Jan 13 2023

How Can Generative AI Be Used in Higher Ed?

Machine learning that generates images and video could find use cases in higher education.

Noticed any fantasy-inspired portrait posts showing up on your social media feeds lately? You might just be looking at artificial intelligence-generated content, a new development in the exponentially expanding world of AI tech that has emerged over the past few years.

Platforms like Stable Diffusion and DALL-E 2 allow users to input a text prompt, which creates an AI-generated image that may or may not accurately reflect what the user intended.

Microsoft recently announced that it will be adding AI image generation to the Office 365 suite, allowing users to insert these images into Word documents and PowerPoint presentations. Google’s Imagen Video platform allows users to create videos from text prompts, and Meta also has announced a similar video generation tool.

But beyond impressing your social media followers, generative AI, while still in its infancy, has the potential to benefit and disrupt visually oriented sectors such as gamingvirtual and augmented reality, and film animation. It also has applications for the image- and video-reliant world of higher education.

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How Does AI-Generated Content Work?

Tianfu “Matt” Wu, associate professor in North Carolina State University’s Department of Electrical and Computer Engineering and head of the university’s Laboratory for Interpretable Visual Modeling, Computing and Learning, says there are two major models being tested: a text encoder trained to understand text inputs and an image decoder trained to synthesize a photorealistic image from the output of the text encoder.

“To a certain extent, the models are trained to encode and align different content, such as imagery and text, in some mathematical latent vector space, on top of which the models can be deployed to generate content,” Wu says. “The mapping between content and vector is made possible due to the remarkable capability of deep neural networks and the availability of web-scale paired data, such as image-text pairs.”

Text prompts, however, are only one possible input or “condition” for generative AI content. Other inputs include sketches, other images or randomly sampled vectors, which follow a process called “unconditional generation.”

What Potential Does AI-Generated Content Hold?

While generative AI might simply seem like a fun experience for casual users, Wu says this technology could significantly affect society. In fact, AI-generated content could serve as “powerful and useful assistants and assets to human experts by significantly improving their productivity and by potentially augmenting their creativity,” he says.


The percentage of all data that generative AI will produce by 2025

Source: gartner.com, “Gartner Identifies the Top Strategic Technology Trends for 2022,” Oct. 18, 2021

On the research side, the success of AI-generated content demonstrates that “we now have methods and tools that are capable of modeling and capturing latent distributions of high-dimensional data, such as imagery, in a mathematically sound way, which has been a longstanding problem in the literature,” Wu says.

With these modeling and learning capabilities, Wu expects that the next wave of AI will spread into almost all disciplines of science and engineering.

Within the higher ed sector, Wu believes that AI-generated content could have a huge impact and be applied in “rethinking and redesigning education resources and tools to facilitate better and faster learning experiences at all levels.”

For example, AI could create personalized content models for teaching and learning and replace textbooks.

“Similar in spirit to how YouTube has reshaped the learning experience for almost everyone, AI-generated content will certainly do more,” he says. “It will be interesting to see how AI-generated content will play out within the higher ed space in the near future.”

Getty Images: Orientfootage (student), Natalya Maevskya (grid)

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