15 Preparing Students for the Future: Teaching AI Literacy
As AI tools and models become increasingly available, students will likely be expected to make use of them in a range of academic and professional settings. AI will also likely become a core part of common tools with the line between non-AI and AI features becoming increasingly blurred. Faculty can develop courses to help students build the digital literacy skills that will be required to engage technology of every kind. In-class and independent assignments can guide students to master (and think critically about) prompt engineering, as well as the quality of the content that AI tools generate.
The University of Florida has developed an “AI Across the Curriculum” initiative to provide all students the opportunity to develop their AI skills for future workforce participation. This will be accomplished through a combination of foundational courses, including “Fundamentals of AI” and “Ethics, Data, and Technology”. They’re also offering disciplinary courses such as:
- “AI in Agricultural and Life Sciences”
- “AI in Social Sciences”
- “AI in the Built Environment”
- “AI in Media and Society”
This means students have the ability to earn a certificate in AI to complement their degree. This approach does have the downside of requiring adding on several stand-alone courses to a student’s degree. However, consideration of the learning outcomes identified in the University of Florida’s program or other similar models might help institutions or programs find opportunities to infuse AI across the curriculum in existing courses
However, even without AI directly integrated into the curriculum, there are already some resources that are available to students. For example, the University of Sydney has created a great website called AI in Education, which is a guide for students (created by students!) with information like creating resumes, understanding content, and overcoming writer’s block. There will certainly be more information like this for students in the future, which is one of the reasons it’s so important to be transparent about these tools now.
Case Study: AI in Design
One example of a field that has already seen the impact of AI is the field of design, which is rapidly evolving. While generative AI tools such as Midjourney and DALL-E (AI that generates images from prompts) have only begun to grab the attention of the general public recently, AI design tools have been used in the industry for some time. For example, Netflix’s AVA (aesthetic visual analysis) is a collection of tools and algorithms which encapsulate the key intersections of computer vision combined with the core principles of filmmaking and photo editing. This AI is being used to create the thumbnails and trailers based on users’ interactions with the content on the platform. Similarly, Alibaba’s AI tool, Luban, is capable of creating eight thousand customized banner ads in one second. Even before the hype AI is currently receiving, many everyday interactions were created by a small design team using AI tools (or, in many cases, created entirely by AI).
In addition, AI is being used to automate many tasks that were traditionally done by designers, such as generating layouts, creating color schemes, and even coming up with new ideas. Here are three examples of different AI tools in design and what they can do:
- Adobe Photoshop’s Content-Aware Fill uses AI technology to select and blend the best replacement pixels.
- Figma’s AI Designer plugin can streamline and enhance the user experience design process.
- Topaz Gigapixel AI can upscale and enhance image detail and resolution by 600%.
Now designers have more flexibility to tackle even more important tasks beyond design production. AI will not replace designers, but it will change the role of designers in the future. Designers will need to bring creativity, critical thinking, and problem-solving skills to the creative process, but they will also need to be proficient with AI tools and technologies.
