This collaborative guide offers an overview of artificial intelligence, including its history, key concepts, and terminology, with a focus on generative AI, its ethical considerations, and its applications in academics.
• Review academic institution policies.
• Consult course instructors for guidance.
• Check course syllabi for AI guidelines.
• If AI use is permitted, ensure proper citation.
This webpage from Duke LILE provides Duke policy for instructors regarding AI-generated content.
What are Ethical Considerations for AI Use?
Ethical considerations for AI are the principles and guidelines that govern the responsible development, deployment, and use of artificial intelligence, ensuring fairness, transparency, and accountability.
The Use of AI in Academia
The use of AI in academia raises concerns about academic integrity, bias in AI-generated content, data privacy, and the responsible use of AI-assisted tools. However, when implemented thoughtfully, AI can enhance research efficiency, personalize learning, and streamline administrative tasks, supporting innovation and academic growth.
| Benefits | Risks |
| Enhances Creativity - Generative AI can produce unique ideas, designs, and content, helping users brainstorm and innovate | Potential for Misinformation - AI-generated content may incorrect or misleading information requiring careful verification before use. |
| Automates Content Creation - AI streamlines writing, image generation, and coding, saving time and effort. | Ethical & Copyright Concerns - AI can unintentionally replicate copyrighted material or produce content that lacks originality, raising plagiarism risks. |
| Personalizes User Experiences - AI adapts response based on user preferences, improving engagement in education, marketing, and customer service. | Privacy & Data Security Issues - AI-driven personalization relies on user data, which can lead to concerns about data misuse, surveillance, or security breaches. |
| Accelerates Research & Problem-Solving - AI quickly analyzes large datasets, aiding in scientific discoveries, business insights, and technical solutions. | Bias in AI-Generated Insights - AI models may reflect biases from their training data, potentially leading to unfair or inaccurate conclusions in research and decision making. |
