AI and Information Literacy: AI@Libraries

EBSCO Responsible Research, Responsible AI

Read more - AI in Library Research Platforms - Findings from EBSCO's Recent Beta Release 

• Statista This link opens in a new window

  Statista provides data about finance, markets, politics, and society across over 170 industries and over 150 countries. Statista integrates data from many sources into one platform with ready-made data tables and visualizations. Sources of information include market research, trade publications, scientific journals, and government databases. Its Research AI tool references data curated by Statista and accesses GPT-4 Turbo for conversational question & answer, and search. Reach more about how Statista Research AI works.

• Coming soon: ProQuest AI Research Assistant

  ProQuest is currently beta testing, but has not yet rolled out, a new AI Research Assistant in ProQuest One databases,  Read more on their blog and in this FAQ.

• WorldCat.org

  WorldCat searches the combined collections of libraries around the globe. Create and sign in with an individual account to see AI-generated book recommendations. More details on WorldCat's blog.

Unlocking the Power of AI: Enhancing Research with Gale Resources

AI in research guidelines:

1. Ethical Considerations:

• Bias and Fairness:
  • Researchers should be aware of potential biases in AI algorithms and training data that could lead to unfair or discriminatory outcomes.
  • Guidelines should emphasize the importance of using diverse and representative datasets and employing bias detection and mitigation techniques.
  • Regularly evaluate AI models for fairness across different demographic groups.
• Transparency and Explainability:
  • Promote the use of AI models that are transparent and whose decision-making processes can be understood and interpreted (Explainable AI - XAI).
  • Document the AI methods and models used, including data sources, hyperparameters, and model performance metrics, to ensure traceability.
• Accountability and Responsibility:
  • Clearly define the roles and responsibilities of researchers when using AI tools.
  • Researchers are accountable for the outputs generated by AI and must verify the accuracy and reliability of the information.
  • Establish mechanisms for auditing AI systems and addressing unintended consequences.
• Privacy and Data Governance:
  • Adhere to data protection regulations and institutional policies regarding the collection, storage, and use of research data in AI applications.
  • Ensure that the privacy of research participants is maintained and that data is handled securely.
  • Understand the terms of service of AI tools, especially regarding data ownership and usage by third-party providers.
• Informed Consent:
  • Obtain informed consent from participants when AI systems are involved in data collection or interact with human subjects.
  • Clearly explain how AI will be used in the research and the potential risks and benefits.
• Intellectual Property and Authorship:
  • Address issues related to intellectual property rights when using AI tools for content generation or analysis.
  • Clearly define authorship responsibilities when AI contributes to research outputs, adhering to relevant publication ethics guidelines.

2. Best Practices for Using AI in Research:

• Complementary Tool: AI should be viewed as a complementary resource to enhance research, not a replacement for critical thinking and researcher expertise.
• Verification and Validation: Researchers must critically evaluate and verify the outputs generated by AI tools using reliable sources and their own expertise.
• Literature Review: Exercise caution when using AI for literature reviews; always verify the accuracy and relevance of cited sources.
• Data Quality: Use high-quality, well-documented data for training and analysis with AI models.
• Transparency in Publications: Disclose the use of AI tools in research publications, including the name and version of the tool and how it was used.
• Reproducibility: Ensure that AI-assisted research is reproducible by documenting the data, code, and AI model parameters used.
• Human Oversight: Maintain human oversight in critical decision-making processes where AI is involved.
• Training and Education: Provide researchers with adequate training and resources on the responsible and ethical use of AI tools.

AI summarizing and Q&A tools

Many of the AI scholarly literature search tools include features for summarizing the search results and uploading PDFs to be summarized. These tools do not search literature, but will summarize items like documents and videos:

• Adobe AI Assistant

  Adobe Acrobat Pro has a beta AI Assistant rolling out in a phased manner. Temple users have free access to Adobe Creative Cloud programs, see Temple ITS' site licensed software page for details.

• ChatPDF

  Allows you to “talk to” an individual PDF file or group of files. Free tier allows 2 PDF uploads per day.

• Explainpaper

  Free tier generates a one sentence PDF summaries and allows unlimited "highlight explanations" (rephrasing passages you highlight) and follow up questions.

• Glasp

  Summarizes YouTube videos using ChatGPT and Claude.

• Illuminate  

            It turns academic journal articles into podcasts.

• Quivr

  Tool for uploading files and data to a collection (a "brain") that then answers questions based on its LLM and documents in the collection. Quivr can be pulled from Github and set up to run completely locally on a computer with an OpenAI API account (paid, for computation used).

