The Centre for Applied Research in engineering and computing Education (CARE) at the University of Sydney is committed to driving rapid, research-informed improvements in engineering and computing education.
We focus on solving the Faculty’s most pressing educational challenges, ensuring strong student learning outcomes while maintaining a high-quality student experience. Our work is anchored in rigorous scholarship, prioritising initiatives that deliver meaningful and scalable impact.
Centre for Applied Research in engineering and computing Education (CARE) envisions a future where the University of Sydney Faculty of Engineering is a world-leader in personalised learning at scale. We are internationally recognised for developing innovative approaches that are grounded in, and contribute to, educational scholarship, and which enable educators to focus on individual student needs while maintaining scalability and efficiency.
Our approaches will be known for leveraging advanced learning analytics, artificial intelligence (AI)-driven adaptive systems, and evidence-based pedagogical strategies, to empower our educators to deliver customised learning experiences that enhance student engagement, retention, and skill development.
The Centre for Applied Research in engineering and computing Education (CARE) at the University of Sydney is committed to driving rapid, research-informed improvements in engineering and computing education. We focus on solving the Faculty’s most pressing educational challenges, ensuring strong student learning outcomes while maintaining a high-quality student experience. Our work is anchored in rigorous scholarship, prioritising initiatives that deliver meaningful and scalable impact.
CARE serves as a central hub for educational research, innovation, and evaluation, fostering a scholarly community of practice for academic and professional staff. We actively build capacity and capability, particularly among education-focused staff, while supporting all who are committed to advancing teaching and learning. Through applied research, we identify and address barriers to excellence in educating the next generation of engineers and computing professionals.
We leverage existing scholarly practices in educational innovation and lead evidence-based approaches for communicating, engaging with, and motivating students. By collaborating across disciplines and engaging with global best practices, CARE ensures that the Faculty remains at the forefront of engineering education. We take leadership in sharing our insights internationally, shaping the future of personalised learning at scale and strengthening the University’s global impact in education.
The Faculty of Engineering has recognised the need to significantly enhance the quality of its educational offerings, and has built this into a range of initiatives within its new strategic plan.
One of these initiatives – the development of this Centre – will focus in two key areas:
A core element in achieving high learning outcomes and an outstanding student experience is having every student feel individually supported. Whilst many institutions have achieved this with small numbers of students, a key challenge is to understand how this can be achieved at scale. Addressing and leading in this challenge is core to the Faculty’s educational strategy.
Within this context, the Centre will:
Our actions and outputs will be underpinned by evidence and their impact will be tracked and monitored over time. We will embrace calculated risks in exploring educational innovations and navigate setbacks with support from our leaders. We will evaluate our progress and learn from our mistakes. As a centre for experimentation, innovation and shared learning, the centre will support continuity and sustainability of innovations and drive adoption of innovation across the faculty.
The Faculty of Engineering has a range of projects being worked on simultaneously, broken down into 5 key areas.
Our Experts: Professor Mary Lou Maher, John Vulic, Jacob Elmasry
This project aims to develop a pathway for pedagogical course specific AI Agents in engineering units of study. Starting with research results from the implementation of course-specific AI Agents in existing units, this project will design and evaluate an approach for generalising agents that support productive learning in any engineering unit of study. The project will track adoption and impact of AI agents in engineering units of study.
Our Experts: Professor Mary Lou Maher, John Vulic, Jacob Elmasry
This project aims to provide engineering academics with AI Literacy learning materials so that they develop their own AI Literacy and adapt the content in this repository in their units of study. The project will track adoption and impact of AI Literacy across the curriculum.
Our Experts: Dr Huaming Chen (CI), Linghan Huang, Associate Lecturer Jiawen Wen, Haolin Jin
This project aims to investigate how course and assessment design in software engineering curriculum should evolve in the context of widespread generative AI use. The project is interested in understanding the widespread leverage of AI-assisted tools for course deliverables, in particular its impacts on students’ approaches to programming, design, and problem‑solving. The project also seeks to examine whether alternative assessment strategies can better capture student learning outcomes in an AI‑enhanced learning environment.
Our Experts: Rex di Bona (CI), Jacob Elmasry, Associate Professor Mohammad Saadatfar, Peter Lok, Thomas Elton
This project aims to research the implementation of the integration of AI in an introductory computing unit for engineers. Currently, the majority of industry programmers use AI for all programming tasks. However, most introductory programming units still teach students to only program by hand. This project aims to replace these traditional teaching methods with an AI based development of competency that uses AI as part of the software stack. This will result in a focus on teaching students prompts and understanding how the prompt is now the program, to develop code. The project will also develop examinations using an AI model and a locked down environment as this new approach to teaching requires a new approach to assessment.
Our Experts: Dr Jonathan Kummerfeld, Dr Mary Lou Maher, Dr Elliot Varoy
This project aims to identify ways in which instructors and tutors can be supported by NLP tools in providing feedback to students. The potential is to achieve outcomes that are not possible to achieve by either humans or tools alone. The goal of this project is to generate new natural language processing techniques for analysis of student-AI conversation analysis that can lead to classification and clustering decisions.
