Effective Work Samples for Hiring AI Training Program Developers

Developing AI training programs for non-technical staff has become a critical function in organizations embracing artificial intelligence. The right training developer can bridge the knowledge gap between complex AI technologies and everyday users, empowering your workforce to leverage AI tools effectively. However, identifying candidates who can translate technical concepts into accessible learning experiences requires more than reviewing resumes and conducting standard interviews.

Work samples provide tangible evidence of a candidate's ability to design, develop, and deliver effective AI training programs. By observing candidates perform realistic job tasks, you gain insight into their instructional design approach, communication skills, and ability to make complex concepts understandable to non-technical audiences. These practical demonstrations reveal how candidates think on their feet, respond to feedback, and balance technical accuracy with accessibility.

The most effective AI training program developers combine technical knowledge with strong teaching abilities. They understand both the capabilities of AI systems and the learning needs of adult professionals without technical backgrounds. Through carefully designed work samples, you can assess whether candidates possess this unique blend of skills before making a hiring decision.

The following four activities simulate key responsibilities of an AI training program developer. Each exercise targets specific competencies essential for success in this role, from curriculum planning to content creation and delivery. By incorporating these work samples into your interview process, you'll gain valuable insights that traditional interviews simply cannot provide.

Activity #1: Training Needs Analysis

This activity assesses the candidate's ability to identify knowledge gaps and design appropriate learning objectives for non-technical staff. A strong AI training program developer must understand both what employees need to know and how to structure learning to address those needs effectively.

Directions for the Company:

• Provide the candidate with a fictional scenario about a department (e.g., marketing, HR, customer service) that needs to implement a specific AI tool (e.g., content generation, data analysis, chatbot).
• Include basic information about the department's current technical proficiency, business goals, and the AI tool's capabilities.
• Allow 30-45 minutes for the candidate to complete the analysis.
• Provide access to a computer with standard office software or a template document.

Directions for the Candidate:

• Review the scenario information provided.
• Create a training needs analysis document that identifies:
• Key knowledge and skill gaps for the department
• Learning objectives for the training program
• Recommended training formats and approaches
• Potential challenges and mitigation strategies
• Success metrics for measuring training effectiveness
• Be prepared to explain your reasoning and approach.

Feedback Mechanism:

• After reviewing the analysis, provide feedback on one strength (e.g., "Your learning objectives are well-aligned with business goals") and one area for improvement (e.g., "Consider how you might address varying levels of technical comfort among staff").
• Ask the candidate to revise one section of their analysis based on your feedback, allowing 10-15 minutes for this adjustment.
• Observe how receptively they incorporate feedback and whether their revisions effectively address the concern.

Activity #2: Explaining AI Concepts Simply

This exercise evaluates the candidate's ability to translate complex AI concepts into language and examples that resonate with non-technical staff. Effective training developers must bridge the gap between technical capabilities and practical applications without overwhelming learners.

Directions for the Company:

• Select 3-5 AI concepts that are relevant to your organization but potentially confusing to non-technical staff (e.g., machine learning, natural language processing, neural networks, training data, model bias).
• Provide the candidate with this list 15 minutes before the exercise.
• Have a team member role-play as a confused employee during the explanation.

Directions for the Candidate:

• Prepare brief, clear explanations of each AI concept that would make sense to someone without technical background.
• For each concept:
• Provide a simple definition
• Use a real-world analogy or metaphor
• Give a practical example of how it applies in the workplace
• Explain why understanding this concept matters to the employee
• Deliver your explanations conversationally, as if speaking to an actual employee.
• Be prepared to answer follow-up questions from the "employee."

Feedback Mechanism:

• The person role-playing the employee should provide feedback on which explanations were clearest and which were still confusing.
• Ask the candidate to re-explain one concept that was identified as confusing, incorporating the feedback.
• Evaluate their ability to adjust their communication approach based on learner needs.

Activity #3: Micro-Learning Module Design

This activity assesses the candidate's instructional design skills and ability to create engaging, bite-sized learning experiences. Effective AI training often requires breaking complex topics into manageable chunks that busy professionals can easily digest.

Directions for the Company:

• Provide a specific AI tool or feature that employees need to learn (e.g., using an AI writing assistant, interpreting AI-generated analytics, or prompt engineering basics).
• Supply relevant documentation about the tool's functionality.
• Provide access to presentation software, document creation tools, or other content development resources.
• Allow 45-60 minutes for the candidate to complete the design.

