TVC 2.0: Fostering AI Literacy & Creative Thinking
Media literacy was once a foundational course to help people develop their capacity to critically analyze messages, broaden their experience of media, and help them develop generative media capability to increase creative skills in making their own media messages. While still important media literacy has been replaced with AI literacy, which is the ability to understand, evaluate, and responsibly use artificial intelligence systems and tools. It encompasses knowing what AI can and cannot do, how it works, and its potential risks and benefits. Last May, the World Economic Forum published an AI Literacy Framework (AILit) that aims to empower learners to navigate an AI-integrated world with confidence and purpose.
As generative AI tools become ubiquitous in schools, workplaces and daily digital life, it’s crucial to instill responsible use from a young age. Learners are not only using AI — they are experimenting with it, often without formal guidance on its limitations and risks.
A couple of years ago I proposed a pre-college Creative AI & Design course for high school youth in Cambridge, MA. Youth signed up for a week-long, intensive program designed to quickly teach a large amount of information or skills in a compressed timeframe. I posted a blog article about it on the Lesley STEAM website. Meanwhile, AI has continued to reshape how people live, work and learn. I continued my daily practice of creating and posting AI-generated images on social media (something I’ve been doing for 8 years). I worked on a math enrichment app as a prompt engineer and worked with a costume designer to create concepts for a film. These engagements and projects inspired me to teach the Creative AI & Design course again this summer, with more AI literacy content.
The Research
AI is pervasive in youth’s lives. A 2024 survey of 1,274 U.S. students, aged 14–22, found that half of young people (51%) have ever used generative AI. The survey found that Black and Latinx youth are more likely to use generative AI tools. Those who have used generative AI primarily use it to get information (53%) and brainstorm ideas (51%) with other usages including getting help with schoolwork, making pictures or images, making sounds or music, assisting their jobs, or to write code. To design this summer’s course I explored a few models or frameworks for teaching and engaging with AI. The AI Literacy Framework (AILit) mentioned above aims to establish a common language about AI literacy, which is instrumental for consistency across diverse educational settings.
AI literacy equips learners and educators to understand both the risks and opportunities that AI presents, and to make meaningful and ethical decisions about its use. It helps learners critically evaluate AI’s impact on their lives, education, and communities while preparing them to shape the future.
The four domains of the AILit Framework represent different ways in which learners interact with AI such as engagement, creativity, management, and design. Creativity involves guiding and refining AI output through prompts and feedback, while ensuring the content remains fair and appropriate. It also involves ethical considerations related to content ownership, attribution, and the responsible use of existing materials. Design empowers learners to understand how AI works and connect it to its social and ethical impacts by shaping how AI systems function. The 2024 Creative AI & Design course touched on several of these ‘nodes’ such as bias, copyright, and ideation.
Also, I looked at the Passive-Participatory (PP) model that is centered on the creative engagement of learners. Creative engagement ranges from passive consumption to expansive learning. Much of the content for the Creative AI & Design course focuses on individual content creation where the learner understands how to generate AI content. However, the most popular activity last year was the prompt battle. A prompt battle is a live competition where participants use text-to-image AI software to generate images based on provided prompts, often with the goal of creating the most compelling images. It’s a playful and engaging way to explore the capabilities of generative AI and the art of prompt engineering.
The Course
The Creative AI & Design course aims to engage youth in activities to develop their knowledge, skills, and attitudes regarding AI. It’s a pre-college course and youth receive a stipend for their participation. Prior to this course we offered a pre-college Art, AI & Robotics course for high school students. Course activities culminate in the presentation of capstone projects that demonstrate the students’ mastery of these skills. Students engage in project reviews to evaluate their peers’ work in a mutual exchange. Members of the community are also invited to experience and respond to the students’ work. Some of these activities were carried over into the Creative AI & Design course, which includes:
- Hands-on projects
- Image and audio creation
- Prompt battles
- AI bias & ethics
- Career exploration
- Capstone projects
What is TVC 2.0?
TVC stands for techno-vernacular creativity, a term and framework I developed to center the creative and active technological engagements of groups that are often underrepresented in STEAM fields. The taxonomy I created describes different modalities or ways of making or doing such as remixing, reappropriation, and improvisation. TVC puts a culturally relevant lens over the AILit and PP frameworks. This lens involves consciously considering cultural factors when interacting with or evaluating AI work. It acknowledges the unique backgrounds, beliefs, and values that shape learners’ perspectives and experiences. The initial TVC work pre-dates the emergence of AI. My daily experiences in the creative AI space and teaching courses like Creative AI & Design has prompted a revisitation of the original TVC framework and taxonomy.
In the new diagram (see right) the habits are the same: mixing together different elements (syncretize); re-viewing elements to place them in historical, cultural, material, or intellectual contexts (contextualize); and combine different elements to create a new, unified whole (synthesize). The TVC modalities (ex. remixing) are the same as well. In the center of the diagram are the new skills that have been revised to reflect creative AI.
Gather: Use tools or prompts to spawn new instances of concepts, ideas, or works, based on training data.
Combine: Make different prompts from just one prompt by swapping certain elements within a prompt to create different versions. Also, combine several images into a single, unique image.
Describe: Use tools to analyze images or sounds that can be uploaded to the system, then generate words or sounds that describe the main image, sound, or idea. Use prompts to interpret or reinterpret existing works.
Riff: Use prompts to freely explore concepts or ideas, especially in order to adapt or modify content. Create different versions of the same content by changing a few details.
Repeat: Use tools to select certain features of a work (ex. image or sound) and produce infinite output with these selected features.
Revise: Use tools to reconstruct, refine, or fill in missing or damaged areas of an AI-generated image or artwork.
These new TVC skills, which are based on or inspired by culturally relevant ways of making and doing, are aligned with fundamental AI literate abilities such as critical and creative thinking, prompt engineering, and computational thinking. It is important that creative people keep their toolboxes up-to-date and it’s critical that young people acquire skills they will need to thrive in the world.