NVivo is approved for academic use. 

It can be installed on managed devices via Software Centre or Self-Service. Students access it through IT Requisitions.

What is NVivo?

NVivo is a qualitative data analysis software that helps faculty and students organize, code, and interpret unstructured data such as interviews, surveys, and open-ended responses. The latest version, NVivo 15, includes an AI Assistant that can summarize documents and suggest codes, supporting learners as they develop analysis skills.

For more information on what GenAI is and how it can be used to support teaching and learning, see the GenAI overview.

What is the purpose of NVivo

NVivo is designed to support qualitative and mixed-methods research by making it easier to manage large volumes of data and identify themes, relationships, and insights. In teaching and learning contexts, it provides a structured environment for introducing students to qualitative methodologies and guiding them through coding and analysis practices.

Not what you’re looking for?

Why use NVivo

  • Helping students practice authentic research methods with real data.
  • Allowing collaborative coding and analysis in coursework or research teams.
  • Providing visualizations (e.g., charts, word clouds, cluster maps) to support interpretation and presentation.
  • Using the AI Assistant to save time on routine tasks so learners can focus on critical thinking and deeper analysis.
  • Protecting user data, as NVivo’s agreement with OpenAI ensures data is not used to train AI models, is deleted after processing, and remains the intellectual property of the user.

Learning Technology Ecosystem (LTE) Principles

NVivo most directly address these LTE goals:

Designed for reflection and growth - Learning is refined and extended through prompted and supported opportunities to focus on understanding and next steps.

Best Practices

DO

DON'T

 Include a statement in your syllabus explaining when and how NVivo will be used.

 Don’t assume all students have equal access to a device that can run NVivo; check technical requirements and provide alternatives if needed.

 Use small, low-stakes exercises (e.g., coding a short transcript) to build student familiarity before moving to larger projects.

 Don’t rely on the AI Assistant as a substitute for student learning; ensure students still practice manual coding and interpretation.

 Encourage collaborative coding and discussion to help students compare interpretations and strengthen methodological transparency.

 Don’t allow students to accept AI-generated codes or summaries without critically reviewing and refining them.

 Scaffold assignments by guiding students through stages of qualitative analysis (e.g., data organization, coding, theme development, visualization).

 Don’t require use of the AI Assistant in contexts where data sensitivity or privacy regulations prohibit it.

 Highlight NVivo’s AI Assistant as a support tool for summarization and code suggestions, while reinforcing the need for critical review of AI outputs.

 Remind students about ethical data practices, including privacy, consent, and secure storage when importing and analyzing real-world data.

 

 Provide guidance on exporting outputs (e.g., code reports, visualizations) so students can include evidence of their analysis in written assignments.

 

 Ask students to document when and how they used the AI Assistant in their analysis, and to cite or acknowledge AI contributions where appropriate.

 

Support for NVivo

Technical Support

(Why isn't this working?)

 

Vendor Supported

 

Training Support

 (How do I learn to use this tool?)

USask IT supported

Vendor Supported 

Teaching Support

 (How do I teach with this tool?)

Gwenna Moss Centre for Teaching and Learning Supported

  • Contact GMCTL to discuss how to use NVivo in your teaching gmctl@usask.ca

Tool Evaluation

Technologies are evaluated based on their alignment with the Learning Technologies Ecosystem Principles.  Click to see explanations of each principle and a justification of the rating. You can also view a complete blank rubric to see more details or read about the assessment process.

Learning must be found easily at any time, and all learners and teachers have equitable access, regardless of culture, language, ability etc. 
More Information

Rating: 

  • Accessibility standards:
    • NVivo does not fully meet WCAG accessibility standards, and some features (e.g., screen reader support) are limited.

  • Cost of use for USask students:
    • Students can access NVivo through the institutional license.

  • Platform/device:
    • NVivo is available for both Windows and Mac computers, but there is no mobile version and feature sets differ by platform.

  • Offline Access:
    • NVivo can be used offline once installed, though features like Collaboration Cloud and the AI Assistant require an internet connection.

Learning is a process of meaning-making, constructed through learning with others, and as a part of an intentional, deliberate system within a course and across experiences.
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Rating:

  • Collaboration:
    • NVivo allows group work through the Collaboration Cloud, but this requires additional licensing and reliable internet, which may limit ease of use.

  • Sharing:
    • Students can export reports, codebooks, and visualizations to share their work, but NVivo does not provide built-in social or peer-to-peer sharing features.

Learning is refined and extended through prompted and supported opportunities to focus on understanding and next steps.
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Rating:

  • Reflection and revision:
    • NVivo strongly supports iterative learning, as students can code, re-code, annotate, and revisit their analysis over time, encouraging deeper reflection and refinement of their interpretations.

Learning is most effective when systems are designed to help learners find, create, and/or repurpose significant content for the value of themselves and others.
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Rating:

  • Creating:
    • NVivo enables students to create coding structures, memos, and visualizations, but these outputs are focused on analysis rather than open-ended creative production.

  • File format:
    • NVivo supports importing many file types (text, PDFs, surveys, audio, video, images) and allows exporting of reports and visuals, but some outputs are locked into proprietary formats that limit reuse.

Learners create and control spaces for learning, understanding and retaining ownership, and purposefully choosing how and when they share.
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Rating:

  • Archiving, saving, and exporting data:
    • NVivo projects are saved locally and can be exported in various formats, though some features (e.g., visualizations) may be limited to proprietary file types.

  • Data privacy and ownership:
    • Lumivero’s (Nvivo's parent company) agreement with OpenAI ensures data is not used to train AI models, is deleted after processing, and all inputs/outputs remain the user’s intellectual property.

  • Sign Up/Sign In:
    • NVivo requires users to sign in with institutional or personal Lumivero accounts, which can create an extra step for students but supports license management.

  • Customization:
    • Students can create and customize their own coding frameworks, memos, and visualizations, giving them control over how they structure their analysis.

Learners need to work in a system that is fluid and requires a minimum number of steps in systems that are intuitive and integrated.
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Rating:

  • Interface:
    • NVivo’s interface is feature-rich but complex, with a steep learning curve that can make it difficult for new users.

  • Additional Downloads:
    • NVivo must be installed on a computer, and add-ons like NVivo Transcription or Collaboration Cloud require separate setup.

  • Functionality:
    • Once mastered, NVivo is powerful for organizing and analyzing qualitative data, but the time and training required can limit its efficiency in teaching contexts.

Learners exist in accessible networks, and connect to the experiences, concepts, people, and ideas that they need.
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Rating:

  • Scale:
    • NVivo works well for individual or small group projects, but it is not designed to support large-scale course-wide collaboration or sharing.

  • Flexibility of Media:
    • NVivo can import multiple media formats (text, PDFs, audio, video, images), but analysis is primarily optimized for text, and working with other media is less flexible and often cumbersome.

  • Engagement:
    • NVivo supports authentic research tasks, but it does little to foster student engagement or interaction on its own; meaningful engagement depends heavily on assignment design and instructor facilitation.

Learning and feedback are iterative, and assessment comes from multiple sources, including self, peers, teachers, and outside experts.
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Rating:

  • Feedback:
    • NVivo does not provide built-in mechanisms for formative or summative feedback; instructors must create separate processes to give students feedback on their analysis work.

  • Engagement:
    • NVivo can deepen engagement with qualitative data and research methods, but it does not directly support assessment activities such as grading or peer review.