Emotional Intelligence (EI) is now recognised as a strategic organisational capability, central to leadership, talent development, engagement and retention. In modern workplaces, EI sits alongside technical and cognitive skills as essential for resilience, collaboration and performance.
Mentoring is increasingly positioned as a powerful way to develop EI at scale, moving beyond traditional one-to-one models towards mentoring ecosystems that include peer, group, reverse, situational and reciprocal mentoring. These approaches are especially valuable for developing empathy, emotional awareness and self-regulation. For Gen Z and Higher Education contexts, EI is particularly important in managing stress linked to societal pressures, AI, automation and hybrid working. Strong EI supports confidence, resilient relationships and reduced burnout. Learning OutcomesBy the end of the workshop, participants will be able to:
The workshop will use mentoring case studies, practical activities and digital approaches to explore how mentors develop their own EI and support mentees’ EI development within wider developmental networks
Mentoring program managers are being asked to do more with less, more participants, more data, more reporting, fewer hours. AI tools can help, but only if you know where to apply them and where to be cautious.
This workshop integrates a proven five-step program design framework with a critical, hands-on examination of AI applications, from goal-setting and matching to data collection and stakeholder reporting.
The morning session builds your program's foundation: defining purpose, mapping theory of change, and recruiting and preparing participants. You will stress-test your program against international quality standards and identify where AI creates genuine efficiency.
The afternoon Comparison Lab puts that analysis to work. Small teams tackle identical program management tasks across different AI platforms, then debrief: Where did the tools help? Where did they hallucinate or introduce bias? What should never be delegated to an algorithm?
You leave with a decision framework, practical workflows, and clearer judgment about responsible AI adoption in your program context.
No technical background required, only a willingness to experiment.
Learning outcomes