Artificial Intelligence: Teaching and Learning Guide

Generative artificial intelligence (AI) comes with certain limitations that need to be considered including but not limited to:

• Quality and Reliability: The quality and reliability of outputs generated by generative AI models can vary. While they can produce impressive results, there are instances where the generated content is inaccurate, nonsensical, or lacks coherence.  
• Bias and Ethical Concerns: Generative AI models learn from large datasets, which can inadvertently perpetuate biases present in the training data. This can lead to biased or discriminatory outputs, reinforcing societal prejudices. Careful evaluation and mitigation of biases are essential to ensure fair and ethical use of generative AI.
• Interpretability and Explainability: Generative AI models often operate as “black boxes,” making it challenging to interpret and understand how they arrive at their outputs.
• Human Oversight and Expertise: Generative AI systems still require human oversight and expertise. While they can automate certain tasks, human intervention is necessary to validate, refine, and interpret the generated outputs. Skillful human guidance is vital to avoid potential errors or misinterpretations by the AI models.

Understanding these limitations is crucial for responsible and effective use of generative AI in education. It is essential to continuously monitor and address these challenges to harness the potential of generative AI in education while mitigating associated risks for students, instructors, staff, and the college.