What happens when #ai agents become the primary way #researchers interact with scholarly content?

Lisa Janicke Hinchliffe (‪@Illinois1867‬ and Apoorva Shah (VP Product Management, ‪@johnwileysons‬ explore how #ai is transforming #libraries publishing, and #research discovery.

From debating whether AI handles #peerreview better than #humans to how agents are replacing #web browsers for research, this episode tackles the fundamental questions facing academia.

What's the role of a #librarian when AI can instantly synthesize information? How do publishers enhance the AI-driven ecosystem?

👉 Learn more about ‪@johnwileysons‬ AI initiatives: https://www.wiley.com/about-us/ai-res...

Key topics:

*Understanding AI agents as research assistants
*The evolving role of libraries and librarians
*Why peer review isn't AI's biggest threat to publishing
*Digital rights and authentication for AI agents
*Building AI literacy programs
*Publisher-library collaboration in the AI era

Essential viewing for:

*Libraries navigating the AI transformation
*Publishers rethinking business models
*Researchers developing AI literacy
*Institutions making strategic AI investments

This conversation challenges assumptions while offering practical insights for anyone at the intersection of AI and scholarly communication.

How do you know if you're using #ai responsibly in your research? When should you disclose AI use and why is transparency around AI often taboo?

Join Sara Falcon, Director of UX Strategy, @johnwileysons and Avi Staiman, CEO, @Aclang Academic Language Experts, for an eye-opening conversation about the practical realities researchers face when integrating AI into their work.

Discover the 2025 ExplanAItions study: https://www.wiley.com/en-us/about-us/ai-resources/ai-study/

Learn about our partnership with Perplexity: https://www.wiley.com/en-us/solutions-partnerships/academic-institutions/instructors-administrators/perplexity/

See our AI guidelines for book authors: https://www.wiley.com/en-us/publish/book/resources/ai-guidelines/

In this episode, discover:
A practical risk-assessment framework for evaluating AI use in research
Why transparency about AI use remains taboo—and how to change that culture
The critical difference between LLMs and RAG tools (and when to use each)
How to write prompts that actually work—think "brilliant student who skipped K-12"
The hidden danger of relying on AI summaries for literature reviews
How early career researchers can develop AI literacy without losing core skills

Perfect for:
Researchers navigating AI adoption
Publishers developing AI guidelines
R&D teams evaluating AI tools
Academic institutions shaping AI policy
Anyone working at the intersection of AI and scientific integrity

Chats, a new series from ‪@johnwileysons‬ explores the practical challenges and opportunities facing academic publishing and research.

Episode One | The AI Choice: Why Waiting Isn't an Option

Choosing, Training and Using AI Models: Expert Interview with BMJ's CTO, Ian Mulvany and Ray Abruzzi (Senior Director of AI Product Management at Wiley). They explore the intersection of #ai development and academic publishing, offering insights for developers building research tools, publishers evaluating AI partnerships, researchers adopting new technologies, and institutions making strategic AI investments.

They discuss the importance of subject matter expert integration in AI development, fine-tuning strategies, prompt engineering, considerations for publisher-developer partnerships, model performance risk assessment, misconceptions around hallucinations and best practices, and more.

👉 Learn more about Wiley’s content licensing opportunities for AI developers: https://bit.ly/46k8HfW

Key Topics Covered:

• Fine-tuning AI models, prompt engineering, and deployment strategies
  
  
• Evaluating AI tools and building transparent publisher-developer partnerships
  
  
• Practical tips for researchers on choosing and using #aitools effectively
  
  
• Addressing misconceptions about #aihallucinations and model limitations
  
  

For AI developers:

• Statistical inference vs. retrieval: why LLMs aren't search engines
  
  
• Fine-tuning strategies, ensemble methods, and context window optimization
  
  
• Prompt engineering insights: why language mastery outperforms technical precision
  
  
• Production deployment lessons from high-stakes environments
  
  

For publishers & institutions:

• Considerations for AI tool evaluation and selection.
  
  
• Building partnerships with AI companies and requesting transparency
  
  
• The publisher's role in AI's "truth infrastructure" and moral obligations
  
  
• Investment priorities: where untapped AI potential lies
  
  

For researchers:

• Practical guidance on choosing reliable AI tools for academic work
  
  
• Understanding AI limitations: when to trust outputs and when to verify
  
  
• Prompt engineering techniques for better research outcomes
  
  
• Avoiding the "search engine misconception" that leads researchers astray
  
  
• Risk assessment for different use cases