# AI literacy

AI literacy or artificial intelligence literacy, is the ability to understand, use, monitor, and critically reflect on AI applications. The term usually refers to teaching skills and knowledge to the general public, particularly those who are not adept in AI.
Some think AI literacy is essential for school and college students, while some professors ban AI in the classroom and from all assignments with stern punishments for using AI, classifying it as cheating. AI is employed in a variety of applications, including self-driving automobiles and Virtual assistants. Users of these tools should be able to make informed decisions. AI literacy may have an impact students' future employment prospects.

## Definitions

One of the earliest and most common definitions for AI literacy was that it is "a set of competencies that enables individuals to critically evaluate AI technologies; communicate and collaborate effectively with AI; and use AI as a tool online, at home, and in the workplace."
Later definitions include the ability to understand, use, monitor, and critically reflect on AI applications, or the ability to understand, use, evaluate, and ethically navigate AI.
AI literacy is linked to other forms of literacy. AI literacy requires digital literacy, whereas scientific and computational literacy may inform it. Data literacy also has significantly overlaps with it.

## Categories

AI literacy encompasses multiple categories, including a theoretical understanding of how artificial intelligence works, the usage of artificial intelligence technologies, and the critical appraisal of artificial intelligence, and its ethics.

### Know and understand AI

Knowledge and understanding of AI refers to a basic understanding of what artificial intelligence is and how it works. This includes familiarity with machine learning algorithms and the limitations and biases present in AI systems. Users who know and understand AI should be familiar with various technologies that use artificial intelligence, including cognitive systems, robotics and machine learning.

### Use and apply AI

Using and applying AI refers to the ability to use AI tools to solve problems and perform tasks such as programming and analyzing big data.

### Evaluate and create AI

Evaluation and creation refers to the ability to critically evaluate the quality and reliability of AI systems. It also refers to designing and building fair and ethical AI systems. To evaluate correctly, users should also learn in which areas AI is strong, and in which areas it is weak.

### AI ethics

AI ethics refers to understanding the moral implications of AI, and the making informed decisions regarding the use of AI tools. This area includes considerations such as:
* Accountability: Hold AI actors accountable for the operation of AI systems and adherence to ethical ideals.
* Accuracy: Identify and report sources of error and uncertainty in algorithms and data.
* Auditability: Enable other parties to audit and assess algorithm behavior via transparent information sharing.
* Explainability: Make sure that algorithmic judgments and the underlying data can be presented in simple language.
* Fairness: Prevent biases and consider varied viewpoints. To do so, increase the diversity of researchers in the field.
* Human Centricity and Well-being: Prioritize human well-being in AI development and deployment.
* Human rights Alignment: Ensure that technology do not infringe internationally recognized human rights.
* Inclusivity: Make AI accessible to everyone.
* Progress: Choose high value initiatives.
* Responsibility, accountability, and transparency: Foster trust via responsibility, accountability, and fairness.
* Robustness and Security: Make AI systems safe, secure, and resistant to manipulation or data breach.
* Sustainability: Choose implementations that generate long-term, useful benefits.

### Enabling AI

Support AI by developing associated knowledge and skills such as programming and statistics.

## Promoting AI literacy

Several governments have recognized the need to promote AI literacy, including among adults. Such programs have been published in the United States, China, Germany and Finland. Programs intended for the general public usually consist of short and easy to understand online study units. Programs intended for children are usually project-based. Programs for students at colleges and universities often address the specific professional needs of the student, depending on their field of study. Beyond the education system, AI literacy can also be developed in the community, for example in museums.

### Schools

Schools use diverse pedagogies to promote AI literacy. These include:
* Performing a Turing test with an intelligent agent
* Creating chatbots
* Building apps using Blockly-based programming
* Project-based learning
* Building robots
* Data visualization
* Training AI models
Artificial intelligence curricula can improve students' understanding of topics such as machine learning, neural networks, and deep learning.

#### Case study: DAILy

The DAILy (Developing AI Literacy) program was developed by MIT and Boston University with the goal of increasing AI literacy among middle school students. The program is structured as a 30-hour workshop that includes the topics of introduction to artificial intelligence, logical systems (decision trees), supervised learning, neural networks, computational learning, deepfake, and natural language generators. Students examine the moral and social implications of each topic, as well as its occupational implications.

### Higher education

Before the second decade of the 21st century, artificial intelligence was studied mainly in STEM courses. Later, projects emerged to increase artificial intelligence education, specifically to promote AI literacy. Most courses start with one or more study units that deal with basic questions such as what artificial intelligence is, where it comes from, what it can do and what it can't do. Most courses also refer to machine learning and deep learning. Some of the courses deal with moral issues in artificial intelligence.

#### Case study: University of Florida

At the University of Florida, a comprehensive effort was made to infuse artificial intelligence into the curriculum across all disciplines. The goal of the move was to provide university students with the skills needed for the 21st century work market. As part of the project, over 100 new faculty members were recruited. Each student was expected to complete a fundamental artificial intelligence course as well as a course on ethics, information, and technology. Each student chose an extra course from a variety of academic areas, including medicine and business. Students who successfully completed all three courses earned an official certificate.
The transition was accompanied by an increase in hands-on learning at the university. Courses were held in collaboration with industry, where students and industry professionals tried to solve real-world problems together, with the help of AI tools.
To supervise the program, a team was formed to analyze existing and new courses and map the literacy areas covered in each. Each course was identified by the areas of literacy to which it related, allowing students to select courses that suited them and administrators to detect gaps or deficits in certain areas.