Generative AI

What is Generative AI?

Generative AI refers to a type of artificial intelligence that can create new content, such as images, text, music, or even code. Unlike traditional AI, which follows predefined rules, generative AI learns patterns from existing data and uses this knowledge to generate original works. Popular examples of generative AI systems include ChatGPT for text generation, DALL·E for creating images from text prompts, and MusicLM for composing music. These tools are revolutionizing how we interact with technology, enabling creativity in new and exciting ways.

Risks of Generative AI?

While generative AI offers incredible opportunities, it also comes with several risks that are important to understand:

Accuracy: Generative AI systems can sometimes produce content that is inaccurate or misleading. Since these AI models rely on patterns from the data they were trained on, they might generate incorrect information, particularly in complex or less common topics.
Quality: The quality of AI-generated content can vary significantly. While it can produce impressive results, it may also create content that is unclear, poorly structured, or lacks the nuance of human creativity. This inconsistency can make it difficult to rely solely on AI-generated outputs.
Over-Reliance: There’s a risk that people might become too dependent on generative AI, using it as a crutch instead of developing their own skills. Over-reliance on AI could hinder learning and critical thinking, especially in educational settings.
Bias: Generative AI models can inherit biases from the data they are trained on. If the training data contains biases, the AI can reinforce and amplify these biases, leading to unfair or harmful content. This can be particularly problematic in sensitive areas like hiring, law enforcement, or news generation.
Learning Challenges: While generative AI can be a great tool for learning, it might also pose challenges. If students rely too heavily on AI for answers or assignments, they might miss out on the deeper understanding that comes from engaging directly with the material.
Ethical Concerns: The use of generative AI raises important ethical questions. For instance, should AI-generated content be clearly labeled? How do we ensure that AI is used responsibly and doesn’t contribute to misinformation or unethical practices?
Data Privacy: Generative AI systems often require large amounts of data to function effectively. This can lead to concerns about privacy, as sensitive or personal information might be used to train these models without proper consent.

Image above created with Dall-E 3

(It took me 8 tries to get this and it's still not right - can you tell?)

Permitted Uses of Generative AI in A COMPUTER SCIENCE Classroom

Generative AI can be a powerful tool for learning in a computer science classroom when used appropriately. Here are some examples of how students can use AI tools like ChatGPT, Magic School, or code generators to enhance their understanding without relying on them to do the work:

Brainstorming Ideas: Students can use AI to help brainstorm project ideas or approaches to solving a problem. For example, ChatGPT can suggest different ways to structure a program or provide examples of how a particular algorithm could be implemented. This helps students get started while still requiring them to write their own code.
Learning New Concepts: If students are struggling with a concept, they can use AI to get explanations or examples. For instance, Magic School can break down complex topics like recursion or object-oriented programming into simpler terms. This supports learning by providing additional resources, but students still need to apply what they’ve learned on their own.
Debugging Assistance: AI tools can be used to help students understand and fix errors in their code. For example, a code generator might point out syntax errors or suggest alternative ways to approach a problem. However, students should use these suggestions as learning opportunities to understand their mistakes, rather than just copying and pasting solutions.
Enhancing Creativity: Generative AI can inspire students to think creatively. For example, AI-generated artwork or music could be used as part of a broader project in game development or multimedia design. This allows students to incorporate AI into their work without depending on it to create the entire project.
Supplementing Research: When working on research projects or presentations, students can use AI to find information or generate outlines. For instance, ChatGPT can help structure a presentation on the history of programming languages. However, students should critically evaluate the AI’s output and ensure they understand and can explain the content themselves.
Practice Problems: AI tools can generate practice problems or quizzes on various topics. For example, a code generator might create sample problems for students to solve, helping them practice coding skills in different languages. This gives students additional practice without doing the work for them.
Understanding Documentation: Students can use AI to help interpret and understand complex documentation for new programming languages, libraries, or APIs. This allows them to grasp the essentials quickly, but they still need to experiment and apply the information in their own projects

Note: If you use AI tools, you should always include a statement, or comment, about how they were used in completion of the assignment.

INAPPROPRIATE USES OF GENERATIVE AI IN A COMPUTER SCIENCE CLASSROOM

While generative AI can be a valuable tool for learning, there are ways it can be misused, leading to a hindrance in a student’s educational development. Here are four examples of inappropriate uses:

Copying Code Without Understanding: If a student uses an AI code generator to produce a complete solution and submits it as their own work without understanding how it functions, they miss out on the critical learning process. This kind of misuse can lead to a superficial understanding of coding concepts, making it difficult for the student to solve problems independently in the future. And passing a test on the concepts will be difficult.
Plagiarizing Written Assignments: Using AI like ChatGPT to generate entire essays, reports, or project explanations and passing them off as original work is a clear violation of academic integrity. This not only hinders learning but also undermines the purpose of assignments, which is to develop the student’s own critical thinking and writing skills.
Bypassing Debugging and Problem-Solving: If a student encounters an error in their code and instead of attempting to debug it themselves, they use AI to generate a corrected version, they miss out on the essential problem-solving practice. Debugging is a key skill in programming, and avoiding this process can prevent students from developing the resilience and analytical skills necessary for coding.
Overusing AI for Routine Tasks: Relying too heavily on AI to perform routine tasks like generating comments, formatting code, or writing simple functions can lead to a lack of familiarity with the basic building blocks of programming. This over-reliance can cause gaps in foundational knowledge, making it harder for students to tackle more complex problems as they advance in their studies.

Note: 80% of this web page was generated with Chat GPT 4o but with my input (prompts), corrections and review. - Mrs. Hansen

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