AI-driven
Data Tools

Data tools focused on identifying and
implementing key improvements to academic timetabling. These include:

• Timetable compactness to reduce students required days on campus, improving student experience and staffing costs

• Clash constraint optimisation to refine the existing clash constraints, to reduce the number of real constraints on the timetable.

Modelling and predictive analytics tools that help universities optimise their space footprint and reduce capital costs, including:

• Predictive unit enrolment estimates – enabling more resilient and accurate planning for space use and workforce.

• Space demand forecasting – identifying space requirements based on changes to enrolments, pedagogy, and technology.

Sophisticated predictive analytics models that enable early identification and engagement of at-risk students, to improve student engagement satisfaction, success, and retention. Our tools also give universities tools to take action on at-risk students and log actions to measure the impact of interventions.

​

​

​

​

We have partnered with a leading WA university to develop a successful Proof-of-Concept (POC) for an AI-driven automated Credit Recognition assessment tool. This creates significant opportunities to save time and speed up credit recognition processing.

​

​

​

​

​