The changing needs of learners within the twenty-first century are increasingly threatening traditional practices of assessment - often limited to standardized, periodic assessments. This research examines how artificial intelligence (AI) and data analytics can change our summative and formative assessments. The paper first describes traditional assessment practices and outlines the evolution of new technologies in classrooms. The research also contemplates how AI tools enhance automated grading systems, adaptive learning, and immediate feedback systems using machine learning algorithms, natural language processing, and predictive analytics. This paper highlights the potential for learning management systems (LMSs) to provide individuals learning pathways and evidence-based instructional decision making; it explores multiple types of educational data and how they combine together. Through case studies and real-world implications at scale, the argument is made that using AI and data-driven assessment can support greater educational equity, effectiveness, and learner engagement. The paper also looks at how new technology can support better learning outcomes across contexts and decrease administrative burden on educators, while also allowing for a more informed decision-making in their pedagogy. Finally, it provides actionable recommendations for how technologists, educators, and policymakers, can ethically utilize A.I. to promote assessment frameworks fit for the future.
Kumar et al. (Thu,) studied this question.