3.2: Bias and Limitations in AI

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AI tools can be powerful partners in teaching and learning—but they are not neutral or infallible. The data used to train AI models reflects human decisions, cultural norms, and systemic biases. As a result, instructors need to critically evaluate the suggestions and content generated by AI tools to ensure equity, accuracy, and alignment with their instructional values.

⚠️ Recognizing AI Bias

Use AI to:

Understand how historical data and internet sources can introduce bias
Explore examples of cultural, gender, or racial bias in AI-generated content
Generate class discussion prompts about fairness and objectivity in technology

✅ Prompt Example:
“Explain how gender bias might appear in AI-generated hiring recommendations. Give an example.”
✅ Prompt Example:
“List three examples of bias that might be present in an AI writing tool trained primarily on English-language academic texts.”

🧱 Acknowledge AI’s Limitations

Use AI to:

Generate ideas, not finished products—AI may be factually incorrect
Supplement, not replace, subject matter expertise
Identify gaps or limitations in AI-generated answers, especially in niche or rapidly evolving fields

✅ Prompt Example:
“List possible limitations of using ChatGPT to explain Indigenous history in the U.S.”
✅ Prompt Example:
“Create a response to a student’s question about the ethics of AI-generated art—include one limitation of using AI in creative fields.”

🔎 Encourage Critical Use of AI

Use AI to:

Model how to fact-check and verify AI output
Help students recognize overgeneralizations, inaccuracies, or outdated references
Develop digital literacy by having students evaluate AI responses alongside scholarly sources

✅ Prompt Example:
“ChatGPT says that X causes Y. Find a peer-reviewed article to confirm or challenge this claim.”
✅ Prompt Example:
“Use an AI tool to draft a summary of a topic, then annotate inaccuracies or missing perspectives.”

🎓 Why This Matters for Instructors

AI can streamline workflows and enhance learning—but it also reflects the biases and blind spots of the data and people behind it. Instructors who understand these limitations are better prepared to use AI responsibly, guide student inquiry, and maintain academic rigor. By modeling critical use, faculty empower students to think ethically and evaluate sources—including digital ones—with care.

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