These resources provide questions you and students can use to evaluate and choose AI tools.
- AI Tools for Research, from Temple University
- This library guide offers a structured framework for assessing AI tools used in academic research. It emphasizes ethical considerations, transparency, and academic integrity, providing guiding questions around accuracy, bias, usability, and cost. Geared toward students and researchers, the site stands out for its critical, values-driven approach rather than tech specs or rankings. Ideal for classroom discussions about responsible AI use.
- Critically Evaluating AI Tools, from University of Toronto Libraries
- This guide encourages a reflective, question-based approach to evaluating image-generating AI tools. It highlights issues like bias, ownership, environmental impact, and creative authorship. Unlike more tool-focused comparisons, it promotes critical literacy over technical reviews. Best suited for students in the humanities and arts exploring the broader implications of generative AI.
- Evaluating AI: ROBOT, from School of the Art Institute of Chicago
- Developed by librarians at SAIC, the ROBOT framework (Reach, Ownership, Bias, Openness, and Time) offers a mnemonic-driven method to evaluate AI tools, especially in art and design contexts. It foregrounds creative agency, intellectual property, and the ethics of tool design. This guide is uniquely tailored to artists and designers seeking to assess AI through a critical and disciplinary lens.
- Evaluating AI Tools, from Purdue University
- This guide presents a practical, criteria-based approach to evaluating AI tools for educational use. It focuses on categories such as functionality, accessibility, data privacy, and licensing. The tone is pragmatic and institution-facing, making it especially useful for faculty and administrators selecting tools for classroom or campus-wide adoption.
- Generative AI: Supporting AI Literacy, Research, and Publishing, from William & Mary Libraries
- This guide outlines key evaluation factors—accuracy, bias, transparency, usability, and cost—alongside examples of how generative AI tools are used in academic settings. It balances practical advice with ethical reflection, making it accessible to both students and instructors. Its strength lies in bridging technical evaluation with pedagogical relevance.
- LibGuides: Artificial Intelligence (AI): Evaluating AI Tools and Output, from the University of Texas Libraries
- This guide offers a short list of questions for critically assessing generative AI tools, focusing on transparency, data sources, ownership, privacy, bias, and potential risks.
- Using AI Tools in Your Research, from Northwestern University Libraries
- Rather than assessing tools themselves, this guide focuses on evaluating the outputs of AI tools—text, images, and other media. It offers detailed criteria like credibility, originality, bias, and accuracy. This product-centered approach makes it uniquely useful for students and instructors analyzing AI-generated work in academic contexts.
Last Updated on July 28, 2025