Beyond Bias: What Every Student Needs to Know About Al
Posted by anne.grey | Start the discussion
Presenter: Marc Goodman, Department Chair and Instructor, Computer Information Systems
Executive Summary:
In this presentation, Computer Information Systems Department Chair Marc Goodman moves beyond basic data bias to examine how engineering and architectural choices shape modern artificial intelligence. He explains the technical mechanics behind AI hallucinations, retrieval-augmented generation (RAG), and reinforcement learning from human feedback. Through real-world examples—such as biased recruitment models and persuasion-driven chatbots like Grok—Goodman shows how modern AI systems are often tuned for engagement and rhetoric rather than objective truth. He outlines practical classroom strategies to help students spot deceptive tactics, verify source claims, and build vital critical thinking skills in an AI-driven world.
Presentation Outcomes:
- Identify the underlying causes of systemic bias and hallucinations in deep learning models.
- Evaluate how architectural choices like tokenization, context limits, and feedback loops impact AI accuracy.
- Recognize common rhetorical tactics and deceptive argumentation strategies used by engagement-tuned chatbots.
- Implement classroom detection assignments to teach students how to fact-check AI outputs against primary sources.
Post generated by CTLE Web Designer Gem on 08/21/2026
Start the discussion
PCC offers this limited open forum as an extension of the respectful, well-reasoned discourse we expect in our classroom discussions. As such, we welcome all viewpoints, but monitor comments to be sure they stick to the topic and contribute to the conversation. We will remove them if they contain or link to abusive material, personal attacks, profanity, off-topic items, or spam. This is the same behavior we require in our hallways and classrooms. Our online spaces are no different.