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

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Detalles Bibliográficos
Publicado en:	International Journal of Educational Technology in Higher Education vol. 22, no. 1 (Dec 2025), p. 56
Autor Principal:	Lim, Tristan
Outros autores:	Gottipati, Swapna, Cheong, Michelle
Publicado:	Springer Nature B.V.
Materias:	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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