Honours and Masters project

This interdisciplinary project explores how artificial intelligence (AI) can support the development of trauma-informed practice among pre-service teachers. Trauma exposure affects more than two-thirds of school students and has significant implications for their academic engagement, emotional wellbeing, behaviour, and social participation at school.

Teamwork feedback tools are increasingly used in computing education to support reflection, coordination, and peer interaction. However, students often experience challenges in interpreting feedback and understanding how to act on it, particularly when interfaces are not designed with their needs in mind.

This project focuses on the user experience (UX) and interface design (UI) of teamwork feedback systems (e.g., Pulse or similar platforms). It explores how interface design influences students’ ability to interpret, trust, and act on feedback during team-based project work.

Generative AI is increasingly being used to support feedback and reflection in education. However, most current tools rely on cloud-based models, raising concerns around privacy, data governance, and trust, especially when handling sensitive student teamwork data.

This project explores the use of locally deployed generative AI (GenAI) to support teamwork feedback in computing education contexts. The focus is not only on technical feasibility, but also on how students and educators perceive, trust, and interact with AI-generated feedback when it is processed locally.

Vision-and-Language Navigation (VLN) has emerged as a promising paradigm for enabling robots to interpret natural language instructions and navigate complex environments. While most prior work focuses on single-agent navigation, this study extends VLN to multi-robot systems, addressing challenges of coordination, communication, and task allocation in dynamic settings.

Ensuring construction quality and safety requires timely detection of defects, yet traditional manual inspections are slow, costly, and inconsistent. This research presents a computer vision-driven solution that automates defect detection using images captured by drones and site cameras. By applying advanced deep learning models, the system are expected to identify cracks, corrosion, and surface irregularities with high accuracy, even under challenging site conditions.

Foundation models have shown strong reasoning abilities, but their reasoning process is often implicit, difficult to inspect, and not always reliable. This project explores neuro-symbolic reasoning as a way to make AI reasoning more structured, interpretable, and robust.

The project will study how neural models can be combined with explicit logic-based reasoning. A neural model may be used to understand or decompose a problem, while a symbolic reasoning component performs structured inference using logic, rules, programs, or other formal representations.

Large Language Models (LLMs) are increasingly used to automatically evaluate other AI systems in tasks such as writing, reasoning, and question answering. This approach called LLM-as-a-Judge is now widely used in research benchmarks and AI development pipelines.

Evaluation benchmarks are a foundational component of artificial intelligence (AI) research, providing standardized ways to measure and compare the capabilities of AI systems. Benchmarks such as MMLU, GSM8K, HumanEval, and HellaSwag have been instrumental in tracking progress in large language models and related systems. However, benchmark usefulness is not static.

Our research explores novel map representations and projections.

This project seeks to design and trial new map representations for seeing Australian population data sets in new and ideally more effective ways. 

Why is this needed? 

Large Language Models (LLMs) such as GPT, Llama, Qwen, and Mistral are increasingly used in commercial and academic applications. As more models become available, identifying which model generated a particular response becomes important for copyright auditing, model verification, and AI transparency.

Current fingerprinting methods often rely on manually selected benchmark questions. However, manually designing discriminative questions is time-consuming and may not capture unique behavioral differences between models.