AI Tool Adoption in Research Teams
A survey studying how research teams evaluate, adopt, and integrate AI tools for data collection, analysis, and reporting. This instrument measures current tool usage, evaluation criteria, adoption barriers, training experiences, data quality perceptions, and team collaboration patterns.
Sample questions
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Are you currently a member of a team that conducts research activities (e.g., data collection, analysis, or reporting) as part of your work?
Which of the following best describes your team's primary research focus?
When evaluating an AI tool for research use, please rank the following criteria from most important (1) to least important (7).
To what extent have each of the following been barriers to AI tool adoption in your research team? 1. Budget or cost constraints 2. Lack of technical skills on the team 3. Concerns about data privacy or security 4. Skepticism about AI output quality 5. Resistance to change from team members 6. Lack of organizational support or policy 7. Difficulty integrating with existing workflows 8. Uncertainty about ethical implications
Has your team received any formal training on using AI tools for research?
Compared to traditional (non-AI) research methods, how would you rate the quality of outputs produced by AI-powered research tools?
How does your team typically make decisions about which AI tools to adopt?
Overall, how satisfied is your team with the AI tools currently used in your research workflow?
Which of the following best describes your role within your research team?
Thank you for completing this survey! Your responses are valuable and will help us understand how research teams are navigating the adoption of AI tools. Your responses have been recorded and will remain confidential. If you have any questions about this study, please contact the research team at the email provided in your invitation.
Which of the following tools does your research team currently use on a regular basis? (Select all that apply)
How important is each of the following when your team evaluates AI tools for research? 1. Accuracy and reliability of outputs 2. Ease of use and learning curve 3. Data security and privacy compliance 4. Integration with existing tools 5. Cost and licensing 6. Transparency of AI methods 7. Vendor support and documentation
You mentioned some barriers to AI adoption. Can you describe the most significant challenge your team has faced when trying to adopt or consider AI tools for research?
How would you rate the quality of AI-related training your team has received?
How confident are you in the accuracy of data collected or analyzed using AI tools?
How frequently does your team share knowledge or best practices about AI tools with each other?
How likely is your team to expand its use of AI tools for research in the next 12 months?
How many people are on your research team?
Has your team used any AI-powered tools for research purposes in the past 12 months?
Which of the following training formats would be most useful for your team to learn AI research tools? (Select up to 3)
To what extent do you agree or disagree with the following statements about AI tools and research quality? 1. AI tools help reduce human error in data analysis 2. AI tools can introduce new types of bias into research 3. I trust AI-generated outputs enough to include them in final reports without extensive manual review 4. AI tools make it easier to replicate research processes 5. The lack of transparency in how AI tools work concerns me
To what extent has the introduction of AI tools changed how your team collaborates on research projects?
Based on your responses throughout this survey, please share any additional thoughts or feelings about how AI tools are shaping research in your team or field.
Which sector does your organization primarily operate in?
Which AI-powered tools has your team used for research? (Select all that apply)
How many years of experience do you have in research?
Which stage best describes your team's current level of AI tool adoption for research?
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