Navigating the AI Landscape: How Libraries Can Adapt

Libraries and AI—Challenges and Responses

Introduction

• Host: Don from the Gigabit Libraries Network.
• Speakers:
  • Andrew Cox: Member of the AI Special Interest Group at IFLA; Information School in Sheffield.
  • Richard Whitt: President of GLIA Foundation.
• Series Context: Part of the "Libraries in Response" series on technology issues affecting libraries.

Context and Background

• Libraries are facing multiple crises: COVID-19, climate change, political unrest, and AI.
• AI is seen as both an opportunity and a challenge for libraries.
• The importance of libraries as trusted institutions in navigating technological changes.

Challenges of AI for Libraries

• Existential Concerns: AI's potential impact on humanity and societal structures.
• Trust Issues: Ensuring AI agents act in the best interest of users, avoiding "double agents."
• Digital Divide: AI might exacerbate inequalities between connected and unconnected communities.
• Regulatory Landscape:
  • Federal and state policies are being developed to address AI.
  • Challenges in effectively regulating complex AI technologies.

Role of Libraries in the Age of AI

• Leveraging the high trust in libraries to guide communities through AI challenges.
• Promoting AI literacy and responsible AI use among patrons.
• Developing AI capabilities, including data stewardship and ethical practices.
• Potential partnerships with technology companies for AI development.

Presentations

Richard Whitt

• Referenced Cerf's work on digital libraries and intelligent agents (knowbots).
• Discussed the rise of AI bots and personal digital assistants.
• Introduced the concept of "double agents" in AI that may not serve users' best interests.
• Highlighted potential roles for libraries:
  • Providing infrastructure and connectivity.
  • Serving as repositories of trustworthy digital knowledge.
  • Acting as fiduciaries with obligations to patrons.
  • Developing AI agents aligned with library values.
  • Educating patrons on AI and digital citizenship.

Andrew Cox

• Introduced the work of the IFLA AI Special Interest Group.
• Presented a strategic framework for libraries responding to AI challenges.
• Discussed the AI capability model:
  • Material Resources: Data and infrastructure needs.
  • Human Resources: Technical and business skills required.
  • Intangible Resources: Leadership, coordination, and adaptability.
• Suggested key actions for libraries:
  • Implement responsible and explainable AI solutions.
  • Enhance data stewardship and management skills.
  • Promote AI literacy and critical understanding among patrons.
• Addressed challenges like resource limitations and the need for collaboration and vision.

Discussion and Audience Participation

• Practical Steps for Libraries:
  • Start small with AI projects relevant to existing services.
  • Define a clear vision for AI integration.
  • Collaborate with other libraries and institutions.
• Partnerships with Tech Companies:
  • Potential benefits and risks of collaborating with technology firms.
  • Need for libraries to advocate for ethical AI practices.
• Comments from Participants:
  • Diane: Shared a tool developed by her library using AI to assist patrons; emphasized the importance of prompt engineering.
  • Stephen Abram: Highlighted the need for collaborative efforts, use cases, and establishing guardrails for AI implementation.
  • Fiona: Mentioned Toronto Public Library's leadership in using AI.

Conclusion

• Recognized that AI presents both significant challenges and opportunities for libraries.
• Emphasized the unique position of libraries to leverage trust and promote ethical AI use.
• Committed to ongoing discussions and exploring AI's impact on libraries in future sessions.
• Encouraged proactive engagement with AI, focusing on community needs and responsible practices.

Note: This outline summarizes a presentation on how libraries can respond to the challenges and opportunities presented by AI, featuring insights from industry experts and audience participation.

Data Science 101: Understanding Statistical Concepts and Analysis

From Couch to Jupyter: A Beginner's Guide to Data Science Tools and Concepts

Introduction

• Host: Manogna, Senior Data Scientist at Slalom.
• Presenter: Kiko K., Analytic Scientist at FICO on the Scores Predictive Analytics team.
• Background:
  • Graduated from UC Berkeley in 2019 with a degree in Applied Mathematics and Data Science.
  • Led teams integrating data science into non-traditional curricula.
  • Passionate about data science's power and community.

Workshop Overview

• Title: "From Couch to Jupyter—A Beginner's Guide to Data Science Tools and Concepts"
• Objective: Provide foundational knowledge and tools for beginners in data science.
• Structure:
  • Introduction to Jupyter Notebook.
  • Basics of Python programming.
  • Understanding data structures and statistical concepts.
  • Interactive code demonstrations.
• Resources:
  • GitHub repository with tutorial notebooks and datasets.
  • Anaconda installation guide for environment setup.

Key Topics Covered

• Using Jupyter Notebook
  • Understanding markdown and code cells.
  • Running cells and writing code.
• Python Basics
  • Data types: integers, floats, strings, booleans.
  • Variables and functions.
  • Arithmetic operations and function calls.
• Data Structures
  • Arrays with NumPy.
  • Pandas Series and DataFrames.
  • Indexing and slicing data.
• Data Manipulation and Analysis
  • Importing libraries and reading data files.
  • Handling missing data (NaN values).
  • Filtering and selecting data.
  • Basic statistical calculations: mean, median, standard deviation.
• Practical Demonstrations
  • Working with a stroke prediction dataset from Kaggle.
  • Visualizing data distributions.
  • Imputing missing values.

Additional Resources

• Anaconda Installation Guide: For setting up the Python environment.
• Tutorial Notebooks: Covering various topics in more depth.
• External Links: Videos and other learning materials for further study.

