Trustworthy AI Meets Educational Assessment: Challenges and Opportunities

Abstract

Artificial intelligence (AI) has made substantial impacts in numerous fields, including education. Within education, learning and assessment are two key areas. Although many AI techniques have been applied to improve teaching and learning, their potential in educational assessment remains under-explored. This paper explores the intersection of AI and educational assessment and presents a rich landscape of challenges and opportunities, especially in the context of trustworthy AI, including fairness, transparency, accountability, explainability, and robustness. We will begin by outlining the foundations of trustworthy AI and educational assessment. Next, we will delve into the application of trustworthy AI for various assessment tasks, such as test item generation, test design, and automated scoring. In addition, the talk will also discuss how insights from educational measurement theory, such as item response theory (IRT) and validity frameworks, can inform the development and evaluation of trustworthy AI models. These frameworks help ensure that AI systems in education are not only accurate, but also equitable and aligned with educational goals. Finally, we will highlight future research directions, focusing on the integration of ethical AI principles into educational technology and the need for interdisciplinary collaboration to tackle the emerging challenges in this field. The aim is to foster a new generation of AI-powered educational tools that are both innovative and trustworthy, ultimately contributing to a more equitable and more effective educational landscape.