Essential Work Samples for Evaluating Multimodal AI Interface Design Skills

Multimodal AI interfaces represent the cutting edge of human-computer interaction, combining text, images, audio, video, and other sensory inputs to create more intuitive and powerful user experiences. As these systems become increasingly prevalent across industries, the demand for skilled designers who can create effective interfaces for these complex technologies continues to grow.

Evaluating candidates for multimodal AI interface design positions presents unique challenges. Traditional portfolio reviews and technical interviews often fail to capture the full range of skills required for success in this multidisciplinary field. Candidates need to demonstrate not only technical proficiency with AI systems but also strong UX design principles, ethical awareness, and the ability to solve complex interaction problems.

Work samples provide a window into how candidates approach real-world challenges in multimodal AI interface design. By observing candidates as they tackle representative tasks, hiring managers can assess their problem-solving processes, technical knowledge, design thinking, and communication skills in context. This approach yields far more predictive insights than hypothetical questions or discussions of past work alone.

The following exercises are designed to evaluate key competencies for multimodal AI interface designers, including system architecture planning, component design, user testing methodology, and ethical consideration integration. Each exercise simulates a realistic scenario that multimodal AI interface designers encounter in their work, providing a comprehensive view of a candidate's capabilities and potential for success in the role.

Activity #1: Multimodal Input Flow Design

This exercise evaluates a candidate's ability to design a coherent user flow for a multimodal AI system that accepts different types of inputs. It tests their understanding of how various input modalities (voice, text, image, etc.) can work together seamlessly while maintaining a consistent user experience. This skill is fundamental for creating intuitive interfaces that allow users to interact with AI systems in natural, flexible ways.

Directions for the Company:

• Provide the candidate with a brief describing a multimodal AI application (e.g., a virtual assistant for interior design that accepts voice commands, photos, and text input).
• Include user personas and key use cases for the application.
• Supply wireframe templates or digital design tools for the candidate to use.
• Allow 45-60 minutes for this exercise.
• Prepare to discuss the candidate's design decisions and rationale.

Directions for the Candidate:

• Review the application brief, personas, and use cases provided.
• Design a user flow diagram showing how users will navigate between different input modalities.
• Create wireframes for 2-3 key screens that demonstrate how the interface handles transitions between input types.
• Annotate your designs to explain your decisions and how they support user goals.
• Prepare to present your solution and discuss your approach in 10 minutes or less.

Feedback Mechanism:

• After the presentation, provide one piece of positive feedback about an aspect of the design that effectively addresses user needs.
• Offer one suggestion for improvement, focusing on enhancing the integration between modalities or simplifying the user experience.
• Give the candidate 10 minutes to revise one aspect of their design based on the feedback and explain their changes.

Activity #2: Multimodal Output Prioritization

This exercise assesses a candidate's ability to make thoughtful decisions about how and when to present different types of information (text, images, audio, etc.) to users. It tests their understanding of cognitive load, information hierarchy, and contextual appropriateness—critical skills for designing interfaces that communicate effectively without overwhelming users.

Directions for the Company:

• Create a scenario involving a multimodal AI system that needs to communicate complex information to users (e.g., a health monitoring application that presents vital signs, medication reminders, and lifestyle recommendations).
• Provide sample data sets representing different types of information the system needs to communicate.
• Include contextual variables such as user location, time of day, and device type.
• Prepare a template or framework for the candidate to document their output strategy.
• Allow 45 minutes for this exercise.

Directions for the Candidate:

• Review the scenario and sample data provided.
• Create a decision framework for determining which modality (text, audio, visual, haptic, etc.) should be used for different types of information in various contexts.
• Design 2-3 example outputs showing how the same information might be presented differently depending on context.
• Document your rationale for each decision, explaining how it balances user needs, technical constraints, and contextual factors.
• Be prepared to discuss how your approach would scale to handle additional types of information or contexts.

Feedback Mechanism:

• Provide positive feedback on one aspect of the candidate's prioritization strategy that effectively addresses user needs or context.
• Suggest one improvement related to accessibility, cognitive load, or contextual awareness.
• Ask the candidate to revise their approach for one specific scenario based on the feedback and explain their reasoning for the changes.

Activity #3: Error Recovery Interface Design

This exercise evaluates a candidate's ability to design interfaces that gracefully handle AI errors and misinterpretations across different modalities. It tests their problem-solving skills, empathy for user frustration, and technical understanding of AI limitations—essential capabilities for creating trustworthy multimodal AI experiences.

Directions for the Company:

• Develop a scenario involving a multimodal AI system that has misinterpreted user input (e.g., a language learning app that has incorrectly assessed a user's pronunciation or a visual recognition system that has misidentified objects in a photo).
• Provide examples of different types of errors that might occur across modalities.
• Include relevant constraints such as technical limitations or business requirements.
• Prepare a template for the candidate to document their error recovery design.
• Allow 50 minutes for this exercise.

