI literacy. Clear assessment expectations, transparent policies, and consistent staff messaging reduce ambiguity and support informed decision-making.

At the same time, institutions should focus on patterns of engagement rather than isolated outputs. When behaviour is visible throughout the student lifecycle, academic leaders can find emerging risks without resorting to punitive responses.

AI-aware integrity frameworks combine policy, education, and data-informed insight to protect standards while sustaining engagement.

The role of a modern student management system

Maintaining student engagement in the age of AI requires more than policy reform or isolated detection tools; it depends on connected infrastructure that provides visibility across the entire student lifecycle, with a modern student management system serving as a single source of truth from recruitment through to graduation.

When assessment milestones, attendance patterns and communication records sit in one environment, institutions can respond early and with context. Configurable alerts highlight meaningful changes, and workflows guide consistent follow-up. Clear dashboards reduce the need to reconcile data across multiple platforms, allowing teams to focus on students rather than systems.

In an AI-enabled environment, insight must be embedded within core processes, not layered on top. Connected systems, such as TechnologyOne Student Management, support proportionate oversight and informed intervention without creating unnecessary complexity.

Learn more about TechnologyOne Student Management

How TechnologyOne supports student engagement in the AI era

TechnologyOne partners with universities and tertiary education providers across Australia, New Zealand and the United Kingdom to deliver integrated lifecycle management on a unified SaaS platform. By connecting recruitment, enrolment, progression, and completion in one environment, institutions gain the visibility to track engagement signals and coordinate early intervention.

Our approach is grounded in what we describe as ‘AI with purpose’. As Mark Jones, Executive Vice President, Education, explains:

Recent innovations extend this philosophy, embedding agentic intelligence directly into the student lifecycle.

Plus – Intelligence embedded within the student lifecycle

Plus, our 20th product, brings agentic intelligence directly into the core of the enterprise environment. Rather than asking staff to navigate multiple screens or export data to separate tools, Plus enables natural-language interaction with live institutional data.

Academic leaders can surface insights into progression, review emerging risks, or generate analysis in moments, all within the student lifecycle.

Academic leaders can surface progression insights, review emerging risks, or generate analysis in moments, all within the student lifecycle.

In the context of student engagement, this matters. Staff can focus on mentoring and academic leadership instead of manual reconciliation across systems.

As TechnologyOne CEO and Managing Director Ed Chung said:

By embedding intelligence within the student management environment, Plus strengthens oversight while keeping humans firmly in the loop.

Guide – Extending engagement beyond the institution

If Plus embeds intelligence within the enterprise, Guide extends that intelligence directly to students and communities. Guide places enterprise-grade agentic AI in the hands of end users, enabling them to request help, access services, and complete tasks through natural conversation on their own devices.

As an extension of Plus, Guide interprets intent and orchestrates actions across institutional systems in real time.

Instead of navigating multiple portals or forms, students can ask for what they need and receive immediate, personalised support through one interface.

According to Mr Chung:

Guide is a complete gamechanger, transforming how institutions connect with the people they serve. For educational institutions, it will revolutionise student engagement.