Assessing Human–AI Collaboration: Interview Exercises and Si

Introduction

As organizations adopt AI tools across administrative workflows, executive assistants (EAs) must demonstrate fluency in human–AI collaboration. This article provides a practical, interview-ready framework for evaluating candidates’ technical aptitude, judgment, communication, and ethical awareness when working with AI. It is designed for hiring managers, talent acquisition teams, and executives seeking rigorous, scalable assessments.

Why Human–AI Collaboration Skills Matter

AI changes the nature of administrative work: routine tasks become automated, while value shifts toward oversight, prompt engineering, data validation, and stakeholder communication. Executive assistants who can orchestrate AI outputs, detect errors, and interpret results retain strategic value.

Background on Human–AI Collaboration

Human–AI collaboration describes how people and AI systems share tasks, with humans providing context, oversight, and ethical judgment while AI handles scale and pattern recognition. For EAs, collaboration means using AI to draft communications, summarize meetings, schedule complex calendars, and prepare decision briefs while ensuring accuracy and confidentiality.

Core Competencies to Evaluate

Design interview content around the following competency domains. Each domain should be mapped to measurable behaviors and scoring criteria.

• Prompting and Tool Fluency: Ability to craft prompts, select appropriate models or tools, and interpret results.
• Verification and Fact-Checking: Methods for validating AI outputs and reconciling discrepancies.
• Contextual Judgment: Prioritization, escalation, and framing outputs for executive decision-making.
• Data Privacy & Security Awareness: Handling sensitive information and complying with policies.
• Communication & Stakeholder Management: Translating AI outputs to actionable guidance for executives and teams.
• Adaptability & Learning Agility: Speed of adopting new tools and embedded continuous improvement.

Designing Interview Exercises and Simulations

Effective assessments blend realism, repeatability, and objective scoring. Use a combination of take-home tasks, live simulations, and structured interviews to capture both asynchronous problem-solving and in-the-moment decision-making.

1. Define business-aligned scenarios: Tie exercises to real responsibilities (e.g., meeting prep, sensitive email drafting, travel planning with constraints).
2. Set explicit inputs and boundaries: Provide data sets, AI tools available, and policy constraints (non-disclosure, data redaction rules).
3. Use layered difficulty: Start with simple prompts and progress to ambiguous or high-stakes tasks that reveal judgment.
4. Include a debrief phase: Ask candidates to explain reasoning, verification steps, and trade-offs after each task.
5. Standardize scoring rubrics: Define target behaviors and sample responses to ensure fairness and inter-rater reliability.

Sample Interview Exercises

The following simulations are designed to be adapted to firm size, technology stack, and risk tolerance. Include time limits and deliverable templates for consistency.

Exercise A — AI-Assisted Briefing Pack (30–45 minutes)

Scenario: The CEO requests a one-page briefing on an incoming M&A target, including risks, timeline implications, and a draft outreach email to the target’s CFO. Candidate has access to a web-enabled summarization tool and an LLM-based drafting assistant.

• Task: Produce a one-page brief and a 3-paragraph outreach email.
• Evaluation focus: accuracy of synthesized facts, identification of missing information, clarity of recommended next steps, and appropriate confidentiality handling.
• Debrief prompts: Ask candidate to list verification sources, highlight AI hallucinations (if any), and explain why they framed recommendations as they did.

Exercise B — Calendar Optimization with Constraints (20–30 minutes)

Scenario: An executive travels across three time zones in one week and needs prioritized meetings with key partners, avoiding red zones and allowing prep time. Candidate can use scheduling automation tools and an AI assistant that suggests options.

• Task: Deliver an optimized calendar, conflict resolution plan, and a short communication to each stakeholder explaining the scheduling rationale.
• Evaluation focus: prioritization logic, ability to set constraints correctly, communication clarity, and tool utilization efficiency.

Exercise C — Sensitive Information Redaction and Compliance (15–25 minutes)

Scenario: Candidate receives a draft board memo containing personally identifiable information (PII) and confidential financial figures. They must prepare a redacted version for external distribution and document the steps taken to preserve chain-of-custody and compliance.

• Task: Produce redacted memo, explain redaction criteria, and list follow-up actions for secure distribution.
• Evaluation focus: knowledge of privacy policies, attention to detail, and procedural rigor.

