AI Literacy
The AI Literacy course teaches non-technical users how AI and large language models actually work, how to evaluate and critically review AI-generated content, and how to use AI tools responsibly—covering capabilities, hallucinations, bias, privacy, copyright, and societal impacts.
Who Should Take This
It is ideal for professionals, students, and everyday technology users who want to become informed, confident consumers of AI tools without needing a technical background. Learners should expect to develop practical judgment about when to trust AI, how to protect their data, and how to participate in conversations about AI's impact on society.
What's Included in AccelaStudy® AI
Adaptive Knowledge Graph
Practice Questions
Lesson Modules
Console Simulator Labs
Exam Tips & Strategy
13 Activity Formats
Course Outline
1How LLMs Work 8 topics
Describe what a large language model is, including the idea that it predicts likely next words based on patterns learned from vast amounts of text data, without storing facts or rules explicitly
Describe what tokens are and why they matter, including how LLMs break text into chunks and why this affects how the model reads input and generates output
Describe the concept of a context window and explain why a model can only see a limited amount of text at once, including practical consequences such as forgetting earlier parts of a long conversation
Explain how training data shapes model behavior, including why models may reflect outdated information, cultural biases, or gaps in coverage for niche topics
Apply an understanding of how LLMs work to explain to a non-technical colleague why a chatbot answered a question confidently but incorrectly
Distinguish between AI chatbots, AI copilots, and AI agents in terms of autonomy, task scope, and how they interact with external tools and data sources
Describe the difference between generative AI, discriminative AI, and classical machine learning, explaining how each type makes decisions and giving everyday examples of products that use each approach
Describe the concept of model updates and knowledge cutoffs, explaining why an AI chatbot may not know about recent events, how retrieval-augmented AI tools address this limitation, and why models can still hallucinate even with web search enabled
2Capabilities and Limitations 7 topics
Describe common tasks where AI tools excel, including drafting text, summarizing documents, answering general questions, translating languages, and generating creative content
Describe common limitations of current AI tools, including difficulty with precise arithmetic, real-time information, multi-step logical reasoning, and tasks requiring verified authoritative sources
Apply knowledge of AI capabilities and limitations to decide whether a given task is a good candidate for AI assistance or requires a human expert, citing specific reasons for the decision
Explain what hallucinations are in AI systems, including why models generate plausible-sounding but factually wrong information and why this happens even when the model appears confident
Apply strategies to detect potential hallucinations in AI output, including cross-referencing with authoritative sources, checking for internal consistency, and identifying suspiciously specific but unverifiable claims
Analyze when AI confidence should increase or decrease your trust in an output, explaining why a model's tone of certainty does not reliably correlate with factual accuracy
Apply knowledge of AI limitations to identify tasks in your daily workflow where AI assistance would be risky without human verification, providing concrete examples from professional or personal contexts
3Evaluating AI Outputs 6 topics
Apply a practical checklist to evaluate whether an AI-generated response is accurate, relevant, and complete, including checking for outdated facts, missing context, and misunderstood intent
Apply source verification techniques to AI-generated factual claims, including identifying the type of claim, finding authoritative primary sources, and distinguishing credible from unreliable verification sources
Apply techniques to improve AI output quality by refining your request, providing more context, or asking the model to reconsider a response when the initial output is unsatisfactory
Analyze the quality difference between AI outputs for different task types, explaining why AI performs more reliably on some tasks (summarizing, drafting) than others (precise facts, complex reasoning)
Apply an appropriate level of scrutiny when reviewing AI-generated content for different stakes levels, distinguishing between low-stakes uses (personal brainstorming) and high-stakes uses (medical, legal, or financial decisions)
Analyze how the framing of a prompt influences the kind of output produced, explaining why the same underlying question can yield very different quality answers depending on how it is worded
4Bias and Fairness in AI 7 topics
Describe what bias in AI means, including how training data reflects historical inequalities and cultural assumptions that can be amplified when a model is deployed at scale
Identify common types of AI bias, including representational bias (underrepresentation of groups in training data), stereotyping bias, and measurement bias in how outcomes are labeled
Apply critical evaluation to AI-generated descriptions, images, or recommendations to identify whether the output may reflect or reinforce biases about demographic groups
Analyze the societal risk when biased AI systems are used in high-stakes decisions such as hiring, lending, healthcare triage, or criminal justice, citing real-world examples of documented harms
Apply awareness of AI bias to your own use of AI tools, including seeking diverse perspectives, questioning outputs about social groups, and not over-relying on AI judgments about people
Apply bias-aware practices when using AI tools for tasks involving people, including hiring screening, student grading assistance, and performance review drafting, by maintaining human accountability for all people-related decisions
Analyze the difference between AI bias as a technical problem (imbalanced training data) and as a systemic problem (biased labels, biased deployment decisions), explaining why purely technical fixes are insufficient without addressing social context
5Copyright, Attribution, and Ownership 5 topics
Describe the unresolved copyright questions surrounding AI-generated content, including whether AI output can be protected by copyright, who owns it, and how training data licensing affects these questions
Apply appropriate attribution practices when using AI-generated text, images, or code in professional contexts, including acknowledging AI involvement and following organizational or publication policies
Identify the risk of AI regenerating near-verbatim copyrighted text from training data and explain steps to detect and avoid inadvertently reproducing protected material
Analyze how policies around AI content use differ across industries (journalism, academia, creative work) and evaluate what responsible disclosure of AI assistance looks like in each context
Apply an understanding of AI and intellectual property to make an informed decision about whether a specific piece of AI-generated content requires attribution, licensing review, or avoidance
