Explore Brightspace for AI Literacy Training
See how Brightspace helps organizations build AI literacy skills, deliver role-specific training and measure learning outcomes.
When employees use AI to produce work they cannot confidently evaluate, errors, bias and unreliable information can make their way into everyday decisions.
As AI takes on a greater role in workplace processes, ensuring your team has the skills to use it responsibly becomes increasingly important.
Giving employees access to AI tools or showing them how to write prompts won’t address these risks. You need a structured approach to AI literacy development that helps employees recognize appropriate uses, evaluate outputs and apply sound judgment in their work.
This guide walks you through identifying essential AI literacy competencies, developing role-specific training programs and measuring whether employees can demonstrate what they’ve learned.
What Is AI Literacy?
AI literacy is the knowledge and judgment people need to:
- understand what an AI system can and cannot do
- recognize when it suits a task
- evaluate its output before using it
Like reading or digital literacy, AI literacy is a skill many employees now need, whatever their role. Every team that uses AI needs people to know when to trust its output and when to question it.
The core AI literacies blend skills and mindsets to build this awareness. As employees are increasingly expected to use AI effectively and responsibly, understanding the key pillars of AI literacy can help you identify gaps in their knowledge and determine where training is needed.
Why Is AI Literacy Important in the Workplace?
Over one year, Liu, Lei and Li followed 1,108 employees and found that higher AI literacy predicted greater work engagement 12 months after the initial survey.
The relationship remained after the researchers accounted for employees’ starting engagement levels and how often they used AI. Employees with higher AI literacy felt more capable of handling their tasks, and that confidence accounted for 41% of the statistical relationship between their initial AI literacy and their work engagement 12 months later.
The findings point to five reasons AI literacy is valuable in the workplace:
- greater confidence: employees can approach AI-supported tasks with a stronger understanding of what to do and where to exercise caution.
- stronger engagement: the study links higher AI literacy with greater work engagement, although it doesn’t establish causation.
- better judgment: employees can identify suitable AI applications, evaluate generated information and make informed decisions about its use.
- clearer development priorities: identifying the AI skills gap within each role helps you direct AI skill development where employees need it most
- preparation for changing work: employees can adapt as AI capabilities, workplace applications and expectations for responsible use evolve
The Five Core AI Literacy Competencies
The US Department of Labor’s AI Literacy Framework identifies five competencies employees need across workplace roles and industries:
- understanding AI principles: recognizing how AI works at a basic level, what it can do and where its limitations create a need for human oversight
- exploring AI applications: identifying workplace tasks where AI can provide useful support rather than using it simply because it’s available
- directing AI effectively: giving AI tools clear instructions, relevant context and the information needed to produce useful outputs
- evaluating AI-generated outputs: checking generated information for accuracy, relevance and reliability before applying or sharing it
- using AI responsibly: recognizing privacy, bias and other risks and following organizational policies when using AI
These competencies give you a practical foundation for AI literacy training. They clarify what employees need to understand, practice and apply as they work through role-specific activities.
Examples of AI Literacy in the Workplace
While the five AI literacy competencies provide a foundation, the skills employees need to demonstrate depend on the decisions they make and the risks involved in their work.
An HR professional, for instance, needs to recognize how bias in AI-generated recommendations could affect hiring decisions, while an educator might need to determine whether AI-generated content is accurate and supports students’ learning objectives.
These examples of AI skills in practice show how professionals can work with AI without needing advanced technical expertise.
| Workplace Role | Example of AI Literacy in Practice | Skill Demonstrated |
| Corporate employees | Use AI to summarize documents, then verify the summary against the original source. | Evaluating AI-generated outputs. |
| Managers | Review AI-generated recommendations before using them to inform team decisions. | Critical thinking and AI evaluation. |
| HR professionals | Evaluate AI-assisted recruitment outputs for potential bias and inappropriate use of candidate data. | Ethical AI use and algorithmic bias awareness. |
| Educators | Use AI to draft learning activities, then check their accuracy and alignment with learning objectives. | AI-assisted instructional design. |
| Education administrators | Evaluate AI tools for data privacy, accessibility and suitability before approving their use. | AI governance and AI safety. |
💡Employees should remain responsible for verifying AI outputs and deciding whether they are fit for use.
This is especially important in industries like higher education, where inaccurate AI-generated information or mishandled student data can have serious consequences for teaching, learning and privacy.
How to Build an AI Literacy Training Program
The differences in how employees apply AI literacy across roles should also shape how you train them. Your program needs to account for the AI tools they use, the decisions they’re responsible for and the skills they already have.
The following five steps will help you identify gaps in their capabilities, develop relevant AI literacy training and give employees opportunities to practice what they learn.
Assess Existing AI Literacy Skills
Before designing training, you should establish what employees can do with AI today. A baseline prevents you from building a program around assumptions about either confidence or ability.
An employee may feel comfortable using AI but still struggle to give clear instructions, check an output or recognize when data should not be shared. To get a more reliable picture, combine short assessments, employee surveys and practical exercises.
| 💡 An AI-enabled LMS can help you deliver and assess all three of these tasks for hundreds of employees in a fraction of the time it would take to manage them manually. |
Map the results against an AI competency framework covering AI knowledge, tool use, data literacy and responsible decision-making. This will show where gaps sit across roles and departments, so you can prioritize the training each group needs.
Set Role-Specific Learning Objectives
“Become proficient in AI” is too broad to guide training or measure progress.
Instead, define what employees should be able to demonstrate once they finish each part of your AI curriculum.
