Objective
To critically examine and understand the ethical implications and creative potentials of generative AI in academic work. This includes exploring academic integrity, access and privilege, representation of knowledge, environmental impacts, and other ethical considerations.
Part 1: Personal Reflection and Examination
Instructions
Write a reflective essay (1000–1500 words) addressing the following questions. Be sure to draw on your personal experiences and prior knowledge of academic ethics.
Understanding Academic Ethics
- What does academic integrity mean to you? How have you practiced it in your academic work?
- Describe any experiences you’ve had with ethical dilemmas in your academic journey. How did you resolve them?
- Where did you learn about academic integrity?
- What are the most important ethics rules to live by for yourself as a student?
Generative AI and Ethics
- How do you perceive the role of AI in academic work? What potential ethical challenges do you foresee?
- Reflect on your understanding of using AI tools in academic settings. What concerns do you have regarding academic integrity when using AI?
- Does your use (actual or intended) of AI tools open up questions about ethics and integrity? How so?
- How does the VT AI Research Guidance support or push against your own initial ideas about research and ethics using AI?
Part 2: Interacting with AI Tools
Instructions
Select three different AI chatbots (e.g., ChatGPT, Bing AI, Bard). Ask each bot the following questions and document their responses. Analyze the similarities and differences in their answers. Once your exploration is complete, write a comparative analysis (1000 words) based on the AI responses. Highlight key ethical issues, potential biases, and your thoughts on the integrity of AI-generated information.
Ethics and Integrity
- What are the ethical considerations when using AI in academic work?
- Can AI tools be used to cheat in academic settings? If so, how can this be prevented?
- What are the gray areas in using AI for academic purposes? How should they be navigated?
Access and Privilege
- How accessible are AI tools to students from different socioeconomic backgrounds?
- What privileges exist in accessing and using AI tools in academic settings?
Knowledge Representation
- How do AI models ensure the representation of diverse knowledge systems, including indigenous knowledge?
- What biases exist in the training data of AI models? How can these biases be mitigated?
Environmental Impact
- What is the environmental impact of running AI servers and data centers?
- How can the environmental footprint of AI be reduced?
Part 3: Case Studies and Discussions
Revision Note: Are these case studies? Or just scenarios?
Instructions
Read the following case studies and prepare to discuss them in class. For each case study, consider the ethical implications, possible solutions, and your stance on the issue.
Case Study 1: Misrepresentation of Information
A student uses an AI tool to generate a research paper but finds that the AI has fabricated sources and data. Discuss the ethical issues and how the student should handle this situation.
Alternate Option: expand to see full case study example
Case Study 1: Misrepresentation of Information
Background:
Jordan, a third-year university student majoring in environmental science, is struggling to meet a deadline for a research paper on the effects of microplastics on marine life. Under pressure, Jordan turns to a popular generative AI tool to help generate content. The AI provides a well-written draft complete with citations and statistics. Relieved, Jordan submits the paper with minor edits.
The Problem:
A week later, Jordan’s professor returns the paper with serious concerns. Several of the cited sources do not exist, and some of the data is unverifiable. The professor suspects academic dishonesty and requests a meeting. Jordan realizes the AI fabricated references and data, something they hadn’t noticed during their rushed review.
Ethical Issues:
This situation raises key ethical concerns:
- Misrepresentation of information: Presenting false data and sources violates academic integrity.
- Failure to verify AI-generated content: Relying on AI without fact-checking undermines the credibility of student work.
- Accountability: Even if unintentional, submitting inaccurate work misleads the instructor and damages trust.
Resolution:
Jordan admits to using AI and failing to verify the information. The professor allows a resubmission but requires Jordan to meet with the academic integrity office. Jordan reflects on the importance of being a responsible researcher and learns how to use AI as a writing aid—not a replacement for research.
Key Takeaway:
AI can support the writing process, but students must critically evaluate its output. Academic integrity requires diligence, especially when using new tools that may produce plausible but false information.
Case Study 2: Access and Privilege
In a resource-limited school, only a few students have access to advanced AI tools. Discuss the ethical implications of this disparity and propose potential solutions to ensure equitable access.
Case Study 3: Environmental Impact
A university decides to incorporate AI tools extensively in their curriculum. Discuss the potential environmental impacts and suggest ways to minimize the carbon footprint of these tools.
Class Discussion
Participate in a group discussion, sharing your insights and solutions for each case study. Prepare a short presentation (5–7 minutes) on your group’s findings.
Part 4: Final Reflection and Creative Exploration
Instructions
Write a final reflection (1000 words) summarizing your learning from the assignment. Address the following points:
- Your evolved understanding of academic ethics in the context of AI.
- The most surprising insights gained from interacting with AI tools and discussing case studies.
- How you plan to ethically and creatively use AI in your future academic work.
Creative Exploration
Create a visual representation (e.g., infographic, poster, video) that captures the key ethical considerations and creative potentials of generative AI. Be prepared to share and explain your creation in class.
Assessment Rubric
| Criteria | Excellent (90–100) | Good (80–89) | Satisfactory (70–79) | Needs Improvement (Below 70) |
|---|---|---|---|---|
| Personal Reflection | Deep, insightful reflection, well-organized, and clearly articulated. | Good reflection with clear insights and organization. | Satisfactory reflection with basic insights. | Lacks depth, unclear, poorly organized. |
| AI Interaction Analysis | Thorough analysis, well-documented AI responses, clear comparisons. | Good analysis, documented AI responses, clear comparisons. | Basic analysis, some AI responses documented. | Lacks thorough analysis, poorly documented. |
| Case Study Discussion | Insightful, well-prepared presentation, clear solutions. | Good discussion, prepared presentation, solutions present. | Basic discussion, some preparation. | Unclear discussion, lacks preparation. |
| Final Reflection | Comprehensive summary, clear evolution of understanding. | Good summary, clear understanding. | Basic summary, some understanding evident. | Incomplete summary, unclear understanding. |
| Creative Exploration | Highly creative, visually appealing, effectively communicates key points. | Creative, visually appealing, communicates key points. | Some creativity, communicates key points. | Lacks creativity, unclear communication of key points. |
Last Updated on July 22, 2025