Support Role-Based AI Training With D2L Brightspace™
D2L Brightspace helps you deliver role-specific training, assess practical skills and track employee progress.
Generative AI training equips employees to use AI tools effectively and responsibly in their day-to-day work, but a lack of relevant skills remains a barrier to adoption. An analysis by the Organisation for Economic Co-operation and Development found that more than half of small and medium-sized enterprises not yet using generative AI cited insufficient skills as a reason for not adopting these tools.
Addressing this skills shortage takes more than a one-size-fits-all course. You need to decide which skills your employees need for their roles, how they’ll practice them and how to determine whether those skills improve workplace performance.
This guide shows you how to build AI training for employees around role-specific competencies, practical activities and meaningful assessments, then measure results and choose a learning management system (LMS) that supports your training goals.
What Should Generative AI Training for Employees Cover?
As employees use generative AI to draft reports or summarize documents, they need to assess whether the information these tools produce is accurate and appropriate for the task.
Without adequate training, employees may include inaccurate AI-generated information in their work or share confidential company documents with unapproved AI tools. These mistakes can undermine work quality and create data security risks.
Your AI training program should therefore teach employees how to verify AI-generated content and recognize when human judgment is necessary. It should also explain your organization’s policies on approved AI tools and information sharing. Employees need to understand how to identify potentially biased outputs before using them.
Developing AI fluency in the workplace doesn’t necessarily require advanced technical training. Research by Green and Lamby, cited in a report by the OECD, suggests that fewer than 1% of workers need advanced AI-specific skills, such as programming or model development.
Instead, the report highlights that most workers need broader digital skills and the ability to work with and interpret data. These findings support a practical approach to generative AI training that employees can apply to their daily responsibilities.
Adapted from the U.S. Department of Labor’s AI Literacy Framework, the following five competencies outline what employees should learn and be able to demonstrate after training:
| Core Competency | What Employees Should Learn | Expected Learning Outcome |
| Generative AI fundamentals | How generative AI tools and large language models (LLMs) work, their capabilities and limitations | Identify appropriate workplace applications and recognize tasks that require human judgment |
| Prompt engineering | How to provide clear instructions, relevant context and constraints when prompting AI tools | Create and refine prompts that produce outputs aligned with task requirements |
| Output evaluation | How to check AI-generated information for accuracy, relevance and unsupported claims | Identify errors and verify outputs before using them |
| Responsible AI use | How to handle sensitive information, recognize bias and follow organizational AI policies | Identify potential privacy, security and ethical risks when using AI |
| Workplace applications | How to apply generative AI to role-specific tasks and existing workflows | Complete an approved workplace task using AI and evaluate the result |
Although they provide a common foundation for training, how employees apply them will depend on their responsibilities.
Your training activities and expected learning outcomes should therefore align with employees’ day-to-day responsibilities and help them use AI safely and effectively.
How to Build a Generative AI Training Program for Employees
Building a corporate AI training program requires a clear process for identifying role-specific skills, developing practical learning experiences and measuring what employees can do after training.
The five steps below provide a structured approach to workplace AI training that you can adapt whether you’re launching your first initiative or improving an existing program.
Step 1. Identify AI Skills Gaps Across Employee Roles
Before deciding what to teach, you need to understand how employees currently use generative AI and where additional skills could improve their work.
Take your HR department, for example. Your HR administrator might only need basic AI skills to organize routine employee records. Your talent recruiter, on the other hand, may need stronger prompt engineering and output evaluation skills to assess AI-assisted candidate screening.
Although they are part of the same department, each person’s responsibilities create different training needs.
To identify your organization’s AI skills gap, review employees’ daily tasks and prioritize opportunities for AI assistance based on business value and suitability.
You’ll also need a clear picture of employees’ current proficiency. Start with a short skills survey asking which AI tools they use, how frequently they use them and where they encounter difficulties. Follow this with practical assessments that require employees to demonstrate their skills on relevant tasks.
The findings will reveal where employees need additional support, allowing you to group them by shared responsibilities and develop AI skills training that addresses those gaps.
Step 2. Set Role-Specific Learning Objectives
Once you’ve identified where employees need additional support, define what they should be able to accomplish after training.
Your learning objectives should reflect their responsibilities and describe actions you can assess, rather than broad goals such as “becoming proficient in AI.”
For example, if you’re training your customer support team to handle inquiries with AI, you could set the following learning objectives:
- Generate: Draft a response to a customer inquiry using an approved AI tool.
- Evaluate: Identify inaccurate information or misleading claims in the AI-generated response.
- Verify: Check product details and proposed solutions against approved company resources.
- Revise: Correct errors and produce a response that addresses the customer’s issue while meeting company communication standards.
With these objectives in place, you can assess employees against specific performance standards rather than course completion alone.
Step 3. Develop Hands-On Training Activities and Assessments
Even well-defined learning objectives won’t produce meaningful results if employees spend their training watching generic demonstrations and completing generic quizzes.
They need opportunities to practice what they’re expected to learn and demonstrate how they’d apply those skills at work through realistic scenarios drawn from their day-to-day responsibilities.
Those scenarios should also account for an important part of AI-assisted work: overseeing what the tools produce.
A Brookings analysis of 25,000 Danish workers across 11 occupations highly exposed to AI found that approximately one-third of new AI-related workplace tasks involved overseeing AI, including reviewing outputs for accuracy and handling ethics and compliance responsibilities.
To prepare employees for these tasks, design exercises that go beyond generating AI outputs. Employees should have to identify errors, assess potential risks and make appropriate corrections before using the results in their work.
