The Faculty of Engineering has launched a dedicated research centre to solve one of the sector's most consequential problems: how to give every engineering student a genuinely personalised high-quality learning experience, regardless of cohort size.
The Centre for Applied Research in Engineering and Computing Education, known as CARE, draws on learning analytics, responsible use of artificial intelligence, and evidence-based educational research. Its founding ambition is to establish the Faculty as a world leader in personalised learning at scale, and to produce research findings that change how engineering is taught well beyond Sydney.
The challenge is real and largely unsolved. The conditions that allow individual students to feel known and supported have historically depended on small class sizes and close interpersonal interactions with their teachers. In contrast, Engineering faculties tend to be large and impersonal by nature. CARE has been built to find rigorous, transferable answers to that problem.
Engineering education faces significant challenges: rethinking education in the age of AI, changing student expectations, the difficulty of maintaining quality in large cohorts.
Professor Mary Lou Maher, CARE Director
Research grounded in real problems
The centre launches with active research across five themes: its own strategic work on AI in the curriculum, responsible use of AI in assessment, the student experience in engineering, innovations in pedagogy and curriculum, and novel applications of AI to support learning.
Building AI into the curriculum on the Faculty's terms
Two strategic projects, led by Professor Mary Lou Maher with Dr John Vulic and Jacob Elmasry, ask what AI in engineering education should look like when designed deliberately rather than adopted by default. The first builds a pathway for course-specific AI agents, generalising an approach from existing trials so an agent can support learning in any unit of study, not only the few where someone has had time to build one. The second develops a repository of AI literacy materials for academics to build their own understanding and adapt for teaching, on the premise that literacy cannot be delegated to a single workshop. Both will track adoption and impact as they proceed.
Assessment that still asks students to think
At Sydney, assessment runs along two pathways: controlled assessments, sat under supervision, and open assessments that permit AI use. Open assessment supports authentic learning, but generative AI has introduced what Dr Muhammad Sajjad Akbar and Dr Mohammad Polash call an illusion of learning, where a student can produce a credible answer without doing the cognitive work that produces understanding. Their project is piloting a platform that analyses assessment specifications using large language models, semantic similarity models, and Bloom's Taxonomy to identify tasks an AI can simply solve, then generates redesigned, student-centred alternatives, aiming to save coordinators' time, improve assessment quality, and keep critical thinking with the student.
Understanding why students disengage
A third strand of research tackles student engagement. Data from across the Faculty and anecdotally worldwide, shows participation has fallen across lectures, tutorials, recorded content, online platforms, and academic support sessions, a pattern that accelerated after the pandemic changed how and where students learn. Dr Elliot Varoy leads a project using learning analytics to build a predictive model that can identify at-risk students early enough to intervene, alongside qualitative interviews exploring what motivates participation choices, drawing on Self-Determination Theory. The team, which also includes Dr Nataliia Stratiienko, Dr Andre van Renssen, Dr Hazem El-Alfy, Dr Xi Wu, Dr Muhammad Sajjad and Ross West, intends the findings to inform teaching models and learning design rather than sit in a report.
Closing the gap with industry
Graduate engineers are expected to arrive with working proficiency in AI, yet university guardrails and industry expectations for AI use are poorly aligned, leaving students to navigate the difference themselves. A project led by Dr Marcello Solomon, Professor Timothy Langrish, Graham Madsen and Associate Professor David Hind uses the Faculty's Major Industrial Project Placement Scheme to close that gap, since students on placement are already doing professional work under professional conditions. Starting with the 2026 cohort, the team will map how industry actually uses AI against student work and develop AI-supported teaching workflows, aiming for a tested framework and materials that improve capability, support academic integrity, and prepare graduates for AI in their careers.
Tutoring that guides students to the answer
Students already use AI tools for writing, coding, and design, but generic AI assistance is good at bypassing exactly the cognitive work that produces mastery. Dr Sichao Li, Dr Zhengyi Yang and Associate Professor Masahiro Takatsuka are piloting a staff-supervised AI system built to do the opposite: it traces a learner's evolving state, including mastery, misconceptions, uncertainty, help-seeking, engagement, and readiness to transfer knowledge, and responds with scaffolded formative feedback rather than solutions. The evaluation will test whether this improves feedback quality and engagement while reducing over-disclosure, dependency, and uncalibrated confidence.
Building capability across the Faculty
CARE will also function as a professional development hub, with particular focus on education-focused academic staff, who have historically had fewer formal development pathways than research-focused colleagues.
The centre also works across institutional boundaries: one project maps the training given to casual academics, whose tutors and demonstrators teach most small-group classes despite undocumented preparation. Another uses large language models to trace a decade of student feedback across three faculties, tracking how expectations shift by discipline and year level.
Looking outward
"We are addressing worldwide research questions in our Faculty. Engineering education faces significant challenges: rethinking education in the age of AI, changing student expectations, the difficulty of maintaining quality in large cohorts. Innovative, applied research rooted in real classrooms has potential for significant impact." Professor Mary Lou Maher, CARE Director
CARE is committed to sharing its findings internationally and engaging with best practice because the solutions it develops should be useful across engineering education globally. The centre's position is straightforward: the engineering and computing education challenges of the next decade will not be solved by intuition or convention, and the Faculty is well placed to lead the work of solving them.
Header image: provided.