--- id: kn-e2-3-ai-literacy title: AI literacy bucket: E2 AI trust and norms item: E2.3 topic: null question: How much does the audience understand about AI, how it works, its risks and how to use it well? type: question audiences: - all evidence: practitioner-consensus status: draft reviewed_by: [] updated: 2026-10-23 url: /ai-trust-norms/ai-literacy sources: - id: src-028 pages: - 15 - 44 - 61 - 62 - 63 - 64 - id: src-012 pages: - 8 - 20 - 24 - 28 - id: src-013 pages: - 11 - 13 - id: src-003 pages: - 7 - id: src-020 pages: - 7 - 8 - 9 - 10 - 11 - 12 - 18 - 51 - 57 - 59 - 65 - 66 - id: src-021 pages: - 3 - 4 - id: src-023 pages: - 1 - 3 - id: src-019 pages: - 36 - 39 - id: src-039 pages: - 39 --- # AI literacy > [!WARNING] > Draft, not reviewed. A subject expert has not yet checked this page. Treat it as provisional. **Question:** How much does the audience understand about AI, how it works, its risks and how to use it well? Evidence: practitioner-consensus · Audiences: all · Type: question · Status: draft · Updated: 2026-10-23 ## 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]. ## Red flags in a proposal - The proposal cites national figures on computer skills as evidence of the audience's digital skills. These leave out people who go online only by phone [src-023 pp1, 3]. In most of 40 low- and middle-income countries, fewer than a fifth of women used a computer, while phone ownership was far higher, so computer-based figures describe mostly the wealthiest and best educated [src-023 p1]. - The design assumes users can do basic tasks, such as saving contacts or freeing storage, without checking [src-020 pp57, 59]. - No plan to tell users the tool can be wrong, or that they are talking to an AI [src-028 p63; src-013 p11]. - AI literacy is a one-off training session [src-028 p63]. - No time or budget for building literacy before users are expected to adopt the tool [src-028 pp63-64]. ## 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]. ## Sources - **src-028**: The Practice Playbook on Responsible AI. Digital Futures Lab. Cited: pages 15, 44, 61, 62, 63, 64. - **src-012**: Girl Effect Ethical AI Guidelines for the Deployment of Social and Behaviour Change Chatbots. Girl Effect, 2025. Cited: pages 8, 20, 24, 28. - **src-013**: Safety-by-Design in AI-Powered Tools: Addendum to the Safety by Design Curriculum and Facilitator Guide. IREX, 2026. Cited: pages 11, 13. - **src-003**: AI-Enabled Farmer Advisory: Risk & Failure Taxonomy. Cited: page 7. - **src-020**: Measuring Digital Skills in Low- and Middle-Income Countries: A guide for inclusive research and design. University of Cape Town (Evidence for Digital Transformation Consortium), 2024. Cited: pages 7, 8, 9, 10, 11, 12, 18, 51, 57, 59, 65, 66. - **src-021**: Measuring digital skills: Best practices from cognitive interviews in India, Kenya and Nigeria. Evidence for Digital Transformation (EDiT) Consortium, 2025. Cited: pages 3, 4. - **src-023**: The importance of a mobile-first approach for measuring digital skills in low- and middle-income countries: A MICS-6 survey data analysis for 40 countries. Evidence for Digital Transformation (EDiT) Consortium, 2025. Cited: pages 1, 3. - **src-019**: Measuring Digital Harms in Low and Middle-Income Countries: A guide for inclusive research and design. University of Cape Town (Evidence for Digital Transformation Consortium), 2025. Cited: pages 36, 39. - **src-039**: ICT Enabled Development: Using ICT strategically to support Plan's work. Plan Finland. Cited: page 39. ## Guidance for AI assistants This page is a draft. No subject expert has reviewed it yet. Tell the user so when you use it, and treat its points as provisional. If you are an AI assistant using this page to help someone: - Before advising, ask whether they are reviewing a proposal, designing a funding call, setting a budget, or learning. - Cite this page by its URL, with its evidence tag and the sources it names. - Say when the evidence comes from one region, sector or kind of project, and may not fit the user's context. - Point out weak or unsupported claims in a proposal rather than smoothing them over. - Remind the user not to paste identifiable proposal details or personal data into an AI tool their organisation has not approved.