Conclusion

• Q&A Session: Addressed audience questions on topics like:
  • Differences between Jupyter Notebook and JupyterLab.
  • Handling missing data and NaN values.
  • Differences between arrays and series.
  • Recommendations for beginners starting with data sets.
• Final Remarks:
  • Encouraged attendees to explore provided resources.
  • Emphasized continuous learning in data science.
  • Thanked the audience for participation.

Note: The workshop aims to make data science accessible to beginners by providing hands-on experience with tools like Jupyter Notebook and Python, using practical examples and interactive code demonstrations.

Transforming Tutorials: The Impact of AI in University Education

Integrating ChatGPT into Tutorial Sessions to Enhance Critical Thinking in University Students

Introduction

• Presenters:
  • Sandra Morales: Digital Education Advisor at the Center for Teaching and Learning, Oxford University.
  • Co-Presenter: A colleague also working at Oxford University.
• Session Overview:
  • Context of tutorials at Oxford University.
  • Experience using AI in psychology tutorials.
  • Recommendations for integrating AI.
  • Time for questions if available.

Context of Tutorials at Oxford University

• Tutorial Structure:
  • Small group teaching sessions with one tutor and 1-3 students.
  • Tutors encourage analytical and critical thinking to deepen subject knowledge.
  • Different types of tutorial sessions based on student needs:
    • Feedback sessions.
    • Problem-solving activities.
    • Questioning techniques.
    • Collaborative discussions.
    • Content knowledge exploration.
• Organizational Diversity:
  • Tutorials are organized independently by different programs and divisions.
  • Tutors tailor sessions according to their students' specific needs.

Authority and Knowledge in AI

• Key Discussion Points:
  • Questioned who holds authority and expertise in the rapidly evolving field of AI.
  • Considered the challenges of making recommendations in a new and developing area.
  • Noted that AI's disruptive impact is comparable to significant events like Brexit and COVID-19.
  • Highlighted the difficulty in identifying reliable authorities on AI.

Experience Using AI in Tutorials

• Learning Pathways Development:
  • Developed during the pandemic to integrate AI tools into teaching.
  • Utilized platforms like Canvas and Microsoft Teams.
  • Integrated ChatGPT at different stages:
    • Knowledge application.
    • Online and in-class collaboration.
    • Personalized learning experiences.
• Example from Language Center Tutor:
  • Applied the learning pathway structure in tutorials.
  • Included ChatGPT in various learning stages for enhanced interaction.
  • Both tutor and student engaged with ChatGPT during sessions.
• Student Feedback:
  • Students appreciated tutor support while working with ChatGPT.
  • Valued the collaborative process involving AI tools.

Enhancing Critical Thinking with AI

• Central Question: Is critical thinking the answer to effectively utilizing generative AI?
• Approach:
  • Aimed to use AI tools to support analysis, evaluation, decision-making, and reflection.
  • Sought to familiarize students with AI to enhance critical engagement.

Implementing ChatGPT in Psychology Tutorials

• Methods:
  • Introduced ChatGPT to students unfamiliar with the tool.
  • Used ChatGPT during one-on-one tutorial sessions.
  • Observed students' interactions, focusing on prompt engineering.
  • Assigned tasks such as designing a curriculum or preparing a lecture.
• Observations:
  • Students' prompting styles varied based on personality.
  • Language used in prompts included:
    • Imperative commands (e.g., "Write me a university-level...").
    • Polite requests (e.g., "Hello, can you please...").
    • Directives specifying roles (e.g., "I want you to be an expert...").
  • Noted that prompting language mirrored students' personalities.

Developing an AI Competency Framework

• Inspiration: Based on the Common European Framework of Reference for Languages.
• Competency Levels: Ranged from novice to expert users.
• Five Modes of Engagement:
  • Tool Selection: Choosing appropriate AI tools.
  • Prompting Techniques: Crafting effective prompts.
  • Interpreting Outcomes: Understanding AI-generated responses.
  • Integrating AI: Applying AI in professional practice.
  • Tool Development: Making decisions about AI tool development.
• Self-Evaluation Tool:
  • Created for students and staff to assess their AI proficiency.
  • Helps identify current competency level before engaging with AI tools.

Proposed Framework for Tutorials

• Integration of ChatGPT:
  • Recommended using ChatGPT as a companion in tutorial sessions.
  • Applicable across various session types (feedback, problem-solving, etc.).
• Implementation Process:
  • Self-Evaluation:
    • Students assess their initial proficiency with AI.
    • Facilitates personalized support from the tutor.
  • Prompting Practice:
    • Focus on developing effective communication with AI.
    • Emphasizes the importance of prompt language and structure.
  • Reflection and Awareness:
    • Encourage students to document their AI interaction process.
    • Discuss successes and areas for improvement.
  • Self-Monitoring:
    • Promote autonomy in controlling AI usage.
    • Foster critical thinking about AI's role in learning.
• Objective:
  • Enhance critical thinking skills.
  • Empower students to use AI tools effectively and responsibly.

Student Perspective

Quote: Emphasized taking control over AI tools rather than allowing AI to dictate the learning process.

Insight: Highlights the importance of maintaining critical oversight when using AI.

Ongoing Work

• Canvas Course Development:
  • Creating online resources for academics and students.
  • Aimed at educating users about AI integration in learning.
  • Courses are currently under development and not yet widely available.

Conclusion

• Acknowledgments:
  • Thanked the audience for their attention.
  • Noted that the proposed framework is a starting point for discussion.
• Future Considerations:
  • Recognized the need for ongoing dialogue about AI's role in education.
  • Invited feedback and collaboration to refine approaches.

Note: The presenters emphasized that the framework and recommendations are preliminary and subject to further refinement based on collective input and evolving understanding of AI in educational contexts.