A class above: UNSW Sydney uses AI to power personalised paths to student success

UNSW Sydney is one of the world’s leading universities, ranking among the top 20 in the QS World University Rankings 2025. This prestigious standing is a testament to its academic excellence as well as its innovative culture.

“We have a lot of great innovators at UNSW,” says Director of Educational Innovation Simon McIntyre. “One of the things we’re working hard to do is bring them closer together so we can maximise the impact of the work that they’re doing.”

This culture has seen UNSW embrace modern technologies like AI to improve its teaching and learning outcomes. In fact, AI is a key pillar of the UNSW Educational Technology Roadmap 2024-2028.

Personalising student journeys with AI

One way UNSW is using AI is through its Data Insights for Student Learning and Support project, led by the university’s 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 early on when students are at risk of academic failure and connect them to the right support and services when they need it most.

“The hope is that we can create a much better and more supportive environment for our students by quickly spotting who needs assistance and helping them make the right decisions to seek out the support they need,” says McIntyre.

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.

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.

“By engaging our support services, we’ve been able to understand the language they use, the types of student personas they see, and how the syntax they use in their communications escalates at different risk levels,” says McIntyre. “So, we’re using this information to build a matrix and then feeding it to the AI model.

“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 take action to help themselves. We’re also providing our support teams with a ‘heads up’ as early as week 2 in the term so they can reach those students who may need more specialised help.,” says McIntyre.

Testing the benefits

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% 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 who were at risk of failing and in need of support, provided academics with updates and insights about student engagement in their class. In addition, 75% of academics stated the ASM identified potential risks much earlier than previously possible, and 49% of students who received proactive nudges from the system showed statistically significant increases in class engagement.

Associate Professor Lynn Gribble from UNSW’s School of Management & Governance has experienced firsthand the project’s benefits.

“Being on this project has really enabled me as a lecturer in charge to understand some things about my students in a quantifiable way,” she says. “We know that students who perhaps leave something to the last minute, go missing from Moodle [UNSW’s learning management system], or aren’t engaging with the course materials will not do as well as students who do.

“I can [also] personalise ASM messages to these students, directing them to support services and helping them get back on top.”

McIntyre believes this holistic approach makes the project unique. “We believe we’re among the first university to look at this whole thing as a connective ecosystem,” he says.

“We’re not just putting all the onus on an individual academic to interpret the data and then act. We’re offering relevant suggestions based on the data, and also giving our students information about their engagement and personalised advice to help them succeed based on their own circumstances.

Enabling responsible AI use at scale

Implementing such a comprehensive project has not been without challenges. According to McIntyre, UNSW teams first needed to collaborate on the development of more AI-capable data infrastructure. UNSW are working with Microsoft Industry Solutions Delivery to further explore and prioritise AI use cases for expansion through a Three Horizon plan, supporting architecture Frameworks and building organisational AI Aptitude for prolonged sustainment.

“We had data scattered across the university and it isn’t necessarily unified. So, my team worked extensively with our UPP and IT teams to set up a data lake that could use AI and ML at scale,” he explains.

McIntyre notes that collaborating with UNSW’s Chief Data & Insights Officer (CIDO), Kate Carruthers, and Microsoft partners Accenture and Altis significantly bolstered the project’s success.

” The support of Microsoft and their partner Accenture really helped us kickstart everything through the co-development of a prototype in the Power Apps Innovation Centre Program. Our CIDO and Altis then helped wire custom configurations [of our Microsoft technology stack] together, which we wouldn’t have been able to do as quickly on our own.” he says.

Ensuring responsible and ethical use of AI has also been a top priority. A steering committee oversees the project, including UNSW students, teaching staff, and members of its Educational Innovation team and legal department. A privacy impact assessment was also conducted to ensure compliance with legislation, and the university’s student privacy agreement was updated to include AI use.

Further expansion and integration

Looking ahead, UNSW 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 all 80,000+ students and 7,000+ staff by the following year.

In addition, UNSW is also investigating other uses of AI in the learning ecosystem to understand the additional value they would bring. “We’re also working on prototyping an orchestrator-style chatbot architecture with multiple AI bots underneath to act as personal concierges,” says McIntyre. “We’ve already got a modest pilot project starting in the latter half of the year exploring the use of AI bots for roles such as student-facing administrative support, academic support on interpreting course information and lecture notes, and future student recruitment.”

This pilot of the chatbot technology will be assessed for its suitability to become the primary interface for the Data Insights for Student Learning and Support project and hopefully other university functions.

“All the things we’re exploring in this project are providing great case studies to assist the university’s exploration of how we can really leveraging the power of AI at scale,” says McIntyre. “It’s been a fantastic project to bring people together to discover its potential and help the university move forward.”