Directions for the Candidate:

• Design a 5-10 minute micro-learning module that teaches one specific skill related to the AI tool.
• Your module should include:
• A clear learning objective
• A brief introduction explaining why this skill matters
• Step-by-step instructions with visuals (screenshots, diagrams, etc.)
• A simple practice activity or knowledge check
• Key takeaways or a quick reference guide
• Focus on making the content engaging, accessible, and immediately applicable.
• Be prepared to explain your design choices and how this module would fit into a broader training program.

Feedback Mechanism:

• Provide feedback on the module's clarity, engagement level, and instructional approach.
• Suggest one specific improvement that would make the module more effective for non-technical learners.
• Give the candidate 15 minutes to implement this change and explain how it enhances the learning experience.

Activity #4: Training Program Roadmap Development

This exercise evaluates the candidate's ability to plan a comprehensive AI training program that progresses logically and builds competence over time. It tests strategic thinking, project management, and understanding of adult learning principles.

Directions for the Company:

• Create a scenario describing an organization-wide AI implementation (e.g., introducing multiple AI tools across departments, or a major AI platform adoption).
• Provide information about:
• The organization's size and structure
• Current technical proficiency levels
• Business goals for the AI implementation
• Timeline constraints
• Available training resources and formats
• Allow 60 minutes for the candidate to develop their roadmap.

Directions for the Candidate:

• Develop a comprehensive training program roadmap that spans 3-6 months.
• Your roadmap should include:
• A phased approach with clear milestones
• Different learning tracks for various roles or proficiency levels
• A mix of training formats (e.g., workshops, e-learning, coaching)
• Required resources and potential constraints
• Evaluation methods to measure program effectiveness
• Strategies for maintaining and refreshing knowledge over time
• Create a visual representation of your roadmap (timeline, flowchart, etc.)
• Prepare to explain your rationale for sequencing, format choices, and resource allocation.

Feedback Mechanism:

• Provide feedback on the roadmap's comprehensiveness, practicality, and alignment with organizational needs.
• Introduce a new constraint or challenge (e.g., "The timeline has been shortened by a month" or "The budget has been reduced by 30%").
• Ask the candidate to adjust their roadmap to accommodate this change, allowing 15-20 minutes for revisions.
• Evaluate their flexibility, problem-solving approach, and ability to maintain program quality despite constraints.

Frequently Asked Questions

How long should we allocate for these work samples in our interview process?

Each activity requires 45-90 minutes including setup, execution, feedback, and revision time. Consider spreading them across multiple interview stages or selecting the 1-2 most relevant to your specific needs. For senior roles, you might conduct all four exercises over a half-day assessment.

Should we provide these exercises in advance or as on-the-spot challenges?

Activities #1 and #4 (Training Needs Analysis and Program Roadmap) benefit from advance notice (24-48 hours), allowing candidates to prepare thoughtfully. Activities #2 and #3 (Explaining Concepts and Micro-Learning Design) work better as supervised exercises with limited preparation time to assess how candidates think on their feet.

How should we evaluate candidates who have strong instructional design skills but limited AI knowledge?

Focus on their learning approach and ability to quickly grasp new concepts. Consider providing basic AI information before the exercises and evaluate how effectively they translate what they learn. Strong instructional designers can often learn technical content, but the reverse is less common.

Can these exercises be conducted remotely?

Yes, all four activities can be adapted for remote interviews using video conferencing and collaborative tools like Google Docs, Miro, or presentation software. For remote sessions, provide clearer written instructions and consider extending time allowances slightly to account for technology transitions.

How do we ensure these work samples don't disadvantage candidates from underrepresented groups?

Standardize your evaluation criteria before conducting interviews and apply them consistently. Provide equal resources and preparation time to all candidates. Consider having diverse evaluators review the work samples, and be mindful of how cultural differences might influence communication styles or approaches.

Should we compensate candidates for completing these work samples?

For extensive work samples (particularly if combining multiple activities), consider offering compensation, especially for senior roles or if candidates must complete the work on their own time. This demonstrates respect for candidates' expertise and time while potentially attracting a more diverse candidate pool.

Incorporating these work samples into your hiring process for AI training program developers will significantly improve your ability to identify candidates who can effectively bridge the gap between complex AI technologies and your non-technical staff. By observing candidates perform realistic job tasks and respond to feedback, you'll gain valuable insights into their instructional approach, communication skills, and ability to make AI accessible to all employees.

Remember that the best AI training developers combine technical understanding with strong teaching abilities and empathy for learners. These work samples are designed to reveal that unique combination of skills that traditional interviews often miss.

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