This thesis examines who turns to AI for mental health support, rather than whether AI can be a therapist. Drawing on TherapyGPT forum analysis and ongoing experiments, the research identifies fear of judgment, trust in AI and past therapist failures as possible drivers of AI therapy use.

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.

Generative AI chatbots are predictive systems that generate human-like responses without true understanding. Using large datasets, they model word relationships similarly to weather forecasting. While effective, they can produce convincing inaccuracies, or “hallucinations.” This research emphasizes interpreting AI realistically—as probabilistic tools with limitations—rather than attributing human cognition to them.

This research exposes a hidden privacy risk in online voting and video conferencing: eye movements captured by standard webcams can reveal user choices. Using AI models, voting decisions were inferred with over 95% accuracy, highlighting that digital security must address behavioral signals—not just encryption.

This research explores next-generation digital twins—virtual representations of real-world systems that support decision-making through simulation and AI. By combining decentralization, privacy-preserving architectures, explainable AI, and scenario analysis, the work aims to help individuals and organizations evaluate alternative futures, make informed decisions, and build more transparent intelligent systems.