Shahid Iqbal

School of Engineering and Technology
Information and Computing Sciences
Dr Ahsan Morshed, Dr Yufeng Lin & Dr Mohammad Saiedur Rahaman
Masters by Research
0009-0005-3176-3750
Muhammad Shahid HDR Candidate

Research Details

Thesis Name

Artificial Intelligence-Assisted Retrospective Analysis of Childhood Safety Incident Data to Inform National Quality Framework Improvments

Thesis Abstract

This thesis will investigate how childcare services can use existing operational data to improve child safety through early identification of risk indicators. The research will  explore patterns such as staffing changes, busy operational periods, ratio pressures, and repeated hazards to determine how data-driven insights can support timely  decision-making. The study aims to develop a practical framework that strengthens incident prevention, reporting, follow-up actions, and continuous improvement.

Why My Research is Important/Impacts

This research aims to help childcare services improve child safety by using the data they already collect through daily operations, digital systems, and service management tools. The key impact is early identification of warning signs before incidents occur, such as pressure during busy drop-off periods, staffing changes, ratio risks, or repeated hazards. If successful, the research could reduce injuries, repeated safety issues, complaints, and incident-management costs. It may also support compliance with the National Quality Framework by improving reporting, follow-up actions, and continuous improvement. For families, the benefit is greater trust and confidence. For childcare providers, stronger safety performance may support reputation, enrolments, and sustainable business growth.