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AI Literacy and Responsible AI Use Survey (Post-Course)

Dear students,

This survey aims to understand your current habits, skill levels, and attitudes toward using generative AI tools (such as ChatGPT, GitHub Copilot, Claude, etc.) in learning, programming, and robotics projects.

This is the post-course version of the survey you completed at the beginning of the semester. All responses are anonymous and will be used only for teaching improvement.

Participant Information Sheet (PIS)

Dear Student,
We cordially invite you to participate in a research survey on "Student AI Literacy and Responsible AI Use." Before deciding whether to participate, please read the following information carefully.

1. What is the purpose of this study?
As Large Language Models (LLMs) become increasingly prevalent in software development, this project aims to understand how students utilize AI tools (e.g., for code generation, algorithm design) in complex robotics projects, such as the dual-mode chess robot development. We also seek to assess your awareness of AI ethics, academic integrity, and tool limitations. Your feedback will directly help us optimize the RBE409TC curriculum and provide valuable empirical data for the university's "Education + AI" strategic agenda.

2. Why have I been invited?
You have been invited because you are currently registered and enrolled in the module RBE409TC (Immersive Robotics Studio 2).

3. What will I need to do?
If you agree to participate, you will be asked to complete the identical online questionnaire twice: once before the course module begins (before Week 7) and once after the module concludes (after Week 14). Each survey will take approximately 5–10 minutes to complete.

4. Will my participation affect my grades?
Absolutely not. Your participation is entirely voluntary. Whether or not you choose to participate, and any answers you provide in the survey, will not affect your grades or assessments in RBE409TC or any other module in any way. The module leader will not have access to any raw data containing your identifiable information prior to the finalization of module grades.

5. How will my data privacy and confidentiality be protected?
The survey will collect your Student ID solely for the purpose of matching your "pre-course" and "post-course" survey data for comparative analysis. Once the data matching is complete, all personal identifiers (e.g., Student ID, Name) will be immediately anonymized (de-identified). The research data will be stored securely and encrypted. No information that could identify you personally will ever be included in the final evaluation reports or any published academic outcomes.

6. Can I withdraw from the study?
Yes, absolutely. Your participation is voluntary. You have the right to decline to answer specific questions or to withdraw from the survey at any time before submitting your responses, without providing a reason and without any negative consequences.

7. Contact Information
If you have any questions about this research or the questionnaire, please feel free to contact the Principal Investigator via email: zhouyan.qiu@xjtlu.edu.cn. This study has been reviewed and approved by the relevant XJTLU Ethics Committee.

1. Informed Consent Form (ICF)
Please read the following statements carefully. If you agree to participate in this study, please check the corresponding boxes (or click to confirm online) and provide your details.

I confirm that I have read and understood the Participant Information Sheet (PIS) and have had the opportunity to ask questions.I understand that my participation is completely voluntary. I have the right to withdraw from this study at any time without providing a reason. I understand that my participation or decision to withdraw will in no way affect my module grades or my standing with the university.I understand that my personal data will be used solely for matching pre-course and post-course survey data, and the research team commits to strictly anonymizing (de-identifying) my data immediately after matching.I agree that the research team may analyze the anonymized data and use it for teaching evaluation reports, academic publications, and conference presentations.Based on the terms above, I voluntarily agree to participate in this survey.

2. Your unique identifier (for pre/post matching): Last 4 digits of your mobile phone number

3. How frequently do you currently use generative AI tools in your studies or programming projects?

NeverOccasionally (a few times per month)Regularly (a few times per week)Frequently (almost daily)

4. In your past projects, what have you mainly used AI tools for?

Basic code generation (from 0 to 1)Code debugging and error troubleshootingAlgorithm design and logic planningWriting project documentation or commentsExplaining complex academic concepts or existing codeOther

5. I can strategically design, test, and optimize prompts to obtain high-quality code or algorithmic solutions. (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

6. I am used to treating software development as an iterative process of “Plan → Prompt → Generate → Review → Refine → Validate” rather than relying on AI to produce a final result in one go. (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

7. I know how to break down complex engineering problems into sub-tasks that AI can understand and process. (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

8. I can quickly spot “hallucinations” in AI-generated content (i.e., outputs that appear plausible but are actually incorrect or non-existent functions/logic). (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

9. When facing AI-generated code, I always perform thorough manual review and independent testing rather than directly copying and pasting it into my project. (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

10. I understand the inherent biases that large language models (LLMs) may have and their potential impact on decision-making systems. (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

11. I consciously check AI-generated code for potential security vulnerabilities or performance issues. (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

12. I am very clear about the academic integrity boundaries when using AI tools in university courses and projects (i.e., what counts as allowed assistance versus academic misconduct). (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

13. I know how to write clear and transparent “AI Contribution Declarations” using process evidence (e.g., keeping prompt logs and version control). (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

14. I believe that as a developer I must take full responsibility for the final submitted system (including AI-generated parts) and maintain continuous “human oversight.” (1 = Strongly Disagree, 2 = Disagree, 3 = Neutral, 4 = Agree, 5 = Strongly Agree)

很不同意

很同意

15. After completing this course, what do you now think is the biggest challenge or risk when using AI-assisted development in complex robotics projects?

16. Looking back on this course, what do you think is the most important “responsible AI use skill” or “prompt engineering technique” you have learned? Please briefly explain with reference to your prompt logs or project experience.

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