For a customer service team, an objective might be to verify an AI-generated customer email for accuracy, tone and policy compliance before sending it.
For educators, it might be: “Evaluate AI-generated lesson materials for accuracy, accessibility and alignment with student learning outcomes.”
Your learning objectives should reflect the AI skills employees need to perform their roles.
Design Hands-On AI Literacy Training Activities
After setting learning objectives, build activities around decisions employees already make at work.
Start with a worked example that shows learners how to approach the task and judge the result. Then give them a similar scenario to complete independently.
For example, after showing your employees how to strengthen a sample prompt or verify a short AI-generated report, ask them to improve a different prompt used in chatbot applications or review a longer report for unsupported claims and confidential information.
Afterward, review their choices and explain where their reasoning was strong or weak. Follow with an assessment that tests the same skill in a new scenario.
| 💡D2L Brightspace™ can support AI eLearning by bringing scenarios, discussions, feedback and assessments together for learners. |
Integrate AI Literacy Into Existing Learning Programs
It can be tempting to create a separate AI literacy program from scratch. However, your employees may benefit more when you integrate AI literacy training into programs they already enjoy.
Here’s why:
1) the skills feel relevant: employees can practice AI use during onboarding, leadership development, role-specific training or teacher professional development.
2) learning builds over time: structured pathways can move employees from foundational concepts to practical tasks, with personalized learning for those who need more support.
3) training stays current: you can update individual activities as your AI policies, approved tools and workplace responsibilities change.
Reinforce AI Literacy Through Ongoing Support
Formal training is only the starting point. Benlian et al. (2026) argue that developing employees’ AI literacy through training isn’t enough to achieve organization-wide adoption. Employees also need shared practices and ongoing support to apply their skills consistently and support AI upskilling.
Useful practices include:
- applying AI skills to real workplace tasks
- sharing experiences and feedback with peers
- using your AI policy to guide decisions and discuss emerging challenges
- updating training as approved tools and organizational requirements change
How to Measure AI Literacy and Training Effectiveness
Course completion tells you who finished the training, not who can use AI well.
A useful AI evaluation should show whether employees understand the five competencies in the AI literacy framework discussed earlier, can apply them to practical tasks and know how to use AI responsibly.
| Assessment Goal | Assessment Method | What the Results Reveal |
| Establish baseline proficiency | Use a short knowledge assessment or practical task before training. | Which AI literacy skills learners already possess and where gaps exist. |
| Assess practical AI skills | Assign tasks such as refining prompts or identifying errors in AI-generated content. | Whether learners can apply AI skills accurately and independently. |
| Evaluate responsible AI use | Present scenarios involving data privacy, bias or inappropriate AI use. | Whether learners can recognize risks and make appropriate decisions. |
| Measure competency development | Compare pre- and post-training assessments using scoring rubrics aligned with learning objectives. | Which competencies learners have developed and which require further training. |
| Evaluate workplace application | Gather manager feedback or review relevant work samples after training. | Whether learners can transfer their AI literacy skills to everyday responsibilities. |
Review these results together to get a complete picture of employees’ progress.
Build AI Literacy Across Your Organization With D2L Brightspace
D2L Brightspace is a learning management system (LMS) that helps L&D teams and corporate organizations deliver, assess and improve AI literacy training across their organizations.
Its ecosystem includes tools for learning design, assessment and reporting, allowing you to:
- create role-specific training: use Brightspace course content, assignments, quizzes and discussions to guide employees and educators through relevant AI literacy activities
- assess demonstrated skills: use the Learning Outcomes tool in Brightspace to connect activities to defined competencies, then assess learner performance with the Rubrics tool
- monitor progress and effectiveness: track learner progress with Brightspace reporting and use D2L Performance+ to analyze engagement and training effectiveness
- develop content more efficiently: use D2L Lumi to help create learning materials and assessment questions aligned with learning outcomes
Together, these capabilities help you develop demonstrable skills rather than promote AI adoption alone.
Frequently Asked Questions About AI Literacy
What Is the Difference Between AI Literacy and Digital Literacy?
Digital literacy covers the skills needed to use technology, find information and work safely online. AI literacy focuses on understanding how AI works, evaluating its outputs and making responsible decisions about when and how to use it.
Do Employees Need Technical or Coding Skills to Become AI Literate?
No, employees don’t need coding skills to become AI literate. However, understanding machine learning basics can help them recognize AI’s capabilities, limitations and risks while developing practical skills in directing AI tools and evaluating their outputs.
How Long Does It Take to Develop AI Literacy Skills?
The time required depends on employees’ existing skills and job responsibilities. Effective AI literacy training combines foundational lessons with role-specific learning pathways and ongoing opportunities to practice as AI tools and workplace expectations evolve.
What Are the Biggest Barriers to AI Literacy in the Workplace?
The biggest barriers include an unclear AI policy, limited opportunities to practice, role-irrelevant training, employee resistance and rapidly changing tools. These challenges can widen the AI skills gap, complicate AI adoption and make ongoing AI upskilling harder to sustain.
How Can Small Businesses Build AI Literacy With Limited Training Resources?
Start with the AI tools your employees already use and focus on practical tasks relevant to their work. You can supplement short team exercises with free AI learning resources, peer discussions and regular feedback to build skills without a large training budget..
Should AI Literacy Training Be Mandatory for All Employees?
Employees who use AI at work should receive foundational training on your organization’s AI policy and responsible AI use. Beyond that, tailor training requirements to each role’s responsibilities, approved tools and potential risks.
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