SPEAKERS
 Salvatore Moccia |
DESCRIPTION
This panel examines how learning can be reliably assessed when AI tools can produce most of the outputs education has traditionally used as evidence of learning. A well-written essay, piece of code, analysis or professional report may no longer tell us what the learner understands, what they can do independently or how well they can exercise judgement. This shift challenges a long-standing assumption in education that the quality of a submitted product reliably reflects the capability of the learner who produced it.
In response, this panel brings together perspectives from education, professional practice and applied learning, with expertise spanning assessment, competence development and responsible AI use. The discussion focuses on what should count as credible evidence of learning and competence, and how assessment can make reasoning and judgement more visible while remaining practical across different educational contexts. It focuses on whether existing assessment strategies still measure the human capability they aim to measure when AI use cannot be ruled out. It addresses questions of credential validity and what education providers and professional bodies can credibly certify as performance increasingly combines independent human capability with AI-supported work.
This session is part of the UNU Macau AI Conference 2026.
To view the full program, please visit the program page.
To join the Conference, please visit the registration page.
AI × Education: AI for Learning, Learning for AI
Nov. 25-26, 2026 | Macau SAR, China
AI Conference 2026 | AI for SDGs - Global Youth AI Future Innovation Competition | AI × Education Workshops
