> For the complete documentation index, see [llms.txt](https://ai-for-researchers.setu.ie/llms.txt). Markdown versions of documentation pages are available by appending `.md` to page URLs; this page is available as [Markdown](https://ai-for-researchers.setu.ie/guidance-for-supervisors/case-studies.md). # Case Studies These case studies were developed by the Working Group on AI in Research in collaboration with members of *AI Connect*, SETU’s Community of Practice for researchers. If you are interested in submitting examples of issues or situations you have encountered as a researcher or as a supervisor, please submit to the Working Group at and we will consider adding to this list of case studies. ### Case Study 1: External GenAI Transcription Tools *Scenario: A postgraduate research student wants to use an external GenAI tool (e.g., Otter.ai, Whisper) to transcribe focus group interviews containing personal, but not sensitive data. The GenAI tool is not university approved.* #### Ethical Checklist • Prefer approved tools; Use MS Teams transcription which keeps data inside the university; secure environment • If using external tools: Check provider’s ownership, location, and data storage. Prioritise EU-based tools so that the tools are GDPR compliant • Confirm data privacy terms; Check whether the tool uses uploaded content for AI training • Ensure encryption; Audio files should be encrypted during upload and stored securely • Update consent form: Include tool name, provider, data type, storage location, and GDPR compliance measures • Conduct risk assessment; Submit completed assessment with updated consent forms to Research Ethics Committee ### Case Study 2: GenAI for Literature Reviews *Scenario: A PhD student is considering using GenAI research assistant tools (e.g., Elicit, Consensus, SciSpace, Research Rabbit) to assist with their literature review. They ask for advice on appropriateness and ethical concerns.* #### Ethical Checklist • Understand database limits: most tools rely on open-access sources and don’t access paywalled literature • VERIFY all outputs; Summaries may omit critical information (e.g. the tools may not identify certain graphs/figures in the document), include hallucinations, or contain fabricated references • ALWAYS read full original paper; never rely solely on AI summaries • Develop literature review skills first before using AI tools for this task; Consider preparing initial literature review without AI to develop search, synthesis and critical skills • Agree boundaries with supervisors; establish acceptable vs. unacceptable uses early on ### Case Study 3: Proofreading with GenAI *Scenario: A researcher working on a co-authored research article wants to use a GenAI tool to help with proofreading and language refinement before submission.* #### Ethical Checklist • Check the individual journal’s guidelines as publishers vary in what they allow • Seek co-author consent; all authors should agree to AI use • Verify changes carefully; AI can change meaning and introduce errors • Keep all draft versions; protects against false AI detection accusations • Acknowledge use for proofreading even if journal doesn’t require it ### Case Study 4: GenAI for Data Analysis/Coding *Scenario: A PhD student is working with a large dataset and wants to use a GenAI tool to clean data, run statistical analyses, or write code. The dataset contains confidential or potentially identifiable information.* #### Ethical Checklist • Data protection first; Do not upload identifiable or confidential data into external AI tools • Remove identifiers; Strip all personal identifiers and sensitive variables before use • Ensure reproducibility; Outputs must be independently verifiable without relying solely on AI • Avoid black box analysis; You must be able to explain the process • Validate results by checking against traditional statistical packages or peer review • Consider using synthetic data; Develop and test code on synthetic data before applying to real data *** --- # Agent Instructions This documentation is published with GitBook. GitBook is the documentation platform designed so that both humans and AI agents can read, navigate, and reason over technical content effectively. 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