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Using Generative AI in Course Creation: A Game-Changer for Educators

Let’s be completely honest – in the history of teaching and learning technology, few tools have been as transformative as generative artificial intelligence (AI). It is a truly powerful tool that can transform the way courses are created, personalized, and delivered. Not only does AI streamline the development process, but it also enhances the learning experience for students. Let’s talk about how generative AI can be used by course creators. 

What is Generative AI?

Generative AI refers to a subset of artificial intelligence technologies that can generate new content, including text, images, and even code, based on the data they have been trained on. This capability makes it an invaluable asset in various creative processes, including course creation – with a few caveats that we will discuss later.

Streamlining Content Creation

One of the most time-consuming aspects of course development is content creation. How many of us have found ourselves (more often than not!) staring at a blank screen, completely blocked? Generative AI can help by generating educational content, such as lecture notes, reading materials, and quiz questions. For example, you can input a topic outline into a generative AI system, which then produces comprehensive, high-quality content tailored to the course’s objectives. 


AI is not always factually accurate, and it will occasionally tell you an outright lie… but as long as you understand the limitations and check and revise accordingly, the resulting draft is still way better than the blank screen. 

Types of Content to Generate with AI

In a nutshell, there are currently two main types of content generation with AI – text (such as ChatGPT) and images (such as DALL-E, MidJourney, etc.). Text-to-speech tools have been gaining momentum as part of various video and e-learning tools, along with custom avatars. And OpenAI (a research organization known for its work on artificial intelligence and responsible for ChatGPT and DALL-E) recently announced the anticipated release of Sora – a text-to-video creation tool. For all content, different models will produce the output of various styles and quality. 

Personalizing Learning Experiences

Generative AI can create customized learning materials that cater to the individual needs and learning styles of students. Although there are claims by emerging AI-driven LMS vendors that their AI-driven systems can modify content in real time to better suit each learner (by analyzing data on students’ performance, preferences, and engagement levels), let’s remember that AI-generated content should be approached with caution – because it’s AI 🤥. However, we can still use it to generate drafts of customized learning materials with the specific characteristics of our students in mind. 

Enhancing Interactive Learning

Interactive (active, social, experiential) learning is crucial for student engagement and retention of information. Generative AI can help create dynamic simulations, interactive case studies, and gamified learning modules that make education more engaging. These interactive elements can adapt based on the learner’s progress, providing challenges that are neither too easy nor too hard, thereby optimizing the learning curve.

Facilitating Automated Assessments

Assessments is another area where generative AI can make a significant impact. AI can generate a wide range of test items, from multiple-choice questions to more complex problem-solving tasks. Most learning management systems can automatically grade certain types of questions and provide instant feedback to students. Some systems allow students to take automated quizzes multiple times, giving students more opportunities to learn from their mistakes. Creating a large pool of questions and setting up quizzes to randomly select from the pool of questions allows each quiz attempt to be different from the one before.

About Prompt Engineering

Take a closer look at the image below. Do you notice anything… off?

AI-generated image shows a doctor and a patient interaction. The doctor has three arms.

Why does the doctor have three arms, you may ask. 

Well, I asked DALL-E to generate an image of an interaction of a black middle-aged doctor and a black patient in his 50s.

However, the very first image depicted the doctor touching the patient’s shoulder. 

The client whose training I was developing specifically requested that there would be NO physical touching between the patient and the provider.  However, when I asked AI to remove the touching, the subsequent images depicted… hugging… of increasing impropriety. Ok, I said, regenerate the image with the doctor’s left hand touching the clipboard. And… VOILA!

At this point, there was no point in arguing with the thoroughly confused AI, so I started a new session.  

What is prompt engineering?

Prompt engineering is the practice of carefully crafting inputs (or “prompts”) to effectively communicate with artificial intelligence (AI) models, particularly those based on natural language processing (NLP), to achieve desired outputs or responses. 

Prompt engineering involves a blend of art and science, requiring an understanding of how AI models interpret and process language, as well as creativity to elicit the best possible responses. The goal is to maximize the efficiency and accuracy of the AI’s output by optimizing the input prompt.

Key Elements of Prompt Engineering

  • Clarity and Specificity: The prompt should be clear and specific to guide the AI towards generating the intended output. Vagueness or ambiguity can lead to irrelevant or inaccurate responses.

  • Context and Background: Providing sufficient context or background information within the prompt can help the AI model better understand the request and generate more accurate and relevant responses.

  • Prompt Structure: The way a prompt is structured can significantly impact the AI’s response. This includes using directives, questions, or statements to guide the type of output you’re seeking.

  • Iterative Refinement: Often, the first attempt at crafting a prompt might not yield the desired outcome. Prompt engineering usually involves iteratively refining the prompt based on the AI’s responses until the optimal output is achieved. As in my example above, iterative refinement in the same session can only take you so far. Sometimes, it’s better to just start over with a refined prompt. 


Generative AI is already playing a significant role in education, offering tools that can make course creation more efficient, personalized, and engaging. As AI technology continues to evolve, it will open up even more possibilities for innovative teaching and learning methods. However, the successful integration of generative AI into education still requires careful consideration of its limitations to ensure that it serves to enhance the educational experience rather than detract from it. 

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