AI literacy

Basics

Find out what the intended users understand about AI: what it can and cannot do, that it can be wrong, and how to use it well for this task. The Digital Futures Lab playbook calls this critical AI literacy: an awareness of AI's strengths and limits, fitted to the user's needs and context src-028 p61. AI literacy rests on general digital skills, so find out those too src-028 p61; src-020 pp7-9. Many people in low-resource settings lack basic phone skills that designers take for granted src-020 pp57, 59. Some users do not realise they are talking to an AI rather than a person src-013 p11.

Why it matters for AI tools

Users who do not know AI can be wrong may trust a chatbot too much, think less critically and make poor decisions src-013 p11. This matters most where users have few other ways to check an answer src-028 p61. Most AI tools need digital and AI literacy that low-income and marginalised groups may lack. Measuring the gap at the start shows whether AI is the right approach, or whether simpler technology or a non-digital option would work better src-028 p15.

Low literacy also wastes what the tool offers. Digital Green lists a failure where a tool's advanced features go unused because users cannot yet see how to benefit from them src-003 p7. Users are seldom taught how to write prompts or how AI tools work src-012 p8.

What good evidence looks like

• The users' digital skills, measured with questions built for phone users, not computer users src-020 pp9-12; src-023 pp1, 3.
• Whether users can do key tasks alone or only with help src-021 pp3-4.
• What users understand about AI: that it can be wrong, that they may be talking to a machine, and what happens to what they share src-013 p11; src-028 p63.
• Whether the words used to explain the tool make sense to users. In India, many respondents did not know words like "hacking", "personal data" or "privacy policy" src-019 p36.
• A plan for AI literacy if gaps exist: what to teach, how often, who delivers it, and the time and budget for it src-028 pp61-64.
• How the tool explains its outputs, tested with the users, for example community health workers src-028 p44.

How to find out

Survey questions on digital skills designed for mobile-first users in low- and middle-income countries; the EDiT guide offers a question bank, tested or in draft, across information, communication, content creation, safety and problem solving src-020 pp9-12. Separate response options for doing a task alone and doing it only with help src-021 pp3-4. The EDiT guide also sets out how to observe people doing tasks on the phone they use most, after a short check that the phone works src-020 pp18, 65-66. Cognitive interviews (testing how people understand each question) on the words used to describe AI, data and risk src-019 pp36, 39. Participatory discussion of existing practices and skills, as a starting point for design src-039 p39. Testing the tool's explanations with users before launch src-028 p44.

Limits and disagreements

The evidence on AI literacy itself is thin. The EDiT guides measure general digital skills and include no questions on AI src-020 pp7-9. The AI-specific guidance comes mostly from one Indian playbook src-028 pp15, 44, 61-64, and its one study on AI explanations involved a simulated tool src-028 p44. Girl Effect asks teams to offer AI literacy content even just to test interest, which suggests little is known about what users want src-012 p24.

Four sources come from one consortium, EDiT at the University of Cape Town (src-019, src-020, src-021, src-023), tested in India, Kenya and Nigeria. Their findings on skills come from cognitive testing, not a representative survey src-020 pp57, 59.

Sources differ on what literacy is for. The Digital Futures Lab playbook frames critical AI literacy as building agency and challenging power, with questions such as who benefits src-028 p61. Girl Effect and IREX frame it more as safe and responsible use src-012 p20; src-013 p11. Both views can shape a funder's ask.

The Plan report on judging what is true online predates AI and draws on a blog by Linda Raftree, MTI's founder src-039 p39.

Practitioner detail

In cognitive testing in low-literacy settings, many people could not save or edit contacts, so depended on others to make calls. Some could install apps but not delete files, and ran out of storage. Many joined WiFi networks someone else had set up src-020 pp57, 59. People with phone problems often ask family, friends or a mobile shop src-020 p51.

For AI literacy programmes in low-resource settings, the playbook reports that short, regular sessions run by community peer educators over a long period work best src-028 p62. It sets honesty about risks and likely errors as the minimum, and advises plain language without jargon src-028 p63. Explaining to users how their data will be used to train the system can build trust src-028 p63.

A cited study of community health workers in rural India found they struggled to understand simplified AI explanations for newborn jaundice but still wanted them. Suggested fixes included less text and local examples and analogies src-028 p44.