Abstract
As artificial intelligence becomes embedded in how institutions identify, design, and monitor transnational education (TNE) partnerships, there is a growing risk that “quality” is reduced to what fits on a dashboard: recruitment targets, progression rates, and survey scores. This session argues that, particularly in the Asia-Pacific context, sustainable TNE depends on care before code: partnership models where human judgment, contextual knowledge, and shared accountability remain central to quality assurance. Drawing on experience from a Canadian university collaborating with Asia-Pacific partners on joint and dual programs and other TNE arrangements, the session explores how AI-enabled tools intersect with governance and oversight. It highlights key moments where human interpretation must reshape what AI-generated indicators appear to say: assessing institutional and regulatory readiness, understanding local pedagogical and cultural norms, interpreting uneven student outcome data, and responding to early warning signs in collaborative programs. Participants will gain practical ideas for embedding care, trust, and mutual responsibility into TNE agreements, structures, and review processes, while using AI to support—rather than define—judgments about quality and risk. The session invites practitioners to treat AI as a tool in quality assurance, not the arbiter of partnership value.
Learning objectives
By the end of this session, participants will be able to:
- Map key stages in the TNE partnership lifecycle—concept development, approval, delivery, monitoring, and renewal—and identify where human judgment must qualify or override AI-generated quality indicators.
- Describe at least two strategies for embedding care, trust, and shared accountability into TNE governance and quality assurance with Asia-Pacific partners, such as joint academic boards, co-developed metrics, or context-sensitive risk reviews.
- Formulate guiding questions to assess AI-enabled tools used in TNE partnerships (e.g., dashboards, risk models, progression analytics) to ensure they support, rather than replace, ethical, human-centred decision-making.
- Apply insights from case examples to refine one existing or planned TNE partnership process so that AI is positioned as a supportive instrument within a broader, care-oriented quality assurance framework.
Target audience
Staff and leaders involved in transnational education partnerships, academic program development, and quality assurance, especially those collaborating with Asia-Pacific institutions and integrating AI-enabled tools into governance, risk review, or cross-border program oversight.
