This paper examines the integration of retrieval-augmented generation (RAG) systems within academic library environments, focusing on their potential to transform traditional search and retrieval mechanisms. RAG combines the natural language understanding capabilities of large language models with structured retrieval from verified knowledge bases, offering a novel approach to academic information discovery. The study analyzes the technical requirements for implementing RAG in library systems, including embedding pipelines, vector databases, and middleware architecture for integration with existing library infrastructure. We explore how RAG systems can enhance search precision through semantic indexing, real-time query processing, and contextual understanding while maintaining compliance with data privacy and copyright regulations. The research highlights RAG’s ability to improve user experience through personalized research assistance, conversational interfaces, and multimodal content integration. Critical considerations including ethical implications, copyright compliance, and system transparency are addressed. Our findings indicate that while RAG presents significant opportunities for advancing academic library services, successful implementation requires careful attention to technical architecture, data protection, and user trust. The study concludes that RAG integration holds promise for revolutionizing academic library services while emphasizing the need for continued research in areas of scalability, ethical compliance, and cost-effective implementation.

Generative AI is rapidly reshaping higher education, yet its influence on student learning in engineering remains insufficiently understood. In courses that require conceptual understanding, design reasoning, and problem-solving, AI tools may support learning by providing explanations, solution pathways, and feedback. At the same time, they raise questions about trust, verification, and the quality of student thinking. This study investigates student perceptions of generative AI in engineering education, focusing on learning, trust, and information-seeking. This work extends a prior conceptual framework grounded in Ellis’s theory of information-seeking. The study applies a six-stage subset of Ellis’s model—starting, chaining, browsing, differentiating, monitoring, and verifying—to examine how students perceived generative AI during an engineering learning activity. It also examines themes related to perceived usefulness, learning support, trust, judgment, and AI use. Within this sample, respondents generally reported perceiving AI as a support tool for conceptual understanding, efficiency, and checking their reasoning. Among respondents who reported at least some AI use, higher agreement was observed for items concerning ongoing task support, perceived deeper learning, and the application of their own judgment, whereas lower agreement was observed for using AI to understand the problem requirements initially or to check and validate technical work.

Artificial Intelligence (AI) is increasingly integrated into healthcare to enhance decision-making; improve patient outcomes; and optimize healthcare operations. However, the adoption of AI in this sector presents challenges, particularly regarding ethical considerations, data privacy, and bias. The purpose of this study is to explore the types of AI bias reported in healthcare literature from 2018 to 2024 and to highlight the implications for AI implementation in medical practice. the paper discusses Data sciences strategies to mitigate these biases, highlighting the critical role for responsible and ethical AI development. It roles in role in mitigating and enhancing the fairness in ML and LLM. It then delves into the process of fine-tuning, outlining the steps taken to adapt the model to specific datasets and tasks. This study is a systematic review of papers published between 2018 and 2024. The literature was analyzed to identify various types of AI bias; including algorithmic bias; data bias; and ethical-human-related bias. Relevant articles were selected based on their contribution to understanding AI bias in healthcare and its effects on healthcare delivery. The analysis identified several types of AI bias prevalent in healthcare. Algorithmic bias; data bias; and ethical-human-related bias were the primary categories. These biases can distort the decision-making process; impact patient outcomes; and challenge the fundamental principles of medical ethics. The review also found that human and data biases are significant factors contributing to AI bias in healthcare systems. The study highlights the need of data sciences for addressing AI-related biases in healthcare to ensure the successful and ethical implementation of AI technologies. Special attention is given to avoiding overfitting and optimizing hyperparameters during the fine-tuning process. Future research should focus on developing strategies to mitigate these biases; ensuring that AI systems are fair; reliable; and trustworthy. Identifying and addressing these biases is essential for the ethical integration of AI in healthcare and for protecting the integrity of the medical profession.