Our Experts: Muhammad Sajjad Akbar & Dr Mohammad Polash
The University uses two assessment pathways: Lane 1 (controlled assessments) and Lane 2 (open assessments that permit AI use). While Lane 2 supports authentic learning, Generative AI has created an emerging "illusion of learning," in which students can produce answers without engaging the cognitive processes required for deeper understanding. This project pilots an AI-powered assessment redesign platform that analyses assessment specifications using Large Language Models, semantic similarity models (SBERT and CrossEncoder), and Bloom’s Taxonomy to identify AI-solvable tasks and generate redesigned, student-centred assessments. The platform aims to save coordinator time, improve assessment quality, and strengthen student engagement, critical thinking, and meaningful learning.
Our Experts: Associate Professor Tom Goldfinch, Dr Ashlee Pearson, Dr Armin Chitizadeh, Dr Young No
Project Partners: QUT (Sam Cunningham, Sarah Dart), UTS (Karen Whelan and Anna Lindqvist)
This project is developing a validated method using LLMs for cluster and sentiment analysis on open-text student feedback data at scale. The project draws on 10 years of qualitative student feedback data across three Australian Engineering Faculties to determine: How student expectations have shifted over time with respect to their learning experience; variance in expectations across subdisciplines and study levels; and, trends in expectations that will enable us to take a proactive approach to improving student experience.
Our Experts: Dr Elliot Varoy, Associate Professor Alissa Beath, Professor David Lowe
This project evaluates the workshop streaming approach to grouping students according to self-report ability and preparedness. The goal is to understand student views on, and reactions to, an implementation of this approach in introductory programming units of study with very large enrolments.
Our Experts: Dr Gobinath Rajarathnam (CI), Irene Benitez, Associate Professor John Kavanagh, Austin Caie, Srishti Chadda, Jayden Dahdah, Associate Lecturer Maria Aira Calma
This project evaluates how a structured CV clinic using real labour-market data supports engineering students to translate curricular, project and work experiences into clear, evidence-based and ATS-readable CV language. The study will examine: changes between selected pre- and post-clinic CV statements; how labour-market skill terms are incorporated; and students' reflections on the process.
Our Experts: Professor David Lowe, Mafruha Mowrin Hossain, Jacob Elmasry
This project focuses on student choices in their engineering curriculum, particularly the choices that are made known to them through the Unit of Study Outlines of different units. Educational literature shows that increasing learner choice can help enhance self-actualisation and motivation in a unit, but is this practice being done at Sydney University? This project is part of a larger research area on personalisation which seeks to understand how students make decisions when given the choice.
Our Experts: Dr Elliot Varoy, Dr Nataliia Stratiienko, Dr Andre van Renssen, Assistant Professor Hazem El-Alfy, Dr Xi Wu, Mohammad Sajjad, Ross West
This project seeks to understand the behavioural engagement patterns evident across teaching modes and what motivates students’ participation decisions. Quantitative analysis of learning analytics will inform a predictive model to identify students at risk of disengagement, enabling earlier and more targeted support. Qualitative interviews, guided by Self-Determination Theory, will examine how autonomy, competence, and relatedness influence participation choices. By integrating behavioural analytics with motivational insight, the project will inform evidence-based decisions about teaching models, resource allocation, and learning design to better support student success.
Our Experts: Dr Sandhya Clement, Dr Xi Wu, Peter Lok
This project seeks to better understand the challenges faced in project-based learning (PBL) units within engineering education including challenges like marking consistency, resource management, and student support. The purpose of the study is to gather insights from unit coordinators, teaching teams, and students across different schools in the Faculty of Engineering at the University of Sydney. By exploring these perspectives, we aim to identify strategies to improve curriculum design, grading practices, and professional development for both staff and students. The ultimate goal is to enhance the effectiveness of PBL and ensure that all students benefit from meaningful and transformative learning experiences.
Our Experts: Dr Mitch Bryson, Thomas Elton, Peter Lok, Associate Professor Guodong Shi, Dr Daria Anderson
This research project seeks to map how the core competencies needed by engineers in programming and computational thinking are changing as Generative AI is integrated inn education. The study will examine the perspectives of educators, industry and employers, graduate engineers and current students, and will provide new competency frameworks that inform the adaptation and evolution of introductory computing curriculums for future engineering students.
Our Experts: Dr Marcello Solomon, Professor Timothy Langrish, Graham Madsen, David Hind
Graduate Engineers are expected to have a working proficiency in AI, yet there is poor alignment between university guardrails and industry expectations for AI usage. The Major Industrial Project Placement Scheme (MIPPS) provides the optimal setting to address this gap through authentic, industry-led research. Starting with the 2026 cohort, this project will map industry AI practices to student work, and develop AI-supported workflows for teaching. This project will lead to the development of a tested framework and new teaching materials that enhance student capability with AI, improve academic integrity, and better prepare students for responsible AI usage in their career.
