A framework using self-rationalization, attribution analysis, and a certification metadata schema with traffic-light workflow enables transparent, audit-ready AI-generated educational assessments aligned to Bloom's and SOLO taxonomies.
Title resolution pending
3 Pith papers cite this work. Polarity classification is still indexing.
3
Pith papers citing it
citation-role summary
background 1
citation-polarity summary
years
2026 3roles
background 1polarities
support 1representative citing papers
Twelve semistructured interviews yield twelve knowledge-based design requirements for tutoring generative social robots, grouped into self-knowledge, user-knowledge, and context-knowledge categories.
Hiding generative AI use to signal expertise reduces knowledge sharing and transparency among workplace colleagues.
citing papers explorer
-
Explainability and Certification of AI-Generated Educational Assessments
A framework using self-rationalization, attribution analysis, and a certification metadata schema with traffic-light workflow enables transparent, audit-ready AI-generated educational assessments aligned to Bloom's and SOLO taxonomies.
-
Knowledge-Based Design Requirements for Generative Social Robots in Higher Education
Twelve semistructured interviews yield twelve knowledge-based design requirements for tutoring generative social robots, grouped into self-knowledge, user-knowledge, and context-knowledge categories.
-
"If You're Very Clever, No One Knows You've Used It": The Social Dynamics of Developing Generative AI Literacy in the Workplace
Hiding generative AI use to signal expertise reduces knowledge sharing and transparency among workplace colleagues.