Directions for the Candidate:

• Review the scenario and error examples provided.
• Design an interface flow that helps users understand and recover from AI misinterpretations across different modalities.
• Create wireframes or mockups showing how the interface communicates errors and guides users toward resolution.
• Include strategies for gathering corrective feedback from users that can improve the AI system over time.
• Document how your design balances transparency about AI limitations with maintaining user confidence in the system.

Feedback Mechanism:

• Highlight one aspect of the candidate's error recovery design that effectively supports users or improves the AI system.
• Suggest one improvement related to clarity, efficiency, or user frustration reduction.
• Ask the candidate to revise one screen or interaction based on the feedback and explain how their changes address the concern.

Activity #4: Ethical Considerations in Multimodal AI Design

This exercise assesses a candidate's awareness of ethical implications in multimodal AI interface design and their ability to proactively address potential issues. It tests their understanding of bias, privacy, transparency, and user agency—critical considerations for designing responsible AI systems that earn user trust.

Directions for the Company:

• Create a scenario for a multimodal AI application that raises ethical considerations (e.g., an AI interview coach that analyzes facial expressions, voice tone, and language; or a content moderation system that processes user-generated images and text).
• Provide a brief on the system's capabilities, target users, and business objectives.
• Include relevant constraints such as regulatory requirements or company values.
• Prepare a template for documenting ethical considerations and mitigation strategies.
• Allow 45-60 minutes for this exercise.

Directions for the Candidate:

• Review the scenario and system brief provided.
• Identify at least 3-5 potential ethical concerns related to the multimodal AI interface (e.g., bias in facial analysis, privacy implications of voice processing, transparency about AI capabilities).
• For each concern, design interface elements or user flows that address or mitigate the issue.
• Create a simple prototype or wireframe demonstrating how your design communicates important ethical information to users or gives them appropriate control.
• Document your rationale for each design decision and how it balances ethical considerations with user experience and business objectives.

Feedback Mechanism:

• Provide positive feedback on one ethical consideration the candidate identified and addressed effectively.
• Suggest one additional ethical consideration or a more robust approach to addressing an identified issue.
• Ask the candidate to enhance their design to address the feedback and explain how their changes improve the ethical stance of the interface.

Frequently Asked Questions

How long should each work sample exercise take?

Each exercise is designed to take 45-60 minutes. However, you may adjust the time based on your hiring process constraints. If shortening the exercises, consider focusing on specific aspects rather than reducing the overall scope, as this will provide more meaningful insights into the candidate's capabilities.

Should candidates complete these exercises during the interview or as take-home assignments?

Both approaches have merit. Take-home assignments allow candidates more time for thoughtful work but require more of their personal time. In-person exercises provide better insight into thinking processes but may increase interview stress. Consider your priorities and the seniority of the role when deciding. For senior positions, a hybrid approach—where candidates prepare some elements in advance and then discuss or extend their work during the interview—often works well.

How should we evaluate candidates who take different approaches to these exercises?

Focus on the reasoning behind their decisions rather than expecting a specific "correct" solution. Strong candidates should be able to articulate why they made certain choices and demonstrate awareness of tradeoffs. Look for evidence of user-centered thinking, technical understanding of multimodal AI capabilities and limitations, and systematic approaches to complex problems.

Do we need technical AI expertise to evaluate these exercises effectively?

While some technical understanding is helpful, these exercises are designed to evaluate design thinking and problem-solving in the context of multimodal AI interfaces. Focus on the clarity of the candidate's approach, their consideration of user needs, and their ability to address the unique challenges of multimodal systems. Include team members with complementary expertise (UX design, AI development, product management) in the evaluation process when possible.

How can we adapt these exercises for candidates with different levels of experience?

For junior candidates, provide more structure and guidance in the briefs and focus evaluation on fundamental design principles and problem-solving approaches. For senior candidates, introduce additional constraints or complexity, such as accessibility requirements, international users, or integration with existing systems. Adjust your expectations for the sophistication of solutions based on experience level.

Should we provide candidates with feedback during the actual hiring process?

The feedback mechanisms described are primarily designed to assess how candidates respond to critique and iterate on their work—a crucial skill for interface designers. While you should provide this structured feedback during the exercise, it's a separate question whether to provide comprehensive feedback after the hiring decision. Many companies find that offering constructive feedback to unsuccessful candidates builds goodwill and strengthens their employer brand.

Multimodal AI interface design represents one of the most exciting and challenging frontiers in human-computer interaction. By incorporating these work samples into your hiring process, you'll gain valuable insights into how candidates approach the unique challenges of designing for systems that combine multiple forms of input and output. These exercises go beyond traditional interviews to reveal candidates' practical skills, problem-solving approaches, and ethical awareness—all critical factors for success in this rapidly evolving field.

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