A Comprehensive Survey on Traditional and Automated Tools for Academic Learning Difficulties

Early identification of academic learning difficulties (ALD) is crucial for ensuring timely interventions and improving educational outcomes. While traditional screening tools have long been in use, advancements in machine learning (ML) offer promising alternatives. This review compares the effectiveness of traditional and automated approaches in identifying ALD among young children. It explores the strengths and limitations of traditional tools, the potential of ML algorithms to enhance early identification, the challenges of implementing ML in educational environment, and the ethical considerations and policy implications associated with these tools. The findings provide valuable insights for educators, policymakers, and researchers aiming to improve early identification and support for children with ALD.