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 critiques AI-based classroom monitoring, arguing that while algorithms can measure behavior, they cannot interpret meaning. It proposes the “Augustinian limit,” where AI supports logistics but human judgment guides interpretation. The framework protects authentic learning moments, emphasizing that true education relies on human insight, not just data-driven evaluation.

AI can answer religious questions, but it often blends traditions and provides incomplete answers. While specialized models exist, general models like ChatGPT can perform better due to broader training data. The key insight is that theology remains a human, dialogical process—AI should assist, not replace, human judgment and interpretation.