Scoring Rubrics and Evaluation Criteria

Rubrics should translate behavioral expectations into numeric scores for comparability across candidates. Use weighted components to reflect role-critical skills.

Example rubric weights (adjust to organization priorities):

• Accuracy & Verification: 30%
• Judgment & Prioritization: 25%
• Tool Fluency & Efficiency: 20%
• Communication Quality: 15%
• Privacy & Compliance: 10%

Sample scoring bands per competency (0–4 scale):

1. 4 — Exemplary: Demonstrates advanced technique, cites verification sources, and presents proactive mitigations.
2. 3 — Proficient: Accurate output with minor gaps, uses tools correctly, and communicates clearly.
3. 2 — Developing: Requires coaching to identify AI errors, occasional lapses in judgment or policy adherence.
4. 1 — Insufficient: Frequent inaccuracies, failure to verify, or non-compliance risks.

Implementing Simulations in the Hiring Process

Operationalize assessments to scale and remain consistent across hiring cycles.

1. Pilot with current senior EAs to calibrate difficulty and rubric thresholds.
2. Train interviewers on rubric use and unconscious bias mitigation.
3. Automate take-home assessments with time-boxed submissions and standardized deliverable templates.
4. Record live simulation sessions (with consent) to allow second reviews and consistency checks.
5. Aggregate scores into a decision dashboard that maps to role expectations and onboarding plans.

Legal and Ethical Considerations

When assessing human–AI collaboration skills, protect candidate data and avoid discrimination arising from tool access disparities. Consider the following:

• Accessibility: Provide accommodations for candidates with disabilities and ensure tools used in assessments are accessible.
• Tool neutrality: Avoid evaluating on proprietary tools candidates may not have encountered; focus on transferable skills like prompting and verification.
• Consent & privacy: Secure candidate consent for any recorded live interactions and limit data retention to hiring needs.
• Bias mitigation: Regularly review assessment outcomes for adverse impact across demographic groups and adjust as needed.

Source note: For baseline best practices on AI governance and workforce impacts, review industry guidance reports such as McKinsey on AI adoption and HBR coverage of human–AI work design.[1][2]

Key Takeaways

• Design multi-stage assessments combining take-home tasks and live simulations to evaluate both craft and judgment.
• Measure six core competencies: prompting, verification, judgment, privacy, communication, and adaptability.
• Standardize rubrics and pilot with incumbent staff to set realistic thresholds and ensure fairness.
• Embed legal, ethical, and accessibility considerations into assessment design and operations.
• Use debriefs to probe candidate reasoning — explanations reveal depth of understanding more than polished outputs alone.

Frequently Asked Questions

How long should AI-focused interview simulations take?

Keep individual exercises short and focused: 15–45 minutes per simulation depending on complexity. Total assessment time for candidates should generally remain under three hours to balance depth and practicality.

Which AI tools should candidates be allowed to use?

Allow accessible, commonly used tools (e.g., mainstream LLMs, summarizers, scheduling assistants) but set clear constraints. Prioritize testing transferable skills (prompting, verification) rather than tool-specific shortcuts.

How do you ensure fair scoring across interviewers?

Use detailed rubrics with examples, conduct interviewer calibration sessions, and use multiple raters when possible. Record live exercises (with consent) for arbitration and training.

Can small businesses apply these simulations without major investment?

Yes. Start with lightweight exercises: a take-home brief and a single live task using free or low-cost AI tools. Focus on structured debriefs and consistent scoring rather than high-fidelity simulation environments.

How do you evaluate ethical judgment in a time-boxed simulation?

Include deliberate ethical constraints in scenarios (e.g., handling PII or ambiguous authorization). Evaluate whether candidates identify risks, propose mitigations, and follow acceptable policy steps under pressure.

What onboarding steps should follow hiring an EA strong in human–AI collaboration?

Provide role-specific tool access, structured mentoring sessions focused on verification workflows, and quarterly review cycles to refine prompt libraries and escalate governance issues. Pair new hires with senior EAs for shadowing during high-risk tasks.

Sources: McKinsey & Company, "The State of AI in 2023"; Harvard Business Review, "Designing Human–AI Workflows".[1][2]

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McKinsey: The State of AI

Harvard Business Review: AI