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. Although trauma-informed professional development programs are increasingly implemented in educational settings, many existing approaches are difficult to sustain, insufficiently personalised, or disconnected from the everyday realities of teaching practice.

This project will investigate the feasibility, acceptability, and educational potential of an AI-supported trauma-informed professional learning program designed for pre-service teachers. The study forms part of a proof-of-concept initiative led collaboratively by researchers from the Faculty of Education and the Faculty of Information Technology at Monash University.

The project sits at the intersection of trauma-informed education, learning analytics, human-centred AI, educational technology, and teacher professional learning. Students undertaking this thesis may contribute to the design, evaluation, or analysis of AI-supported learning experiences and investigate how digital systems can provide scalable and responsive support for educators working with trauma-affected students.

Depending on your research trajectory, you may investigate questions such as:

• How can AI-supported systems personalise trauma-informed professional learning for pre-service teachers?
• What patterns of engagement emerge from participants’ interactions with AI-supported learning tools?
• How do pre-service teachers perceive the usefulness, accessibility, and trustworthiness of AI-generated supports?
• How can conversational AI or adaptive feedback systems support educators in responding to trauma-related classroom scenarios?
• What ethical, pedagogical, and human-centred design considerations arise when integrating AI into trauma-informed education?
• How can qualitative and quantitative evidence be combined to evaluate the effectiveness and acceptability of AI-supported professional learning programs?

Possible thesis contributions

Students may focus on one or more of the following areas:

• Design and evaluation of AI-supported educational tools
• Learning analytics for professional learning environments
• Human-centred AI for teacher support systems
• Conversational AI and adaptive feedback systems
• Trauma-informed educational technologies
• Mixed-methods evaluation of digital learning interventions