OPPORTUNITIES AND CHALLENGES OF INTEGRATING MACHINE LEARNING FOR THREAT PREVENTION IN SECONDARY SCHOOLS IN ANAMBRA STATE

Abstract

The main purpose of the study was to examine the opportunities and challenges of integrating
Machine Learning (ML) for threat prevention in secondary schools in Anambra State. Two research
questions guided the study and one hypothesis was tested. The descriptive survey design was adopted
for the study. The population of the study comprised 267 public secondary school principals in
Anambra State. Census Sampling method was adopted for the study. A selfstructured questionnaire
was developed and used by the researchers to collect relevant data for the study. The instrument was
validated by three research experts and the instrument was subject to a trial test. Mean, standard
deviation and one-sample t-test was used to analyze data. The finding of the study revealed that ML
presents substantial opportunities for enhancing school security, including real-time surveillance,
behavioural analysis and predictive threat detection. However, the study also identified several
challenges hindering effective ML implementation, such as inadequate infrastructure, limited
technical expertise, data privacy concerns and resistance to technology adoption. Furthermore, the
null hypothesis was rejected, indicating a significant influence of ML on threat prevention. Based on
these findings, the study recommends increased investment in security infrastructure, capacity
building for school staff and awareness campaigns to enhance stakeholder involvement in ML
integration.