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Question

Artificial Intelligence (AI) has reshaped information retrieval in libraries by enabling faster, more accurate, and context-aware access to resources. Traditional systems relied heavily on Boolean logic, keyword matching, and subject indexing, often demanding strong search skills. In contrast, AI-driven tools use Natural Language Processing (NLP), Machine Learning (ML), and semantic search to interpret user intent and retrieve results that match the user’s context. For instance, instead of listing thousands of items for a term like "digital preservation," AI systems can identify whether the user needs theoretical insights, technological solutions, or case studies and prioritise accordingly. Chatbots and Virtual Reference Services (VRS) represent another major advancement. Earlier reference services were limited by staff availability and slower response times. AI-enabled chatbots now provide 24/7 scalable support, assisting users with catalogue navigation, database searching, and citation queries. Enhanced VRS also include voice-enabled interaction, multilingual help, and links to institutional repositories and e-learning platforms, expanding access for remote and diverse users. However, AI adoption introduces challenges. Ethical concerns include data privacy, dependence on automated systems, and algorithmic bias, which may unintentionally suppress unconventional research areas if tools are trained on mainstream datasets. Additionally, chatbots, despite their efficiency, cannot replicate human empathy, judgement, or critical thinking, making a hybrid approach that blends AI with human expertise the most sustainable model. For LIS professionals, integrating AI demands new competencies. Librarians must develop skills in digital ethics, algorithmic transparency, and data analytics while ensuring that AI tools are critically assessed. Their role increasingly includes evaluating AI systems to ensure they align with educational goals, rather than purely commercial interests. Balancing human expertise with AI capabilities remains central to ensuring equitable and meaningful access.

According to the passage, Chatbots cannot replicate which essential human quality?

The correct answer is
Empathy and contextual judgement

Chatbot Limitations in Library Information Access

Artificial Intelligence (AI) tools, such as chatbots, enhance library services by offering 24/7 assistance and improving efficiency in tasks like catalogue navigation and database searching.

The passage explicitly states that while chatbots are efficient, they cannot replicate certain fundamental human qualities. These qualities include human empathy, judgement, and critical thinking.

Option 1, 'Empathy and contextual judgement', directly aligns with the human qualities the passage identifies as irreplaceable by chatbots. The passage highlights these as essential aspects that contribute to a more nuanced and complete user interaction.

Conversely, qualities like speed in answering, skill in citation formatting, and the ability to cross-search databases are precisely the types of tasks AI and chatbots are designed to perform efficiently. Therefore, these are not the limitations mentioned.

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Important Questions from Artificial Intelligence

  1. In the context of AI, NRF may consider a three-pronged approach. These are:
    A. Advancing International research efforts to address global challenges.
    B. Developing and deploying application.
    C. Efforts to address global challenges through research.
    D. Advancing core AI research.
    E. Enhancing the knowledge regarding AI, NRF
    Choose the correct answer from the options given below:
  2. Arrange the steps involved in the case-based reasoning
    A. System finds closest fit and retrieves solution
    B. System asks user additional questions to narrow search
    C. System modifies the solution to better fit the problem and got successful
    D. System searches data base for similar cases
    E. User describes the problem
    Choose the correct answer from the options given below :
  3. In designing an agent in AI, PEAS stands for :
  4. What is the primary function of supervised Machine Learning in Disaster Risk Management (DRM)?
  5. How do computational models contribute to disaster research?
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