Enhancing Student Experience with AI

The main criteria for a successful institution as per understanding are the below ones

1. Superior academic delivery quality
2. Student Experience
3. Student Progression

IT passively supports the student journey from all angles. And it’s important to ensure the adoption of technology by the institutions. Especially in the AI era. Trust me I am still trying to see the balance when it comes to opening up AI for students and academics. An interesting area to analyze and expand.

With increasing student expectations for seamless digital interactions, institutions must leverage technology to create efficient, personalised, and data-driven experiences.

The challenge here is not gathering ideas around AI technologies but to curate a roadmap that works for all academic departments. Meeting the demand is the key challenge for the IT teams now.

Implementing an AI strategy is a balancing act between ambitious goals, diverse departmental needs, and constrained IT resources. The key to success lies in a phased, scalable, and business-driven approach.

How to prioritise?

Instead of attempting a broad AI rollout, focus on high-value, low-complexity areas where AI can create immediate impact

Leverage Low-Code AI and Prebuilt Solutions

Many universities lack deep AI expertise, but low-code AI tools and third-party solutions can accelerate adoption.

Enable AI Governance and Data Readiness

AI is only as good as the data it learns from. Many institutions struggle with data silos and inconsistent governance. To overcome this:

1. Establish centralised data governance with clear ownership and access policies.
2. Use data lakes and warehouses (like your Thesis system) to aggregate and cleanse data.
3. Implement ethical AI frameworks to ensure bias-free and explainable decision-making.

Build AI Capabilities Within the Organisation

While hiring expensive AI talent to meet the immediate needs, focus on upskilling existing staff and creating cross-functional AI teams:

1. Train IT teams on AI fundamentals.
2. Encourage faculty collaboration to integrate AI in research and teaching
3. Leverage AI consultants on a project basis to guide early implementations.

Adopt a Phased and Measurable Rollout

AI adoption should follow a crawl-walk-run approach:

1. Phase 1: Pilot AI in one department (e.g., automating student support).
2. Phase 2: Expand AI-driven analytics for student success.
3. Phase 3: Scale AI across research, teaching, and administrative processes.
   

Each phase should have clear success metrics, ensuring gradual but sustainable AI adoption.

Even with limited IT capabilities, an AI strategy in higher education is achievable if approached incrementally and pragmatically. By focusing on high-impact use cases, leveraging low-code AI, ensuring data readiness, and upskilling internal teams, can drive AI transformation without overwhelming resources.