2023-09-13, 09:40– (Asia/Tokyo), Training Room 2
This systematic literature mapping study aim to provide practical insights on the ethics of artificial intelligence (AI) in assessment. It is important to study the divide between what may be ethically permissible and not permissible, especially in fundamental societal institutions like education, when teaching practitioners or researchers apply AI in academic processes such as assessments. This study applied a systematic literature mapping methodology to scour extant research, so as to holistically structure the landscape into explicit topical research clusters. Through topic modelling and network analyses, research mapped key ethical principles to research archetypical domains, and reviewed the influence of these ethical principles in each thematic domain. Results of this study identified five key research archetypical themes, with presence across the system layers of cognitive, information and physical domains of an AI-based assessment pipeline, namely: (i) AI system design and check for assessment purposes; (ii) AI-based assessment construction and rollout; (iii) data stewardship and surveillance; (iv) administration of assessments using AI systems; and (v) AI-facilitated assessment grading and evaluation. Ten AI ethics principles, namely, (i) fairness, (ii) privacy, (iii) explainability, (iv) accountability, (v) accuracy, (vi) inclusivity, (vii) trust, (viii) human centricity, (ix) auditability and (x) cheating, epitomize the key ethics considerations across each of the five research themes; each manifesting varying levels of importance. The findings of this research can provide researchers and practitioners the insights into the application methods of AI in assessments and their intertwined ethical challenges, and in particular, the generalizable key research themes structured across the assessment pipeline, for follow up studies.
This study considers Artificial Intelligence in Education (AIED), particularly in assessments, and their relationship with ethics. The study uses a systematic literature mapping approach, leveraging on topic modeling and network analysis, to identify key research themes to focus on for future research. The study also proposes a framework to generalize ethics within AI-based assessments.
Artificial Intelligence in Education (AIED), Assessment, Ethics, Systematic Literature Mapping
Tristan is a Lecturer at the School of Business Management Banking and Finance department, and a member of the Institutional Review Board of Nanyang Polytechnic. His research interest lies in Finance, Financial Technology and Educational Technology. Tristan has experience spanning the private and public sector, in the areas of investment management, corporate finance, venture capital and technology advisory. Tristan holds postgraduate degrees in Finance, IT and Engineering from The University of Sydney, Singapore Management University and National University of Singapore. He is a CFA and CAIA charter holder, and a member of the National Advisory Council of the Chartered Institute for Securities and Investment (U.K.).