Hudson Vitale "AI Essentials: From Tools to Strategies: A 2025 NISO Training Series, Session One - AI in Scholarly Communication"

This presentation was provided by Cynthia Hudson Vitale of Johns Hopkins University, for the initial session of the NISO training series "AI Essentials: From Tools to Strategies." Session One, "AI in Scholarly Communication," was held on October 9, 2025.

1. AI Essentials: From Tools to Strategy

2. About the Series Designed for those evaluating, guiding, or implementing AI use in scholarly communication. Focus on practical tools, policy and compliance pressures, and strategic planning. Each session blends grounded examples with forward-looking strategy. By the end, you’ll have a completed Institutional AI Readiness & Strategy Guide.

3. The 8-Week Journey Date Topic Focus Oct 9 AI in Scholarly Communication What’s here, what’s coming, what matters. Oct 16 Integrity in an AI Era Fraud, bias, transparency, and trust. Oct 23 AI Governance Copyright, licensing, and compliance. Oct 30 AI in Workflows & Infrastructure Embedding AI in publishing and operations. Nov 6 Supporting Researchers & Staff Policy, training, and consultation models. Nov 13 Planning for AI with Purpose Aligning AI efforts with organizational values. Nov 20 Assessing Organizational Readiness Gauging policy, data, and capacity. Dec 4 Building a Roadmap Turning insights into actionable plans.

4. Week 1: AI in Scholarly Communication: What’s Here, What’s Coming, What Matters Grounding in AI’s core capabilities and language. Examples of how AI is already impacting research and publishing. Early sense of what the evolving ecosystem looks like. Begin connecting global trends to local practice.

5. Quick Poll: How Are You Feeling About AI?

6. Where We’re Headed Today Welcome & Orientation AI & Me: Getting to Know One Another What Do We Mean by AI? Breakouts & Discussion

7. AI & Me 1. Name, role, and organization . 2. One AI tool or experiment you’ve tried (personal or professional). 3. If you were writing a headline about AI and your field today, what would it be?

8. What do we mean by AI? Artificial Intelligence (AI) refers to systems that can perform tasks requiring human-like intelligence, learning, reasoning, pattern recognition, language use, and problem solving.

9. AI in Scholarly Communication AI in this context = software and data services, not robots. Automates or enhances: Tagging and classification Recommendation and prediction Summarization and generation

10. The Layers of AI Technologies Machine Learning (ML) – systems that learn patterns from data and improve with experience. Natural Language Processing (NLP) – enables computers to understand and generate human language. Large Language Models (LLMs) – advanced NLP models trained on massive text corpora (e.g., ChatGPT, Claude, Gemini). Multimodal AI – integrates text, images, audio, and other data types into unified models.

11. Why It Matters for Scholarly Communication AI is influencing every stage of the research and publishing lifecycle: Authoring: writing aids, translation, citation support Peer Review: reviewer matching, bias detection Publishing: metadata generation, quality control Discovery: recommendation engines, semantic search Assessment: research analytics, impact forecasting

12. Scanning the Landscape Breakout Groups Whiteboard link: https://zoom.us/wb/doc/fkhp24ECQ_ieVNz9USQT-w Use the stickies in the whiteboard to discuss: Most interesting or surprising AI development you’ve seen outside your organization (publishing, research, higher ed). Which of these trends could have the biggest impact on how knowledge is created, reviewed, or shared? Where do you see hype overshadowing real value? Report Out Highlight one “signal of change” and one “tension or uncertainty.”

13. Where AI touches your work Open Worksheet 1, on your own, complete current touchpoints, reactions and reflections, and looking ahead sections

14. Discussion What surprised you about the bright spots? Where do you see alignment—or tension—between opportunity and risk? If we ran this same poll six months from now, what might look different?

15. Wrap Up Identified external AI trends shaping the research ecosystem. Mapped where AI is already impacting their own organizations. Built peer connections across the scholarly communication community. Begun drafting their Institutional AI Readiness & Strategy Guide. Week 2: Integrity in an AI Era: From Research to Algorithms This session takes a broad view of integrity, covering risks related to research fraud, ghostwriting, and paper mills, alongside issues of bias, transparency, and environmental cost in AI models themselves. We’ll also explore how organizations are responding with detection tools, community standards, and values-based decision-making.