The Paradigm Shift in Education in Big Data Era: Exploring the Intersection of Historical Assessment Frameworks and AI-Powered Assessment Methods in Education

Abstract

This paper delves into the dynamic intersection between traditional assessment processes and AI-powered technology assessment models within the expansive landscape of the big data era. Tracing the origins of large-scale standardized testing, from Francis Galton's pioneering intelligence testing proposals to the subsequent development of norm-referenced testing (NRT) by Alfred Binet and Theodore Simon, the historical trajectory underscores the evolution towards authentic assessment paradigms such as computerized adaptive testing (CAT) and performance-based assessment (PBA). These shifts reflect a broader aim of equipping educators with tools to measure student learning effectively and foster continuous learning beyond the confines of the classroom. However, despite the proliferation of methods integrating big data and AI technologies in educational settings, a discernible gap persists between technological capabilities and their practical implementation in education, notably in the realm of student assessment and evaluation. This gap highlights challenges in enhancing assessment accuracy and reliability, evaluating non-cognitive skills, and delivering personalized feedback. The introduction of AI-powered technologies like ChatGPT raises ethical considerations regarding fairness, privacy, and bias, necessitating responsible and equitable deployment.

This paper addresses fundamental research questions concerning the historical influences on assessment frameworks, the role of AI-powered technology in driving paradigm shifts, and the ethical considerations surrounding its application. Through a comprehensive literature review, it explores the potential of AI technology to enhance assessment accuracy and reliability while advocating for its responsible use. Despite the challenges posed by traditional assessment processes and AI-powered technology assessment models, educators continually strive to improve student learning and readiness for lifelong learning.

By celebrating the invaluable contributions of educators, we can shift the narrative surrounding assessment practices towards a more positive and supportive environment, fostering ethical and innovative uses of AI-powered technology. This celebration serves as inspiration for future research and development in assessment, emphasizing the importance of building upon educators’ innovative practices to shape the future of assessment in the big data era.