Student expectations are rising, budgets are tightening, and traditional support models are struggling to keep up. In this session, we explore how further and higher education institutions are rethinking student support, moving from fragmented contact centres to connected, AI-enabled service models.
You’ll see how Microsoft technologies are being used to unify data, automate routine queries, and deliver faster, more personalised support across the entire student journey, without losing the human touch where it matters most.
You’ll also hear practical transformation stories from programmes including Nottingham College, alongside insights from our expert panel:
- Mark Wilson, CTO
- Sakina Mannan, Senior AI Process Specialist – Microsoft
- Richard Tough, Principal CE Presales Consultant
- Richard Saunders, Business Development Manager – Public Sector
We hope you enjoyed this webinar. Please feel free to read and download the transcript below or get in touch if you have any questions about our exploration sessions.
Setting the Context: Why Student Support Must Change
The discussion opened by reframing contact centres not as technology platforms, but as student service operating models designed to solve institutional challenges.
Across further and higher education, institutions are shifting focus from recruitment and delivery to retention, continuation, and student outcomes.
Three key pressures are driving this:
- Regulation and accountability – retention and completion are now formally measured
- Financial sustainability – student dropouts directly impact income
- Student expectations – demand for instant, digital-first, consistent support
Current models are often fragmented, with disconnected channels, limited visibility, and no consistent triage, particularly under peak demand such as enrolment.
The core issue is not just technical, but behavioural: when students cannot access support easily, they disengage or drop out.
From Contact Centre to Student Service Model
Modern student support must shift from reactive communication to proactive lifecycle engagement.
Rather than simply handling queries, institutions should support students across:
- Recruitment and admissions
- Active study
- Support and wellbeing
- Alumni engagement
The real value comes from improving routing, reducing friction, and enabling faster resolution, not by increasing staffing levels, but by redesigning the operating model.
Technology is an enabler, not the starting point.
Microsoft’s Unified Platform Approach
The Microsoft perspective focused on delivering student support through a single, connected platform, enabling institutions to meet students where they are.
Using Microsoft technologies including:
- Microsoft Dynamics 365
- Microsoft Azure
Institutions can:
- Deliver omnichannel engagement (chat, SMS, social, voice)
- Unify data and automation
- Provide real-time insights for faster resolution
Key principle: AI is only as strong as the data behind it. Fragmented systems limit performance and increase cost.
Many universities currently spend heavily on maintaining disconnected systems rather than investing in innovation.
What Good Looks Like: A Connected Student Journey
A modern model connects every touchpoint into one ecosystem:
- Students interact via preferred channels
- AI provides instant self-service
- Complex issues are escalated seamlessly to human staff
- Staff receive full context and student history in real time
This reduces complexity and enables:
- Faster resolution times
- Lower operational cost
- Improved student experience across the full lifecycle
Importantly, institutions do not need to rip and replace existing systems, integration can sit on top of current infrastructure.
Live Demonstration Highlights
A live demo illustrated how a student query might flow through the system:
- A student asks: “When is my next lecture?”
- AI identifies the student via their contact record
- The system queries timetable data
- A personalised response is generated instantly
If the student cannot attend:
- The system updates attendance records
- Notifies relevant staff automatically
- Triggers downstream workflow actions
For complex issues (e.g. financial difficulty):
- The AI escalates to a human advisor
- A case is automatically created
- Staff receive full student context, including history and prior interactions
The system also demonstrated:
- Real-time multilingual translation
- Case summarisation
- Integrated student journey timeline
- Multi-channel engagement (SMS, chat, web, social)
Human + AI Collaboration
A key theme was the balance between automation and safeguarding.
AI should not independently handle sensitive areas such as:
- Mental health support
- Financial hardship decisions
- Safeguarding concerns
Instead, systems must:
- Apply strict routing rules
- Escalate appropriately to human teams
- Ensure deterministic behaviour in sensitive scenarios
This ensures both safety and trust in AI-enabled services.
Knowledge, Data, and AI Readiness
A key challenge is keeping knowledge bases accurate and current.
Approaches discussed included:
- Using website content as a live knowledge source
- AI-assisted knowledge agents that identify gaps
- Human validation to ensure accuracy
This avoids outdated or inconsistent responses and improves system reliability.
The importance of a single source of truth (“golden record”) was emphasised to support personalisation and accurate routing.
Personalisation and System Integration
Personalisation is enabled through:
- Student record systems (e.g. UCAS, SIS platforms)
- Unified identity mapping
- Data orchestration via cloud architecture
Once data is unified, institutions can:
- Personalise communications by student profile
- Tailor support based on lifecycle stage
- Trigger proactive engagement (e.g. events, open days)
Q&A Highlights
Q: How is personalisation achieved?
Personalisation depends on unified data and a system of truth. Once student identity is resolved, systems can dynamically tailor responses and journeys.
Q: How do we keep knowledge up to date?
Knowledge can be:
- Linked to live web content
- Enhanced with AI-generated suggestions
- Managed with human oversight for validation
Q: Do we need digital workers or agents?
AI agents can now handle orchestration more directly, reducing reliance on traditional Power Automate workflows. However, the right architecture depends on cost, scale, and organisational maturity.
Q: What about AI limitations and risk?
Sensitive scenarios must always be routed to humans. AI should be constrained through rules and escalation logic to avoid inappropriate responses.
Final Thoughts
The webinar concluded that institutions are moving towards a hybrid model of AI + human service delivery, where:
- AI handles scale and speed
- Humans handle judgement and care
- Unified data enables personalisation
- Contact centres evolve into strategic student service hubs
Successful adoption depends not on full automation, but on careful design, governance, and phased implementation aligned to institutional priorities.
We hope you enjoyed this webinar. Please feel free to explore our other videos or get in touch if you have any questions about our exploration sessions.
