Bridging Research and AI for Impact Narratives
- Diego Lozano(Participant)
Activity:
Transferencia de Conocimiento
Difusión como Panelista, conferencia magistral
The Research Evaluation Group (INORMS) and the REACH Network are pleased to invite you to a Global Symposium on “AI in Research Assessment”, taking place 11 February 2026. This event brings together leading voices in research policy and evaluation, to explore one of the most urgent questions facing the scholarly community: Can AI assess research?
Artificial intelligence promises to reshape how research is evaluated. Emerging AI tools can generate detailed, human-like assessment - opening exciting opportunities to reduce administrative burden, support evaluation at scale, and uncover new insights. At the same time, these technologies raise concerns around accuracy, transparency, equity, and the protection of research integrity.
The symposium arrives at a moment when the global research community is already rethinking evaluation practices. As assessment evolves beyond narrow bibliometric indicators, researchers and institutions increasingly recognize the importance of mentorship, open science, societal and economic contributions, and collaborative work. How might AI support these responsible approaches - and under what safeguards?
Our aim is to engage critically with both the promise and the pitfalls of AI-assisted evaluation, asking:
• When can AI meaningfully contribute to research assessment?
• What standards, governance, and oversight are required?
• Which tasks should remain firmly in the hands of peer-review?
Artificial intelligence promises to reshape how research is evaluated. Emerging AI tools can generate detailed, human-like assessment - opening exciting opportunities to reduce administrative burden, support evaluation at scale, and uncover new insights. At the same time, these technologies raise concerns around accuracy, transparency, equity, and the protection of research integrity.
The symposium arrives at a moment when the global research community is already rethinking evaluation practices. As assessment evolves beyond narrow bibliometric indicators, researchers and institutions increasingly recognize the importance of mentorship, open science, societal and economic contributions, and collaborative work. How might AI support these responsible approaches - and under what safeguards?
Our aim is to engage critically with both the promise and the pitfalls of AI-assisted evaluation, asking:
• When can AI meaningfully contribute to research assessment?
• What standards, governance, and oversight are required?
• Which tasks should remain firmly in the hands of peer-review?
Activity Information
Activity type
Difusión como Panelista, conferencia magistral