💡 What is generative artificial intelligence (AI) and what does it mean for higher education?


AI developments and usage will continue to expand swiftly across the world. In the interest of our University’s mission to prepare our students for life, work and citizenship in the twenty-first century, we must lean into evolving technologies such as AI. Our campus community is encouraged to learn about and integrate innovative AI solutions in a responsible, secure and ethical manner. By embracing AI ourselves, we can more effectively prepare our students for their futures. We expect to update this information and add additional articles to the Knowledge Base to stay current with the rapidly evolving AI technologies, address questions that arise, and incorporate new or changing regulations and best practices. 


What is generative artificial intelligence (AI) and what does it mean for higher education? 


Artificial intelligence (AI) has become a catchall term for applications that perform complex tasks that once required human input, such as communicating with customers online or playing chess. The term is often used interchangeably with its subfields, which include machine learning (ML) and deep learning. 

There are differences, however. For example, machine learning is focused on building systems that learn or improve their performance based on the data they consume. It’s important to note that although all machine learning is AI, not all AI is machine learning. 

Generative AI is a kind of artificial intelligence that creates new content, including text, images, audio, and video, based on patterns it has learned from existing content. Today’s generative AI models have been trained on enormous volumes of data using deep learning, or deep neural networks, and they can carry on conversations, answer questions, write stories, produce source code, and create images and videos of any description, all based on brief text inputs or “prompts.” 

Generative AI is called generative because the AI creates something that didn’t previously exist. That’s what makes it different from discriminative AI, which draws distinctions between different kinds of input. To say it differently, discriminative AI tries to answer a question like “Is this image a drawing of a rabbit or a lion?” whereas generative AI responds to prompts like “Draw me a picture of a lion and a rabbit sitting next to each other.” 

What (generative) AI means for higher education is a very large question that is difficult to answer right now. The field of AI is constantly growing, changing, and adapting, making it difficult to predict exactly what will happen in the future.  

The implications for generative artificial intelligence on higher education are still developing, but what is clear is that these tools are not going to disappear. It’s also worth noting that as we consider the impact this new technology has on higher education, plenty of other enterprises have already been, and will continue to be, impacted by it as well. As with any new technology, it will take time for everyone to figure out to what degree they find it useful. Right now, the best thing to do, for students and instructors alike, is to familiarize themselves with the strengths and weaknesses of these types of software and to experiment from a critical perspective. Many of them, like ChatGPT, are open access. It is through personal practice and exploration that you will understand the benefits, limitations, and areas of concern. 

Types of generative AI tools 

  • Text generation tools for stories, articles and dialogue. 
  • Image generation tools for new and visually appealing images based on existing patterns or styles. 
  • Music generation tools for melodies, harmonies and even entire compositions. 
  • Video generation tools for creating or enhancing videos. 
  • Design and creativity tools for designs, artwork or other artistic elements. 
  • Game content generation tools for creating game levels, characters or other elements to enhance gameplay. 
  • Data augmentation tools for augmenting or enhancing existing datasets, and improving training for AI models. 
  • 3D model generation tools for generating models based on specified parameters or examples. 

Why use generative AI tools? 

  • Content generation: Generative AI tools can help with producing fresh and original content like copy, images, music and videos. 
  • Idea exploration and inspiration: Generate new ideas, concepts, designs or compositions. 
  • Time and cost efficiency: Automate repetitive or resource-intensive tasks, saving both time and costs. 
  • Personalization and customization: Create personalized content tailored to individual preferences or requirements. 
  • Data augmentation and training: Augment training datasets by generating synthetic data with realistic variations. This helps machine learning models work better and be more accurate. 

Note: While generative AI tools have lots of advantages, you'll need to make sure the generated content respects copyright, privacy and legal requirements. 


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Article ID: 146843
Mon 8/14/23 2:01 PM
Tue 5/14/24 2:10 PM

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