6Privacy and Data Risks with AI Tools 5 topics
Describe what happens to data you submit to AI tools, including how different providers store, use for training, or share conversation data and why this matters for sensitive information
Apply data minimization principles when using AI tools, including avoiding submitting personally identifiable information, confidential business data, passwords, or sensitive health information into public AI chat interfaces
Identify the privacy settings and opt-out options available in major AI tools such as ChatGPT, Copilot, and Gemini, including memory features, training opt-outs, and enterprise tier differences
Analyze the privacy risk difference between using consumer AI tools and enterprise AI deployments, explaining how data handling, retention, and training policies typically differ between the two
Apply a practical decision framework to determine whether it is safe to use a public AI tool for a specific task, based on the sensitivity of the information and the provider's data policy
7Cost, Latency, and Model Selection 6 topics
Describe how AI services are typically priced, including subscription tiers, pay-per-use token pricing, and the tradeoff between cost and capability across consumer and API-level products
Explain why larger and more capable AI models are slower and more expensive than smaller ones, and describe scenarios where using a lighter-weight model is the better practical choice
Apply cost-awareness when choosing between free, paid, and enterprise AI tools by matching the tool's capability level to the complexity of your task and the sensitivity of your data
Analyze the hidden costs of AI adoption beyond subscription fees, including time spent verifying outputs, potential reputational risk from errors, and the organizational cost of over-reliance on AI
Describe the key differences in cost and capability between leading AI products including free tiers (ChatGPT Free, Gemini Free), subscription tiers (ChatGPT Plus, Claude Pro), and enterprise API access, and explain what each tier is suited for
Apply a practical evaluation framework to select the right AI tool for a given task, considering accuracy requirements, cost, privacy constraints, and whether the task requires a specialized tool (image generation, coding, search-augmented answers)
8Basic Prompting for Non-Developers 5 topics
Apply fundamental prompt-writing principles to get better results from AI tools, including being specific about the task, providing relevant context, stating the desired output format, and specifying the intended audience
Apply iterative refinement to an AI conversation by rephrasing a poorly worded request, adding missing context, and asking follow-up questions to guide the model toward a more useful response
Identify common prompting mistakes made by new AI users, including vague instructions, omitting context, not specifying tone or length, and expecting the AI to read between the lines
Apply role-based prompting to improve AI output quality in everyday tasks such as asking the AI to respond as a plain-language explainer, a devil's advocate, or a subject-matter expert in a specific field
Analyze the difference between asking an AI to generate content from scratch versus asking it to review or improve existing human-written content, and explain when each approach produces better outcomes
9AI in Daily Workflows 5 topics
Apply AI tools to common personal productivity tasks such as drafting emails, summarizing long documents, brainstorming ideas, and creating to-do lists, identifying which tasks benefit most from AI assistance
Apply AI tools to common workplace tasks such as generating meeting summaries, drafting slide outlines, researching unfamiliar topics, and translating documents, while maintaining human review of outputs
Identify ethical boundaries when using AI in workplace contexts, including not presenting AI-generated work as entirely your own without disclosure, and following your organization's AI use policy
Analyze the risk of over-relying on AI in professional workflows, including skill atrophy, loss of critical thinking, and the danger of distributing AI errors at scale without adequate human review
Apply a human-in-the-loop mindset to AI-assisted workflows by establishing personal checkpoints for verification, setting quality standards before accepting AI output, and maintaining accountability for final decisions
10Societal and Ethical Impacts of AI 8 topics
Describe the major societal concerns associated with widespread AI adoption, including job displacement fears, misinformation at scale, privacy erosion, and concentration of AI power among a small number of companies
Explain the concept of AI-generated misinformation and deepfakes, including how synthetic media is created, why it is difficult to detect, and its potential impact on elections, journalism, and personal reputation
Apply media literacy skills to evaluate whether a piece of content (text, image, audio, video) may have been generated or manipulated by AI, including checking for artifacts, inconsistencies, and provenance signals
Describe the role of AI regulation and governance efforts globally, including EU AI Act categories, voluntary commitments by AI developers, and emerging norms around transparency and accountability
Analyze the tension between AI innovation speed and the pace of regulatory and ethical safeguards, and explain why individuals, not just institutions, have a role in shaping responsible AI norms
Apply an informed perspective to evaluate claims about AI capabilities in news articles, social media posts, and product marketing, distinguishing realistic assessments from hype or fear-mongering
Describe what is meant by artificial general intelligence (AGI) versus the narrow AI in use today, explain the spectrum of expert opinion on AGI timelines, and apply critical reasoning to evaluate AGI-related claims in media reporting
Apply an informed perspective to conversations about AI's impact on employment, explaining what research suggests about job displacement versus job creation, augmentation versus replacement, and which occupational categories face the most disruption
Scope
Included Topics
- How LLMs work at a high level (tokens, context windows, training data, next-token prediction), AI capabilities and limitations, hallucinations and confabulation, evaluating AI-generated output, when to trust vs. verify AI responses, types of bias in AI systems, copyright and attribution issues with AI-generated content, privacy and data risks when using AI tools, cost and latency tradeoffs across AI products, AI agents vs. chatbots vs. copilots, basic prompt strategies for non-developers, integrating AI into daily personal and professional workflows, societal and ethical impacts of AI
Not Covered
- Prompt engineering for developers (covered in Prompt Engineering domain)
- LLM training, fine-tuning, RLHF, or model architecture internals (covered in Deep Learning and NLP domains)
- Writing or evaluating code with AI assistance
- Enterprise AI deployment, MLOps, or API integration
- Adversarial machine learning and security-focused attacks on AI systems