This research investigates how AI-generated dating profiles influence romantic attraction. An experimental study found that people were less willing to date someone they believed had used AI than someone who received similar help from a friend, suggesting AI-assisted dating may undermine authenticity and reduce social appeal despite its growing popularity.

This research examines why AI adoption often fails in medium-sized businesses despite significant investment. By interviewing employees and leaders, it develops a practical framework that treats AI as a new colleague requiring onboarding, trust, and cultural integration. The findings will help organisations implement AI more successfully and improve long-term adoption.

This research investigates how reliance on AI systems affects human cognition and reasoning. Using concepts from cognitive offloading, the study compares AI-assisted and independent problem solving, measuring verification behavior, reasoning depth, and decision confidence. The work explores whether increasingly capable AI tools may unintentionally reduce critical thinking and human expertise.

This research redefines digital health literacy for an AI-driven world, emphasizing the alignment between users and technology. Using a Delphi method, it identifies three core components—knowledge, skills, and context. The resulting framework guides the design of digital health tools that better support behavior change by adapting to users’ real-world needs.

This talk explores emotional resistance to AI through a personal storytelling project. It argues that AI adoption is an adaptive challenge tied to identity, not just technology. Using Robert Kegan’s framework, it demonstrates how testing limiting beliefs can reduce resistance, emphasizing that successful AI integration depends on addressing human concerns about autonomy, competence, and connection.