Effective Work Samples for Evaluating AI Customer Training Personalization Skills

Implementing AI-driven personalization in customer training content represents a significant competitive advantage in today's market. Organizations that effectively leverage AI to customize learning experiences see higher engagement rates, better knowledge retention, and improved customer satisfaction. However, identifying candidates who truly understand how to implement AI personalization strategies requires more than reviewing resumes or conducting standard interviews.

The intersection of artificial intelligence and instructional design demands a unique skill set. Candidates must demonstrate technical proficiency with AI tools and algorithms while also showing deep understanding of learning principles and content development. They need analytical skills to interpret user data and creative abilities to design adaptive learning paths that respond to individual needs.

Work samples and practical exercises provide the most reliable method for evaluating these complex capabilities. By observing candidates as they tackle realistic scenarios, hiring managers can assess both technical knowledge and practical application skills. These exercises reveal how candidates approach problems, adapt to constraints, and balance competing priorities—all critical aspects of successful AI implementation in training contexts.

The following four activities are designed to comprehensively evaluate a candidate's ability to implement AI-driven personalization in customer training content. Each exercise targets different aspects of this specialized skill set, from strategic planning to technical implementation, content adaptation, and data analysis. Together, they provide a holistic view of a candidate's readiness to drive AI personalization initiatives in your organization.

Activity #1: AI Personalization Strategy Development

This exercise evaluates a candidate's ability to develop a comprehensive strategy for implementing AI-driven personalization in customer training content. It reveals their understanding of both the technical requirements and the instructional design considerations necessary for successful implementation. This activity assesses strategic thinking, knowledge of AI capabilities, and awareness of practical implementation challenges.

Directions for the Company:

• Provide the candidate with a brief describing a fictional company that needs to implement AI personalization for its customer training program. Include details about the company's products, current training approach, customer base, and business goals.
• Supply basic information about available data sources (e.g., user behavior data, assessment results, customer profiles) and existing technology infrastructure.
• Allow 45-60 minutes for the candidate to develop their strategy.
• Have a technical team member and a learning/content team member present to evaluate the response.

Directions for the Candidate:

• Review the company brief and develop a strategic plan for implementing AI-driven personalization in their customer training content.
• Your plan should include:

1. Recommended approach to AI personalization (e.g., rule-based, machine learning, hybrid)
2. Key data points to collect and analyze for personalization
3. Content adaptation strategy to support personalization
4. Implementation roadmap with key milestones
5. Success metrics and evaluation approach

• Prepare to present your strategy in a 10-minute presentation followed by 10 minutes of Q&A.
• Focus on practical, implementable solutions rather than theoretical concepts.

Feedback Mechanism:

• After the presentation, provide specific feedback on one strength of the candidate's strategy (e.g., "Your approach to progressive data collection was particularly insightful").
• Offer one area for improvement (e.g., "Consider how you might address privacy concerns with your data collection approach").
• Ask the candidate to spend 5 minutes revising their implementation roadmap based on the feedback.

Activity #2: Personalization Algorithm Design

This exercise tests the candidate's technical understanding of how AI personalization works in practice. It evaluates their ability to design logical rules and algorithms that drive content personalization based on user data and behaviors. This activity reveals the candidate's technical depth, problem-solving skills, and ability to translate learning principles into algorithmic logic.

Directions for the Company:

• Prepare a simplified dataset representing 10-15 fictional users with various attributes (e.g., role, prior knowledge level, learning goals, past module completion, quiz scores).
• Create a content library description with 20-30 training modules of varying difficulty, topics, and formats.
• Provide a whiteboard or digital drawing tool for the candidate to map out their algorithm.
• Allow 45 minutes for this exercise.

Directions for the Candidate:

• Review the user dataset and content library provided.
• Design a personalization algorithm that would recommend the most appropriate training content for each user.
• Your algorithm should:

1. Define the key variables that influence content recommendations
2. Establish rules or weighted factors for matching users to content
3. Include at least one feedback loop that allows the system to improve over time
4. Account for changing user needs as they progress through training

• Diagram your algorithm using flowcharts, pseudocode, or other appropriate notation.
• Be prepared to walk through how your algorithm would work for 2-3 specific users from the dataset.

Feedback Mechanism:

• Provide feedback on the technical soundness of the algorithm (e.g., "Your weighting system for prior knowledge is well-designed").
• Suggest one improvement area (e.g., "Consider how you might incorporate time-based factors into your algorithm").
• Give the candidate 10 minutes to refine their algorithm based on the feedback.

Activity #3: Adaptive Content Transformation

This exercise assesses the candidate's ability to transform standard training content into adaptive, personalized learning experiences. It evaluates their instructional design skills, content strategy expertise, and understanding of how AI can deliver different content paths based on learner needs. This activity reveals how well the candidate can bridge the gap between content creation and technical implementation.

Directions for the Company:

• Provide a sample of existing customer training content (e.g., a product tutorial, feature guide, or onboarding module) in its current non-personalized format.
• Include information about three distinct user personas who would access this training.
• Supply a list of available data points that could be used for personalization.
• Allow 60 minutes for this exercise.

