Microsoft, Accenture and Altis power UNSW’s AI initiative

Microsoft, Accenture and Altis have played pivotal roles in advancing the University of New South Wales’ use of artificial intelligence to enhance student learning and support systems.

UNSW uses AI through its Data Insights for Student Learning and Support project, led by its Learning Analytics Intelligence team, with significant support and contributions from the IT and UNSW Planning and Performance (UPP) teams.

The project aims to use machine learning to detect when students are at risk of academic failure early and connect them to the right support and services when they need it most.

The project uses a modular approach built around an Academic Success Monitor (ASM). The ASM employs a predictive machine learning model trained on historical data from learning and administration systems.

This model identifies potential academic risks based on student engagement in the digital learning environment, allowing academics and students to take proactive measures.

Simon McIntyre, director of educational innovation at UNSW, said the Learning Analytics Intelligence team has also worked closely with UNSW’s student support services to develop AI-generated recommendations based on individual students’ circumstances.

 “Microsoft and their partners Accenture and Altis helped us kickstart everything through the co-development of a prototype in the Power Apps Innovation Centre Program,” he said. “Our chief data and insights officer and Altis then collaborated to wire custom configurations of our Microsoft technology stack.”

According to McIntyre, by engaging support services, the university has understood the language, the types of student personas they see, and how their syntax in their communications escalates at different risk levels.

“We’re using this information to build a matrix and then feeding it to the AI model,” he said. “We’re not replacing support services and doing their job for them – we’re just systemising that approach so that we give students awareness of relevant support options and the autonomy to help themselves.”

McIntyre said UNSW is also giving our support teams a ‘heads-up’ as early as week 2 in the term to reach students who may need more specialised help.

The project’s ASM is powered by various Microsoft solutions, including Azure, Azure Machine Learning Studio, Azure OpenAI Service and Power Apps.

The ASM’s initial small-scale testing in 2023 involved 33 academics and 25 courses across all UNSW Sydney–based faculties. The results were promising, with the model confidently identifying 79 percent of at-risk students in the first few weeks of a course.

Testing then expanded into a pilot in early 2024 for 80 courses, which included around 17,000 students and 83 academics. The ASM identified 284 students at risk of failing and needing support and provided academics with updates and insights about student engagement in their classes.

In addition, 75 per cent of academics stated the ASM identified potential risks much earlier than possible and 49 per cent of students who received proactive nudges from the system showed statistically significant increases in class engagement.

UNSW also has ambitious plans for its Data Insights for Student Learning and Support project and related initiatives. The ASM is set to roll out to all first-year students and teaching staff at the start of 2025 and then reach about 80,000 students and about 7,000 staff by the following year.