Generative AI

This page provides a centralized, evolving collection of resources to support the effective and responsible use of Generative AI in research, scholarly communication, and grant development within the Dunlop School of Biological Sciences. Curated and maintained by the Research Administration and Development (RAD) Unit, these materials are designed to help faculty, postdoctoral scholars, graduate students, and other researchers understand emerging best practices, apply AI tools to research workflows, and stay informed as institutional and national guidance continues to develop. New content and future additions, such as a prompt library, will be added regularly.

If you have suggestions for additional resources or guidance that would be useful, please share your feedback so that we can continue to refine and expand this page.

A. Foundational AI Literacy & Training

• Anthropic – AI Fluency: Framework & Foundations: Self-paced modules explaining how modern LLMs work (training data, alignment, safety) with an emphasis on responsible, practical use—ideal primer before incorporating AI into lab or grant workflows.
• MIT Media Lab – Your Brain on ChatGPT: Research-based perspective on how LLMs shape reasoning and learning; useful for PIs and mentors setting expectations for trainees who use AI in reading, coding, or writing.

B. AI Use in Research, Grant Writing, & Scholarly Communication

• PLOS Computational Biology – Ten Simple Rules to Leverage LLMs for Getting Grants: Actionable rules for idea generation, structuring aims, and drafting language while preserving originality and research integrity—directly applicable to R01s, fellowships, and center proposals.
• Microsoft – Is Using AI the Same as Plagiarism?: Clarifies when AI-assisted writing risks plagiarism and how to cite/acknowledge AI use—helpful guidance for labs producing manuscripts and trainee statements.
• Nature – Article on AI & Science: High-level perspective on how AI systems are influencing discovery across the life sciences—from data analysis to hypothesis generation.
• IBM – Chain of Thoughts: Plain-language explainer on chain-of-thought reasoning and when to be cautious interpreting model-generated rationales in data analysis and review.
• Science Advances – Article: Peer-reviewed discussion of AI’s role in scientific practice, including opportunities and integrity risks—relevant for data-rich biological research.
• APA – Policy on Generative AI: Defines acceptable and prohibited uses of AI for writing, citation, and authorship—useful reference for behavioral, neuroscience, and psych-related bioscience projects.
• Journal of Biological Chemistry – AI Use Guidelines: JBC’s expectations for AI-assisted text and image handling, including transparency requirements—important for molecular/biochemistry manuscripts.
• ScienceDirect / Brain Research – Guide for Authors (AI Policy): Publisher guidance for AI use in manuscript preparation and figure workflows for neuroscience-adjacent submissions.
• Nature Scientific Reports – AI Use Policies: Clarifies what assistance is permitted (e.g., language polishing) and what must be disclosed when submitting to Scientific Reports.
• ACS – AI Policy for Authors: ACS’s rules for AI in writing and figure generation—relevant to structural biology, chemical biology, and biochemistry papers.

C. Research Integrity, Misconduct, and Detection

• NIH Notice NOT-OD-25-132: NIH guidance addressing responsible AI use in funded research, with implications for human/animal studies, data governance, and transparency.
• Wiley – AI-Powered Papermill Detection Service: Overview of Wiley’s AI-driven screening for papermill and fraudulent manuscripts—context for rising editorial scrutiny in biomedicine.
• NSF – Research Misconduct Policy Supplement (PAPPG): Defines fabrication, falsification, and plagiarism in the AI era; essential reading for NSF-funded biosciences projects.
• NSF – Artificial Intelligence Policy: Overview of NSF’s guidance on responsible use of AI in research, including expectations for transparency, integrity, and compliance across NSF‑funded projects.
• Nature – Article on AI-Generated Content Detection (2025): Commentary on evolving methods to detect AI-generated text/images in scientific communication—relevant for figure integrity and review processes.

D. AI-Enhanced Tools for Literature Search & Discovery

• arXiv – AI Research: Current AI-methods preprint (often computational/methodological) that can inform pipelines and analysis strategies in computational biology.
• Google Scholar Labs – Experimental Search Tools: Experimental ranking/summarization features that can speed up scoping reviews and related work sections.
• PubMed GPT (Community Tool): Natural-language interface over PubMed for rapid question-driven queries; verify results against PubMed.gov records.
• PubMed.ai: AI-augmented PubMed search with summarization, clustering, and relevance ranking for faster landscape scans.

E. UCI-Specific Guidance & Resources

• UCI Libraries – Research & AI Guide: Campus-curated guidance on responsible AI in research workflows, including examples and tool suggestions for UCI investigators.
• UCI Libraries – Generative AI Guide: Overview of available tools, usage norms, and citation guidance tailored for the UCI community.
• UCI Office of Research – AI in Research: Policy and compliance hub outlining expectations for AI use on sponsored projects at UCI.
• ZotGPT: UCI’s centralized portal for accessing campus-supported generative AI tools, help, and training opportunities.