Module 2 Overview: GenAI and Ethics
📌 About Module 2
🔎 Module 2 Learning Objectives
By the end of this module students will be able to:
- Distinguish between acceptable and unacceptable use of GenAI in different contexts
- Analyze the ethical implications of GenAI use in academic and professional settings
- Understand the instructor’s academic integrity policy regarding GenAI use
- Practice transparent citation and disclosure of GenAI use
The module is designed to address the following elements based on the credit and contact hour limitations of the relevant course:
| Essential | Academic integrity, ethics, citation and attribution |
| Recommended | Misinformation (fakes), Hallucinations |
| Optional | Bias, environmental impact, labor, equity |
🗂️ Module Components
📌About Module 2
- Overview
- Learning Objectives
- Module Components
- Key Concepts
🧭 Lesson Plans for Module 2
- 60 minute lesson plan
- 90 minute lesson plan
- Studio Hour lesson (60 mins)
📖 Teaching Resources for Module 2
- Essential Materials
- Recommended Materials
- Optional Materials
🎲 Student Activities
Instructors are encouraged to review the available materials and use their discretion to remix and integrate elements of the module as suited to their unique pedagogical goals and circumstances.
💡 Key Concepts
This language has been written as student-facing so you can copy and paste it directly into Brightspace or your instructional slides.
1. Bias
In GenAI, bias occurs when an algorithm produces results that are unfairly prejudiced or skewed. This usually happens because the data used to train the GenAI contains human prejudices or doesn’t represent everyone equally.
The School Analogy: If a “Student of the Month” GenAI was trained only on data from the football team, it might wrongly conclude that you must play sports to be a good student, ignoring the amazing artists and scientists in the school.
2. Hallucinations
GenAI hallucination is when a model generates a response that sounds confident and factual but is actually completely made up. GenAI doesn’t “lie” on purpose; it just predicts the next word in a sentence so convincingly that it can accidentally invent fake historical dates, non-existent book titles, or “facts” that never happened.
3. Citation
A citation is the practice of giving credit to the original source of information. In the world of GenAI, this is tricky because most GenAI models don’t naturally tell you exactly which website or book they got their specific answer from. A core component of GenAI literacy (and one students often don’t encounter until it’s too late) is knowing how to properly cite GenAI-generated content. These modules teach you how to cite GenAI using the major citation styles you’ll encounter across your courses, including MLA and APA. You will also learn how to generate a transcript of their GenAI sessions, a skill that matters because instructors may ask you to submit that transcript as part of your work.
- Why it matters: Using GenAI-generated text without attribution is widely considered a form of plagiarism, and you need to understand that clearly before you develop habits that could put your academic standing at risk. Just as you would credit a human author, you need to credit GenAI when it contributes to their work.
- Beyond attribution, you should also understand how to use GenAI responsibly as a research aid. These modules encourage you to prioritize GenAI tools that provide links to their sources, so you can verify the information and properly credit the human creators whose work the tool drew on. This connects GenAI literacy directly to the information literacy and source evaluation skills they’re building across their courses.
4. Ethical Use
Ethical use is the “Golden Rule” of GenAI. It refers to using these tools in a way that is honest, fair, and doesn’t hurt others. This includes:
- Academic Integrity: Not using GenAI to cheat on assignments.
- Transparency: Being open about when you’ve used GenAI to help you create something.
- Safety: Not using GenAI to generate harmful content or spread misinformation.
- Intellectual honesty: Representing your own thinking and learning accurately
🔗 Links to Module 2 Materials
(these also appear as subpages under the Module 2 dropdown menu)
