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What students really think: unpacking AI ethics in educational assessments through a triadic framework

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Udgivet i:International Journal of Educational Technology in Higher Education vol. 22, no. 1 (Dec 2025), p. 56
Hovedforfatter: Lim, Tristan
Andre forfattere: Gottipati, Swapna, Cheong, Michelle
Udgivet:
Springer Nature B.V.
Fag:
Students
Assessments
Educational evaluation
Ontology
Systems design
Automation
Privacy
Cognitive ability
Ethics
Research & development > R&D
Feedback
Cognition & reasoning
Accountability
Artificial intelligence
Educational objectives
Education
Instructional scaffolding
Personalized learning
Literature reviews
Surveillance
Perceptions
Efficiency
Fairness
Risk assessment
Teaching
Ethical dilemmas
Morality
Frame analysis
Evaluation
Learning
Management
Effectiveness
Structural equation modeling
Learning Analytics
Dropout Rate
Intelligent Tutoring Systems
Influence of Technology
Diagnostic Tests
Learning Theories
Educational Technology
Grading
Student Experience
Network Analysis
Integrity
Interpersonal Relationship
At Risk Students
Instructional Effectiveness
Adaptive Testing
Educational Assessment
Formative Evaluation
Outcomes of Education
Language Processing
Data Processing
Algorithms
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Resumen:The rise of AI in educational assessments has significantly enhanced efficiency and accuracy. However, it also introduces critical ethical challenges, including bias in grading, data privacy risks, and accountability gaps. These issues can undermine trust in AI-driven assessments and compromise educational fairness, making a structured ethical framework essential. To address these challenges, this study empirically validates an existing triadic ethical framework for AI-assisted educational assessments, originally proposed by Lim, Gottipati and Cheong (In: Keengwe (ed) Creative AI tools and ethical implications in teaching and learning, IGI Global, 2023), grounded in student perceptions. The framework encompasses three ethical domains—physical, cognitive, and informational—which intersect with five key assessment pipeline stages: system design, data stewardship, assessment construction, administration, and grading. By structuring AI-driven assessments within this ethical framework, the study systematically maps key concerns, including fairness, accountability, privacy, and academic integrity. To validate the proposed framework, Structural Equation Modeling (SEM) was employed to examine its relevance and alignment with learners' ethical concerns. Specifically, the study aims to (1) evaluate how well the triadic framework aligns with learners' perceptions of ethical issues using SEM analysis, and (2) examine relationships among the assessment pipeline stages, ethical considerations, pedagogical outcomes, and learner experiences. Findings reveal robust connections between AI-assisted assessment stages, ethical concerns, and learners' perspectives. By bridging theoretical validation with practical insights, this study emphasizes actionable strategies to support the development of AI-driven assessment systems that balance technological efficiency, pedagogical effectiveness, and ethical responsibility.
ISSN:2365-9440
1698-580X
DOI:10.1186/s41239-025-00556-8
Fuente:Political Science Database