CONTINUOUS, ADAPTIVE, AND LEARNER-CENTRED EVALUATION

Abstract

This paper explores the emergence of continuous assessment in the age of artificial intelligence (AI) and the creation of adaptive, personalised and data-driven assessment. Continuous assessment has been acknowledged for its formative and learning-monitoring features, but practice is often constrained by a range of issues, including timely feedback, personalised assessment, teacher load, and assessment subjectivity. The paper draws on constructivist learning, mastery learning and assessment for learning to develop the use of artificial intelligence (AI) technologies, including machine learning, learning analytics and automatic feedback, to address these issues. The study demonstrates the development of an AI-Driven Adaptive Continuous Assessment Model (AIDACAM), which involves learner data collection, data analysis, adaptive assessment tasks, personalised feedback, and data dashboards for teachers. The model facilitates timely feedback, adaptive assessment and data-driven learning practises. Moreover, the research explores the potential of AI-based assessment, including the benefits of efficiency, feedback and learning outcomes, as well as ethical considerations around data privacy, algorithmic bias and fairness. In all, the authors conclude that the application of AI technologies has the potential to enhance continuous assessment practises if applied with pedagogically and ethically sound approaches.