Directions for the Candidate:

• Review the provided training content and user personas.
• Redesign the content to support AI-driven personalization for the different user types.
• Your redesign should include:

1. Content variations for different user personas
2. Decision points where the AI would select different content paths
3. Metadata structure to support AI content selection
4. Adaptive assessment components that influence the personalization

• Create a visual map showing how the content would adapt based on user characteristics and behaviors.
• Prepare a brief explanation of how your redesigned content would improve the learning experience for each persona.

Feedback Mechanism:

• Highlight one particularly effective aspect of the content transformation (e.g., "Your approach to creating micro-modules that can be recombined is very effective").
• Suggest one area for improvement (e.g., "Consider how you might incorporate more varied media types for different learning preferences").
• Ask the candidate to revise one section of their design based on the feedback.

Activity #4: Personalization Data Analysis and Optimization

This exercise evaluates the candidate's ability to analyze user data to improve personalization algorithms and content strategies. It tests their analytical skills, data interpretation abilities, and understanding of how to translate insights into actionable improvements. This activity reveals how the candidate would approach continuous optimization of AI personalization systems.

Directions for the Company:

• Prepare a dataset showing 3-6 months of fictional user interaction data with an AI-personalized training system.
• Include metrics such as completion rates, time spent, assessment scores, satisfaction ratings, and personalization decisions made by the AI.
• Ensure the data contains some clear patterns and anomalies that could be identified.
• Provide basic visualization tools or spreadsheet software.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Analyze the provided dataset to evaluate the effectiveness of the current personalization approach.
• Identify at least three insights about how users are interacting with the personalized content.
• Develop recommendations for:

1. Improving the personalization algorithm based on actual usage patterns
2. Modifying content to better support personalization needs
3. Collecting additional data points that would enhance personalization
4. Addressing any performance issues or gaps revealed by the data

• Create at least one data visualization that illustrates a key finding.
• Prepare to present your analysis and recommendations in a 10-minute briefing.

Feedback Mechanism:

• Provide positive feedback on one aspect of their analysis (e.g., "Your identification of the correlation between quiz attempts and completion rates was insightful").
• Suggest one area where the analysis could be deepened (e.g., "Consider how you might segment the data by user experience level to reveal more patterns").
• Give the candidate 10 minutes to refine one of their recommendations based on this feedback.

Frequently Asked Questions

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

Each exercise requires 45-60 minutes for completion, plus time for setup, feedback, and discussion. We recommend scheduling them as separate interview segments or selecting 1-2 exercises most relevant to your specific needs. For senior roles, consider using Activity #1 and #4 as take-home assignments with a follow-up presentation.

Do we need technical AI expertise on our interview panel to evaluate candidates effectively?

While having someone with AI knowledge is helpful, these exercises are designed to evaluate practical application rather than deep technical expertise. Focus on the candidate's approach, reasoning, and ability to connect AI capabilities to learning outcomes. The key is having evaluators who understand your training needs and can assess whether the proposed solutions would work in your context.

How should we adapt these exercises for candidates with stronger technical backgrounds versus those with stronger instructional design backgrounds?

For technically-oriented candidates, place more emphasis on the algorithm design and data analysis activities, but still evaluate their understanding of learning principles. For instructional design-focused candidates, emphasize the content transformation exercise while assessing their technical literacy and ability to collaborate with AI specialists. The strategy development exercise is valuable for all candidates.

What if we don't currently use AI in our training programs but want to hire someone to implement it?

These exercises are still valuable for evaluating candidates who will build your AI personalization capabilities from scratch. Focus on Activities #1 and #3, which assess strategic thinking and content design skills. Modify the exercises to emphasize planning and initial implementation rather than optimization of existing systems.

Should we provide these exercises to candidates in advance?

For Activities #1 and #3, providing the company brief or content samples 24 hours in advance can result in more thoughtful responses. Activities #2 and #4 are better conducted as live exercises to assess the candidate's analytical thinking and problem-solving abilities in real-time. Be consistent in your approach across all candidates for fair comparison.

How do we evaluate candidates who propose approaches different from what we had in mind?

Focus on the soundness of their reasoning rather than alignment with your preconceived solutions. Strong candidates may introduce innovative approaches you hadn't considered. Evaluate whether their solution addresses the core requirements, demonstrates understanding of both AI and learning principles, and would be feasible to implement in your environment.

The ability to effectively implement AI-driven personalization in customer training represents a significant competitive advantage in today's market. By using these practical work samples, you can identify candidates who not only understand the theoretical aspects of AI personalization but can also apply these concepts to create meaningful learning experiences for your customers. The right talent in this specialized area can transform your customer training from a standard offering to a dynamic, adaptive system that responds to individual needs and drives better business outcomes.

For more resources to help you build an exceptional team, check out Yardstick's AI Job Description Generator, AI Interview Question Generator, and AI Interview Guide Generator.

Build a complete interview guide for AI in Customer Training Content Personalization by signing up for a free Yardstick account