Our Computing Education research focuses on advancing evidence-based approaches that transform the student experience and shape the future of learning. This research theme focuses on pedagogical and technical innovation, ensuring our teaching methods and curricular frameworks remain robust, inclusive, and highly effective in an evolving academic landscape. This research has taken on new urgency as recent advances in Generative AI introduce massive shifts in how students learn and work. Rather than treating these technological disruptions in isolation, we view them as catalysts within our broader research agenda, enabling us to rethink assessment, deepen student engagement, and design rigorous, data-driven frameworks that prepare learners to thrive in an increasingly complex digital world.
Our research spans across multidisciplinary research
Our research addresses student disengagement by developing data-driven strategies to identify and support students before they fall behind. We focus on implementing strategies to identify high-risk students early in their academic journey, allowing us to deploy targeted, predictive interventions tailored to specific clusters of Absent Fail (AF) students. By understanding the unique needs of these student segments, we can provide the precise support they need to get back on track. Ultimately, our goal is to drive wider adoption of these innovative approaches across the entire Faculty of Education, establishing a unified framework to identify common patterns of disengagement and elevate student success across all disciplines.
This research will contribute to reducing Absent Fail (AF) rates by identifying patterns of student disengagement and enabling earlier, targeted interventions. By identifying distinct groups of at-risk students, educators will be tailor support for students who are struggling to engage with different needs. The predictive models and intervention strategies developed during this research will provide a scalable approach to identifying at-risk students, with the potential for wider implementation across the Faculty of Education to improve student engagement and academic success.
Elliot Varoy, Professor Irena Koprinska
We explore improvements to student assessment using AI. This includes how students are assessed, how they are marked, and how they are given feedback. In each case, our work enhances the abilities of educators while ensuring the assessment process is still human-centred. For example, marking decisions are still made by educators, but with AI tools assisting. As well as enabling new assessment methods, our work introduces innovations in Natural Language Processing (NLP).
This work enables more accurate assessment of student learning. That provides employers with confidence in the knowledge of graduates, and provides students with better opportunities to demonstrate their skills and knowledge.
Dr Jonathan Kummerfeld, Dr Mohammad Polash, Jose Meza, Dr Mary Lou Maher, Associate Professor Anusha Withana
AI literacy, building on digital literacy, extends beyond technical expertise by encompassing the diverse knowledge and competencies needed to understand, interact with, and critically assess AI systems. This research establishes a 4-Pillar approach to teaching AI Literacy: (1) Knowledge of technical AI concepts, (2) Skills in responsible interaction with Generative AI, (3) understanding of the ethical issues in the use of AI, (4) critical analysis of the impact of AI. This research develops instruments to evaluate students' self-assessment of their AI Literacy and their integration of AI into their learning experience.
This research will contribute to the development of AI Literate graduates. This will prepare students to enter industry with a strong understanding of best practice artificial intelligence. The results will inform the implementation of the 4-Pillar approach to teaching AI Literacy. The instruments being designed and implemented in first-year computing during this research, will be developed into transferrable modules for wider impact within faculties across the University.
Dr Mary Lou Maher, Elliot Varoy
Generative AI is fundamentally reshaping engineering by automating routine tasks and altering the core skills required for entry-level roles. This research addresses the critical disconnect between current university curricula and these evolving workforce demands. Adopting a robust mixed-methods design, the study triangulates data from a quantitative analysis of industry job postings with qualitative consultations involving students, academics, and hiring managers. This investigation identifies specific “AI-assisted competencies”, such as prompt engineering, ethical oversight, and collaborative problem-solving, that are becoming essential for professional practice. By providing actionable recommendations for updating course designs and learning outcomes, this research aims to produce future-ready graduates who can effectively and responsibly collaborate with AI, ensuring their competitiveness in a rapidly changing professional landscape.
This research is contributing to engineering education by highlighting critical gaps between existing curricula and the rapidly evolving demands of AI-driven industries. It provides an evidence-based understanding of emerging AI-assisted competencies, informing curriculum discussions, teaching practices, and the development of graduate capabilities. By connecting industry expectations with academic perspectives, the research strengthens dialogue between universities and employers, promoting more relevant and responsive engineering education. Its emphasis on human-AI collaboration, ethical judgement, and professional adaptability is shaping discussions around graduate readiness, responsible AI integration, and the transformation of engineering education in an increasingly AI-enabled workforce.
Dr Mohammad Polash, Muhammad Sajjad Akbar, Dr Armin Chitizadeh, Mr Jose Meza, Dr Xi Wu
Our research focuses on leveraging generative AI to transform engineering and computer science education through interactive learning, intelligent assessment, and pedagogical evaluation. We design AI-powered systems that enable active learning experiences that improve student engagement, confidence, and retention in programming education. We also develop robust AI-driven methods for digitising and interpreting handwritten assessments, aiming to streamline grading workflows and improve accuracy across diverse exam formats. In addition, our work explores the use of generative AI to evaluate and refine notional machines in computer science education, providing structured frameworks for assessing pedagogical clarity and effectiveness. By combining learning sciences with AI system design, our research advances scalable, evidence-based educational tools that enhance both teaching and learning outcomes. Ultimately, we aim to build intelligent, adaptable, and impactful AI systems that support the future of CS education.
This research is advancing engineering and computer science education by demonstrating how generative AI enhances student engagement, personalised learning, and assessment practices. AI-powered interactive learning tools are supporting active participation, strengthening programming confidence, and addressing challenges in student retention. Intelligent assessment methods are improving the efficiency, consistency, and scalability of handwritten exam evaluation, reducing educators' administrative workload. Furthermore, AI-driven pedagogical evaluation is providing new insights into the effectiveness of notional machines and programming instruction. By integrating AI innovation with evidence-based pedagogy, this research is contributing to more accessible, engaging, and effective learning experiences while supporting educators in adopting responsible and scalable AI-enabled teaching practices.
Dr Mohammad Polash, Muhammad Sajjad Akbar
