How can the reasoning ability and depth of a thesis be enhanced through AI?

AI tools can augment thesis reasoning by providing structured analytical frameworks and identifying logical gaps. Their use is feasible when implemented as complementary scaffolds rather than replacements for human critical analysis. Effective enhancement relies on specific strategies: deliberate prompt engineering to probe argument depth (e.g., requesting counterarguments); iterative refinement of premises using AI-generated critiques; systematic mapping of argumentative flows with chain-of-thought prompting; and verification of evidence coherence. Crucially, outputs require meticulous scholarly review to detect biases or oversimplifications inherent in AI models. These approaches presuppose user expertise in discerning valid insights. Application occurs primarily in drafting and revision phases. Researchers deploy AI to challenge initial assumptions, ensuring claims withstand scrutiny, and to uncover overlooked connections between concepts or data. This process strengthens internal validity, reduces logical fallacies, and enriches theoretical discussions, ultimately yielding more rigorous and defensible scholarly conclusions. The key value lies in systematic intellectual provocation beyond unaided reflection.