Here’s what that could look like in practice:
| Employee Role | Learning Objective | Hands-On Activity | Skills Assessment |
| Sales representative | Create accurate, personalized prospect outreach | Use an approved AI tool to draft an email based on a fictional prospect’s needs and company product information | Assess prompt clarity, relevance to the prospect and accuracy of product claims |
| Customer support representative | Evaluate and improve AI-generated replies to customer inquiries | Review an AI-generated reply containing incorrect product information and revise it using approved resources | Identify inaccuracies, verify product details and ensure the revised response follows company communication and AI policies |
Step 4. Choose Training Formats and Plan the Rollout
Imagine introducing a lengthy, instructor-led program during your organization’s busiest sales period, with mandatory sessions that employees struggle to fit around their responsibilities.
They may miss sessions, rush through activities or struggle to give the training the attention it requires.
Time is already a challenge for AI skills development. In a survey of nearly 1,300 workers globally, The Conference Board found that only 48% agreed their organizations provided sufficient time during work hours to develop AI skills.
This means more than half of respondents could not affirm that their organizations provided enough time for AI skills development.
Avoid making training another responsibility employees have to squeeze into their workday. Set aside dedicated learning time, provide access to approved AI tools and work with managers to ensure employees can participate without falling behind on their regular responsibilities.
The delivery format should also reflect the skills being taught. Self-paced AI eLearning offers flexibility for foundational concepts, while instructor-led workshops allow for discussion and immediate feedback. More complex tasks may benefit from supervised practice.
Before expanding the program across teams, pilot it with a small group. Gather feedback on scheduling, training activities and tool access, then use those insights to address potential challenges before the wider rollout.
Step 5. Measure Skills Development and Training Effectiveness
Seeing high completion rates after rolling out your AI training program won’t tell you whether your employees have developed the skills they need. Instead of relying on completion rates alone, evaluate their progress by:
- Comparing assessment results: Use the same competency criteria discussed earlier before and after training to identify improvements in assessment scores and demonstrated AI skills
- Measuring workplace performance: Track whether employees complete AI-assisted tasks faster, make fewer errors or achieve measurable improvements in AI productivity.
- Collecting feedback: Ask employees how well the training prepared them for their responsibilities and ask managers what changes they’ve observed in employees’ performance.
These findings will help you distinguish what employees learned during training from how effectively they apply those skills at work. Review the results against your original learning objectives to identify remaining skill gaps.
How to Choose an LMS for Generative AI Training
Now you’ve identified the AI skills your employees need, designed practical training activities and established how you’ll measure their progress. The next question is whether your existing learning management system (LMS)—if you have one—can support all of these activities.
To find out, review your current or prospective LMS against the learning objectives, training activities and assessment requirements you’ve established. Consider whether it can accommodate different employee needs, offer AI-enabled features, support hands-on learning and provide meaningful insights into skills development.
You may need a new LMS if:
- Learning paths are too rigid: You cannot tailor training to employees’ roles, responsibilities or existing AI skills.
- Assessment options are limited: You cannot evaluate how employees apply AI skills through practical exercises and role-specific tasks.
- Reporting lacks depth: You can track course completion but not competency development or training effectiveness.
- Integrations are inadequate: The platform cannot connect effectively with your HR systems or support training across departments.
- Content updates are difficult: Creating and revising training materials takes too much effort as AI tools, business applications and AI governance requirements evolve.
If your existing LMS meets these requirements, you may only need to update your training content and delivery approach.
How Brightspace Supports Generative AI Training for Employees
Brightspace helps you bring the different elements of your generative AI training program together in one learning platform ecosystem.
With the Brightspace Core package, you can deliver role-based learning experiences, manage training activities, and assess employees against defined learning outcomes using assignments, quizzes and rubrics.
As your training needs evolve, other products within the Brightspace ecosystem can help you develop and manage more engaging learning experiences.
D2L Lumi uses AI-powered capabilities to support content creation, assessment development and feedback. D2L Creator+ allows you to build interactive activities where employees can practice applying AI skills to realistic workplace scenarios.
Beyond training delivery, you need visibility into what employees are learning.
D2L Performance+ provides advanced analytics to monitor engagement, assessment performance and training effectiveness, while D2L Link connects Brightspace with your existing HR and business systems to support training administration and data exchange.
Together, these capabilities help you deliver relevant generative AI training, evaluate demonstrated competencies and make informed improvements based on employee progress.
Explore Brightspace for Corporate Training
See how Brightspace helps organizations deliver role-based generative AI training, develop employee skills and measure learning outcomes.
Frequently Asked Questions About Generative AI Training
How long does it take to train employees to use generative AI?
Generative AI training duration depends on employees’ existing skills, job responsibilities and the complexity of the tasks they need to perform. Your employee AI training schedule should allow time for foundational learning, practical application and ongoing AI skills development.
How much does generative AI training for employee cost?
Generative AI training costs vary based on employee numbers, delivery formats, content development and technology requirements. Your employee training budget should account for these expenses alongside instructor fees and employee time when comparing corporate AI training programs.
How often should companies update their generative AI training programs?
Review your training program regularly and whenever new AI tools, AI governance policies or workplace requirements change. These generative AI training updates support continuous AI learning and ensure employee upskilling remains relevant.
Should all employees receive the same generative AI training?
No, while all employees need foundational generative AI skills, their training activities should reflect their job responsibilities and existing proficiency. Role-based AI training allows you to create personalized learning paths that make employee AI training relevant to daily tasks.
Should companies build generative AI training in-house or use external providers?
In-house AI training works well for organization-specific workflows and policies, while external AI training providers can supply specialized expertise and ready-made AI training resources. Corporate AI training programs can combine both approaches based on available expertise, budget and learning objectives.
Do employees need technical skills before starting generative AI training?
No, coding and advanced technical skills are not standard generative AI training prerequisites for nontechnical employees. AI training for beginners should focus on foundational AI literacy, practical prompting, output evaluation and responsible AI use.
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