• Scholarcy

  Creates article summaries in the form of AI-generated flashcards with several sections. Free tier allows creating 3 flashcards per day, web browser extension, and exporting to Word.

• Sider

  AI assistant browser extension with daily credits for free accounts. Reads and summarizes webpages, and includes ChatPDF.

• Summarify

  Distill lengthy content into concise summaries, including text, PDF, URL, blog, Youtube, and audio

• Summarize.tech

  Generate an AI summary of any YouTube video.

• TLDR this

  Free tier can summarize, query, and extract information from PDFs and .doc files.

• Wordtune Read

  AI summarizer and notetaking tool oriented towards writing and content professionals. Free tier offers a limited number of summaries and writing outputs per day. Ask questions of your entire library, but not of individual documents. Includes writing templates for common outputs like social media posts, co-worker communications, and others. Has a browser extension for using on particular webpages.

1. Authority Is Constructed and Contextual

• College Research: Students often encounter sources with varying levels of authority and must learn to critically evaluate them based on context, expertise, and potential bias. This includes understanding peer review, identifying different types of publications (scholarly vs. popular), and recognizing the influence of social, cultural, and historical contexts on knowledge production.
• AI Integration:
  • AI-powered source evaluation tools: Could help students analyze the credibility of websites by examining domain registration, author reputation (based on citation analysis or expert databases), and potential biases flagged by natural language processing.
  • Contextual analysis of information: AI could help students understand how a particular piece of information fits within a broader scholarly conversation by identifying related articles, contrasting viewpoints, and tracing the evolution of ideas.
  • Identifying potential bias: AI tools could analyze text for loaded language, logical fallacies, or skewed representations, prompting students to consider the author's perspective and potential biases.

2. Information Creation as a Process

• College Research: Students need to understand that information doesn't just appear; it's the result of a process involving research, experimentation, writing, editing, and dissemination. Understanding this process helps them appreciate the effort involved in creating knowledge and the different formats information can take (e.g., journal articles, datasets, creative works).
• AI Integration:
  • Visualizing the research process: AI could generate visual representations of the research lifecycle for different disciplines, highlighting key stages and outputs.
  • Understanding data creation: For students working with data, AI could explain the data collection and analysis processes used in specific studies.
  • Exploring different publication models: AI could provide information and comparisons of various publishing models (e.g., open access, subscription-based) and their implications.

3. Information Has Value

• College Research: This frame emphasizes the multifaceted value of information, including its economic, social, political, and cultural significance. Students need to understand issues related to intellectual property, copyright, data privacy, and the ethical use of information.
• AI Integration:
  • AI-powered copyright checkers: Could help students understand the copyright status of different types of materials.
  • Analyzing the economic value of information: AI could provide insights into the market for scholarly publications or the economic impact of data breaches.
  • Exploring ethical considerations of AI: Discussions could focus on the ethical implications of AI in creating, disseminating, and using information, such as algorithmic bias or the spread of misinformation.

4. Research as Inquiry

• College Research: This frame highlights research as an iterative process of asking questions, exploring possibilities, gathering evidence, and refining understanding. It encourages students to be curious, persistent, and open to unexpected findings.
• AI Integration:
  • AI-powered research question generation: Could help students brainstorm and refine research questions based on existing literature.
  • Exploratory data analysis tools: AI can assist students in identifying patterns and trends in data, leading to new research questions.
  • Simulating research scenarios: AI could create simulations of research projects, allowing students to practice different approaches and see the potential outcomes.

5. Scholarship as Conversation

• College Research: Students need to see research as an ongoing dialogue among scholars, with new contributions building upon and challenging existing work. Understanding citation practices, literature reviews, and the role of scholarly communities is crucial.
• AI Integration:
  • AI-powered literature review tools: Can help students identify key articles, understand the relationships between them, and visualize the evolution of scholarly debates.
  • Identifying experts and research communities: AI could help students find leading scholars in their field and connect with relevant research communities or online forums.
  • Analyzing citation networks: AI can visualize citation patterns to show influential works and identify emerging areas of research.

6. Searching as Strategic Exploration

• College Research: Effective searching is a critical skill involving planning, using appropriate keywords and search strategies, evaluating search results, and adapting the search as needed.
• AI Integration:
  • AI-powered search assistants: Could help students refine their search queries, suggest relevant keywords, and understand the underlying logic of search algorithms.
  • Personalized search recommendations: AI could learn a student's research interests and suggest relevant resources or search strategies.
  • Visualizing search results: AI could present search results in more intuitive ways, helping students identify key themes and relationships between sources.