Honours and Minor Thesis projects

Are you ready to dive into the future of education and revolutionise how software projects are assessed? Join this innovative project aimed at creating cutting-edge learning analytics capabilities within the Faculty of IT at Monash University.

This project aims to enhance student engagement and comprehension by combining Data Comics with Generative AI. Data Comics present complex information in an engaging, accessible format, and by leveraging AI, we seek to automate their creation, making the process efficient and scalable.

LLMs have enabled us to break the performance ceiling in several tasks. Language Agents took this further by connecting language models with tools, environments and enriched them with memory and feedback.

Web is filled with content, and language agents (as an emerging family of AI systems) are still far from capable in tapping into the information available on the web during their course of action. This project will move on this exciting direction by building a language agent that for any given web page can (1) write a python crawler on-the-fly, and (2) identify its core content.

In education, writing is a prevalent pedagogical practice employed by teachers and instructors to enhance student learning. Yet, the timely evaluation of students' essays or responses represents a formidable challenge, consuming considerable time and cognitive effort for educators. Recognizing the need to alleviate this burden, Automatic Essay Scoring (AES) has emerged, which refers to the process of using machine learning techniques to evaluate and assign scores to student-authored essays or responses.

In education, writing is a prevalent pedagogical practice employed by teachers and instructors to enhance student learning. Yet, the timely evaluation of students' essays or responses represents a formidable challenge, consuming considerable time and cognitive effort for educators. Recognizing the need to alleviate this burden, Automatic Essay Scoring (AES) has emerged, which refers to the process of using machine learning techniques to evaluate and assign scores to student-authored essays or responses.

This could be a one-term or two-term minor thesis project for master students. The main task is to explore the privacy issues in practical smart meter systems and propose a solution to address the issue. 

As the number of mental health patients increases, the demand for qualified counselors is on the rise. However, training/practice sessions with actual patients are often limited, let alone meeting a sufficient number of patients of different personalities. This project aims to use large language models to simulate therapy sessions under certain predefined circumstances. This project is co-supervised by a collaborator from the Psychology department in Jeffrey Cheah School of Medicine and Health Sciences.

A recommender system is a subclass of information filtering/retrieval system that provides suggestions for items that are most pertinent to a particular user without an explicit query. Recommender systems have become particularly useful in this information overload era and have played an essential role in many industries including Medical/Health, E-Commerce, Retail, Media, Banking, Telecom and Utilities (e.g., Amazon, Netflix, Spotify, Linkedin etc).

Chatbots for mental health are shown to be helpful for preventing mental health issues and improving the wellbeing of individuals, and to ease the burden on health, community and school systems.  However, the current chatbots in this area cannot interact naturally with humans and the types of interactions are limited to short text, predefined buttons etc. In contrast, psychologists in real-world interact with patients with multiple modalities, including accustic and visual information.