Keynote Speakers

Professor Elizabeth McKinley is Professor of Indigenous Education at the University of Melbourne. Prior to returning to the Faculty of Education she was the Executive Director of the Atlantic Fellows for Social Equity (AFSE), a program for Indigenous leadership. She is known for her work exploring the interaction between science, education and Indigenous culture. She has a strong research and publication record in the field of sociology of education, Indigenous science education, and Indigenous curriculum.  Before moving to Melbourne in 2014 she was Professor of Māori Education and Director of the Starpath Project for Tertiary Participation and Success at the University of Auckland. Professor McKinley has served on a number of national panels and committees. She has received a New Zealand Honour as an Officer to the New Zealand Order of Merit (ONZM).

Title: A decolonizing approach to assessment: Higher Education and Indigenous student outcomes

Abstract

Assessment systems are largely cultural products of a society. The content that is assessed reflects the needs, skills and knowledge valued by the society who determines the education system. In settler colonial societies (Canada, Australia, New Zealand and the United States of America) Indigenous students are identified as ‘not achieving’ or ‘underachieving’. Such descriptors produce a discourse of a gap between Indigenous and non-Indigenous students. This ‘gap’ is interpreted by policymakers as a ‘crisis’ and something to be solved. However, proposed solutions often target single issues and people become concerned that the gaps do not close. While narratives of assessment and achievement for Indigenous students are complex, in this talk I will briefly review the current international research literature on assessment and Indigenous students. The task of getting reliable data is problematic because countries/states do not always identify and collect (or make public) Indigenous student outcomes. Furthermore, published research on initiatives trying remedy issues are not always readily available. With increasing pressure on educational institutions to respond to the needs of Indigenous students, including concerns about attrition, retention and completions, there is an expectation to research and find practices that support their success. I will argue there are practices that develop a strengths-based focus of Indigenous student achievement.

Dr. Hua-Hua Chang, Charles R. Hicks Chair Professor of Education at Purdue University, is a leading expert in Educational Measurement. His groundbreaking research spans both theoretical and applied domains, including Computerized Adaptive Testing, Cognitive Diagnosis, and Differential Item Functioning. Dr. Chang is also at the forefront of developing innovative web-based assessment tools to enhance personalized learning.

He has served as President of the Psychometric Society and is a fellow of both the American Educational Research Association (AERA) and the American Statistical Association (ASA). His distinguished career has been recognized with numerous prestigious awards, including the 2017 E. F. Lindquist Award (AERA and ACT), the 2021 NCME Career Contribution Award, and the 2024 Samuel J. Messick Award for Distinguished Scientific Contributions (APA Division 5). Dr. Chang also served two times as Fulbright Specialist, Australia (2005) and Colombia (2019).

Title Exploring the Role of Computerized Adaptive Testing (CAT) in the Era of Generative Artificial Intelligence (Gen-AI)

Abstract

Over the past 50 years, the theory and methods of Computerized Adaptive Testing (CAT) have been extensively developed, and advancements in technology have facilitated large-scale CAT implementation. However, CAT remains relatively unknown among researchers in Generative Artificial Intelligence (Gen-AI). This paper explores the potential of CAT to enhance Gen-AI applications in education, particularly in designing personalized learning pathways and delivering tailored insights to students. We discuss how CAT-based assessments can optimize individualized learning experiences while balancing detail and brevity in instructional feedback. In an open-source Gen-AI environment, we will demonstrate the feasibility of developing reliable and affordable AI-powered diagnostic tools that allow schools to automatically assess students’ mastery levels for any set of cognitive skills. Additionally, we will showcase how these tools can facilitate individualized learning on a large scale.

Xiaoming Xi is Director at the Hong Kong Examinations and Assessment Authority, leading the Assessment Technology and Research Division, the Education Assessment Services Division and the International and Professional Examinations Division. Previously she was Executive Director of New Product Development at ETS in the U.S. responsible for Global Education and Workforce products and Senior Director in R&D overseeing the research and development of global and U.S. language tests and assessments.  Her research leadership has impacted global large-scale tests such as ETS’s TOEFL, TOEIC and higher education tests as well as Hong Kong’s tests for college admissions, teacher certification and students’ progress monitoring.

A strong contributor to the educational assessment community, Xiaoming has been a Council Member of the International Test Commission (ITC) since September 2023. She has also served on the Executive Board of the International Language Testing Association (ILTA), chaired various ILTA award committees, and currently chairs the ILTA By-Laws Committee.

Xiaoming has been on the Editorial Board of several leading assessment journals, and has won multiple awards, including the 2015 Top 25 Women in Higher Education and Beyond, the Sage/ILTA Best Book Award, the ILTA Best Language Testing Paper Award, the ETS Scientist Award, the ETS Presidential Award, etc.

Xiaoming has published widely in assessment theories and practices including validity, fairness, construct definition, assessment design, human and automated scoring, and AI technology. She is best known for her work in the development and research of AI engines, ethical and responsible use of AI in assessment and learning, assessment validity theories and fairness frameworks, and evaluation frameworks for adaptive learning solutions. She has guest edited two special issues “Automated Scoring and Feedback Systems” and “Advancing Language Assessment with AI and ML” for Language Testing and Language Assessment Quarterly respectively and has multiple patents in applications of AI.