The implementation of AI driven tools in academic literature searches represents a significant advancement, yet it also prompts profound questions about the impact on traditional search practices and information quality. AI transforms academic searches by incorporating natural language processing, contextual recommendations, and algorithmic result selection. This shift enhances user experience by moving from static, keyword-based searches to dynamic, AI driven exploration, thereby lowering barriers associated with input methods and literacy skills. However, it also necessitates critical considerations regarding algorithmic transparency, bias, and the quality of information. This presentation highlights that adopting AI tools represents not merely a technological upgrade, but a fundamental transformation. Rather than providing definitive answers, this session invites attendees on a reflective journey to explore these significant issues, fostering a deeper understanding of the complexities and potential developments in AI integration within academic research.