Our Experts: Dr Aditya Putranto
This study aims to evaluate the effectiveness of the use of daily life (contextual) examples and case studies to help students comprehend the concepts and assess the impacts of industrial based projects on the students’ performance. The project examines student perceptions of contextuality of examples, relevance of industrial based projects, engagement to the unit because of contextualisation approach (daily life examples and industrial based project), and overall learning satisfaction.
Our Experts: Dr Gobinath Rajarathnam, Dr Aditya Putranto, Maria Aira Lacson Calma, Jacob Elmasry
This project evaluates the impact of introducing a structured oral assessment into an undergraduate engineering unit by examining relationships between oral assessment performance, written report marks, final examination scores, and student experience measures.
Our Experts: Dr Hamish Fernando, Thomas Elton, Peter Lok, Dr Young No
Project Partner: University of Wollongong (Sasha Nikolic)
This project evaluates how students perceive and differently engage with instructor video feedback versus AI-restructured text feedback derived from the same spoken commentary, across a scaffolded multi-checkpoint assessment.
Our Experts: Dr Imdad Ullah
The aim of this project is to evaluate the feasibility, acceptability, and educational utility of personalized, instructor-reviewed code-understanding quizzes (known as RepoProof). This project explores how reliably and effectively customized questions and immediate feedback can be integrated into existing university course workflows. The project analyses student engagement by reviewing completion rates and response times, while gathering anonymous student feedback on quiz usefulness, clarity, and fairness.
Our Experts: Dr Thomas Chaffey
This project develops and evaluates an AI agent to help students revise material in an interactive way. The agent provides the student with quiz questions based on a section of course content, for example the assumed knowledge for a course, the material for a particular quiz, etc. The agent generates questions of varying difficulty depending on the student’s answers, and keeping within the scope of the relevant part of the course.
Our Experts: Dr Rahul Gopinath
This project builds a unit-specific AI chatbot that can be deployed for units in the school of computer science (as a pilot), grounded in educator-uploaded materials: lecture recordings, slides, and prior Ed discussion threads. Using retrieval-augmented generation (RAG) over a cloud LLM, the chatbot answers student questions by drawing directly on unit content. Educators control what the chatbot knows about the unit of study; students get accurate, on-demand support; and the instructor can learn questions course materials are failing to answer clearly.
Our Experts: Muhammad Sajjad Akbar & Dr Mohammad Polash
This project pilot is an AI-powered, gamified learning platform that combines game-based learning, adaptive AI support, and optional virtual reality (VR) experiences to create immersive, interactive learning environments. The platform generates personalised challenges, provide real-time feedback, adapt learning pathways, and enable students to learn through interactive scenarios rather than passive content consumption. The project aims to improve engagement, motivation, knowledge retention, and higher-order thinking while exploring the potential of AI and immersive technologies to transform engineering education.
Our Experts: Professor Eduardo Velloso, Dr Zhanna Sarsenbayeva, Associate Lecturer Zhongyi Bai
This project studies what happens when an AI replies on someone’s behalf before that person has seen the message. It introduces reviewable agency as an interaction model in which a stand-in answers first, then the represented person can endorse or edit the reply, and that review stays visible to the recipient. This project has been designed as a research probe for instructor-student communication, so students can get timely course help when an instructor is unavailable while the instructor still oversees what was said in their name. This work examines how later review shapes trust, how ownership and responsibility shift after endorsement or correction, and when this kind of delegated communication is acceptable.
Our Experts: Dr Imdad Ullah
This project evaluates FeedbackSelector, a rule-based decision layer embedded in an AI tutoring chat agent for a database management course in the school of computer science. The tool selects among five pedagogically distinct feedback types (hint, direct explanation, worked example, guiding question, metacognitive prompt) based on a student's behavioural signals, estimated Bloom-taxonomy level, and curriculum context, rather than responding the same way to every student. This targeted approach has the potential to personalise AI tutoring and help students learn more than traditional pure LLM tutors.
Our Experts: Dr Sichao Li, Dr Zhengyi Yang, Masahiro Takatsuka
Engineering students are increasingly use AI tools for writing, coding, and design, but generic AI assistance can bypass the cognitive work needed for mastery. This pilot will develop and evaluate a staff-supervised AI system that traces evolving learner states—mastery, misconceptions, uncertainty, help-seeking behaviour, engagement, and transfer readiness—and provides scaffolded formative feedback rather than direct answers. We will evaluate whether AI that is aware of the learner’s state improves feedback quality, student engagement, and teaching productivity while reducing solution over-disclosure, dependency, and uncalibrated confidence, creating a scalable model for AI-native engineering education.
CARE currently has two co-directors who jointly set the direction of the research centre:
CARE has also employed a research fellow to assist the planning and delivery of engineering education projects throughout the faculty:
