A note on some clarifications related to the potential misuse of generative AI to do assignments and dissertations: for Housing Studies students
My question 1 with MS copilot: "My question to MS copilot: "What are the main dishonest behavior offences of university students (e.g. on housing studies) related to the misuse of generative AI tools in doing course assignments and dissertation projects?"
The reply: "University students misuse generative AI tools in ways that often constitute academic dishonesty, including plagiarism, unauthorized assistance, and data fabrication—especially in disciplines like housing studies.
Here’s a breakdown of the most common offences:
🧠 Key Dishonest Behaviors Involving Generative AI
1. AI-Plagiarism (“AI-giarism”)
Definition: Submitting AI-generated content without proper attribution or pretending it’s original work.
Examples:
Copy-pasting entire essays or literature reviews from ChatGPT or similar tools.
Using AI to paraphrase existing sources to evade plagiarism detection.
Impact: Undermines academic integrity and violates institutional plagiarism policies.
2. Unauthorized Use of AI for Assignments
Definition: Using AI tools when explicitly prohibited by course guidelines.
Examples:
Employing AI to write reflective journals, design housing models, or complete take-home exams.
Using AI to generate citations or references that were never consulted.
Impact: Breaches rules on independent work and misrepresents student effort.
3. Fabrication or Falsification of Data
Definition: Creating fake data or results using AI tools.
Examples:
Generating fictitious survey responses or housing market statistics for dissertations.
Using AI to simulate interviews or case studies that never occurred.
Impact: Violates research ethics and can lead to severe academic penalties.
4. Ghostwriting via AI
Definition: Using AI as a surrogate author for substantial parts of academic work.
Examples:
Having AI write entire chapters of a dissertation.
Submitting AI-generated policy analysis or urban planning proposals.
Impact: Considered equivalent to hiring a ghostwriter, which is a serious misconduct.
5. Misrepresentation of AI Assistance
Definition: Failing to disclose the extent of AI involvement in academic work.
Examples:
Claiming originality in work heavily shaped by AI suggestions.
Not acknowledging AI-generated visualizations or design elements in housing studies.
Impact: Breaches transparency and authorship standards.
🏛️ Why Housing Studies Are Vulnerable
Housing studies often involve:
Data-driven analysis (e.g., affordability metrics, urban planning models)
Design and visualization tasks (e.g., architectural layouts, GIS mapping)
Policy critique and proposal writing
These areas are particularly susceptible to AI misuse because tools can easily generate plausible—but unverified—content that appears academically rigorous.
🛡️ Institutional Responses
Universities are responding with:
Updated academic integrity policies to include AI-specific clauses.
AI detection tools to flag suspicious submissions.
Ethics training to help students understand responsible AI use."
*** Also study this blog note on this topic."
My question 2 with MS copilot: "My question to MS copilot: "Generative AI tools are very useful to generate a relevant and illustrative assignment prototypes (e.g. on a dissertation proposal) for Housing Studies students. Please give some advice how these students could properly use these AI generated assignment prototypes to facilitate them to make their own study learning (e.g. reading academic materials) and reflective/ critical thinking so as to produce their own version of their assignment works. The concern is also about avoiding dishonest academic behaviour by the students."
The reply: "That's a thoughtful and important concern — especially as generative AI becomes more integrated into academic work. Here's a guide tailored for Housing Studies students on how to ethically and effectively use AI-generated assignment prototypes to enhance their learning and critical thinking:
🧠 How to Use AI-Generated Assignment Prototypes for Deep Learning
1. Use Prototypes as Scaffolding, Not Substitutes
Treat AI-generated content as a starting point or outline, not a finished product.
Analyze the structure, flow, and logic of the prototype to understand how arguments are built.
Ask: Why did the AI organize it this way? What assumptions are being made?
2. Engage with Academic Sources
Use the prototype to identify key themes or gaps in your understanding.
Follow up by reading peer-reviewed articles, policy papers, and housing reports to deepen your knowledge.
Compare AI-generated claims with real academic evidence — this builds critical literacy.
3. Practice Reflective Thinking
After reviewing the prototype, ask yourself:
Do I agree with the arguments presented? Why or why not?
What perspectives are missing?
How does this relate to my own experiences or case studies in housing?
Keep a reflection journal to track your evolving thoughts and insights.
4. Customize with Your Voice and Context
Rewrite sections using your own words, examples, and interpretations.
Incorporate local housing issues, policies, or case studies relevant to your region or interest.
This ensures your work is authentic and original.
5. Cite AI Assistance Transparently
If you use AI to generate ideas or outlines, acknowledge it in your methodology or appendix.
This promotes academic integrity and shows you're engaging responsibly with new tools.
🚫 Avoiding Dishonest Academic Behavior
❌ Don’t Copy-Paste
Submitting AI-generated text without modification or understanding is plagiarism.
Universities increasingly use tools to detect AI-generated content.
✅ Do Learn from It
Use AI to model good writing, not to bypass the learning process.
Think of it as a study partner, not a ghostwriter.
🧭 Ask for Feedback
Share your draft with peers or tutors and discuss how you’ve built on the prototype.
This reinforces your learning and helps you stay accountable.
🏡 Housing Studies-Specific Tips
Use AI to brainstorm policy comparisons, urban planning models, or housing affordability frameworks.
Then dive into local government data, housing surveys, or community interviews to enrich your analysis.
Reflect on ethical dimensions like gentrification, homelessness, or housing rights — areas where critical thinking is essential.".
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