Free Leadership Effectiveness Survey Template | Excel with Auto-Scoring
Free Leadership Effectiveness Survey Template | Excel with Auto-Scoring
This template turns ai interview questions for software engineers into a consistent, scorable “interviewer survey” you can use across panels. You get faster decisions, early warnings on risky AI habits, and cleaner debriefs because everyone rates the same behaviors.
If you already run skill matrices, you can map these scores into your skill management workflow and track improvement over time, without forcing candidates to use private accounts or specific vendors.
Survey questions: ai interview questions for software engineers
Use a 1–5 scale (1 = Strongly disagree, 5 = Strongly agree). Interviewers answer based on what the candidate demonstrated in discussion, a live exercise, or a take-home (AI allowed under your rules).
2.1 Closed questions (Likert scale)
- Q1 The candidate uses AI to speed up scaffolding without shipping unreviewed boilerplate.
- Q2 They can explain how they validate AI-generated code beyond “it compiles.”
- Q3 They proactively align AI output with existing code style and naming conventions.
- Q4 They use AI to refactor safely while preserving behavior and performance constraints.
- Q5 They can spot when AI suggestions introduce hidden complexity or poor readability.
- Q6 They treat AI as a draft partner, not as the owner of the final solution.
- Q7 They can use AI to propose tests, then adjust tests to match real edge cases.
- Q8 They use AI in PR review to find issues, but still do manual reasoning.
- Q9 They can explain when AI is not suitable for code review decisions.
- Q10 They check AI-suggested changes for maintainability, not only correctness.
- Q11 They can articulate how they avoid “cargo cult” fixes from AI.
- Q12 They communicate AI usage transparently in PRs or review comments when relevant.
- Q13 They follow “Datenminimierung” when using AI tools with code, logs, or tickets.
- Q14 They can clearly describe what they would never paste into a public AI tool.
- Q15 They recognize secrets exposure risks (tokens, keys) in prompts and outputs.
- Q16 They can explain how they anonymize examples (PII, customer data) before prompting.
- Q17 They show awareness of IP/confidentiality risks when sharing proprietary code.
- Q18 They know how to escalate unclear tool policies to Security/Legal/Datenschutz.
- Q19 They use AI to explore architecture options, then validate trade-offs with first principles.
- Q20 They can explain how AI proposals change when constraints (latency, cost, compliance) change.
- Q21 They can produce or refine concise documentation (README, ADR) with AI support.
- Q22 They verify AI-generated docs against actual code behavior and system boundaries.
- Q23 They can detect when AI invents components, dependencies, or non-existent features.
- Q24 They can explain how they keep documentation consistent across teams and repos.
- Q25 They write prompts with clear context, constraints, and acceptance criteria.
- Q26 They can iteratively improve prompts based on failures, not only on successes.
- Q27 They separate “private details” from “useful context” to reduce data exposure.
- Q28 They can ask AI for alternatives, then choose one with explicit reasoning.
- Q29 They can create reusable prompt patterns for debugging, testing, and refactoring.
- Q30 They can explain how they avoid prompt-induced confirmation bias during debugging.
- Q31 They can collaborate on team AI norms (prompt templates, review rules, PR labeling).
- Q32 They can describe a lightweight governance setup that won’t block delivery.
- Q33 They show respect for Works Council (“Betriebsrat”) involvement where required.
- Q34 They understand why logging, retention, and access controls matter for AI tooling.
- Q35 They can discuss vendor/tool risk at a high level without claiming “compliance guarantees.”
- Q36 They can drive alignment across Engineering, Security, Legal, and Product.
- Q37 They use AI to accelerate debugging while keeping a clear hypothesis trail.
- Q38 They can reason about failures with partial information, not only with AI output.
- Q39 They can use AI on logs/error traces after redacting sensitive identifiers.
- Q40 They know when to stop prompting and reproduce the bug with minimal steps.
- Q41 They can identify whether AI-suggested fixes increase incident risk (regressions, blast radius).
- Q42 They can describe how they would document an AI-assisted incident analysis.
- Q43 The candidate explains AI usage in a way that supports team learning.
- Q44 They demonstrate psychological safety (“psychologische Sicherheit”) by inviting challenge and review.
- Q45 They respond well when an interviewer questions an AI-based approach.
- Q46 They show good judgment on when to invest in deeper understanding vs shipping faster.
- Q47 They can mentor others on safe AI usage without creating dependency.
- Q48 They can describe how they measure AI impact (cycle time, defects, review quality).
- Q49 Their overall approach reduces risk while improving delivery speed and code quality.
2.2 Optional overall / NPS-like question
- Q50 How likely are you to recommend hiring this candidate for AI-safe engineering? (0–10)
2.3 Open-ended questions (for notes)
- Which 1–2 behaviors gave you the most confidence in their AI-assisted engineering quality?
- Where did you see the biggest risk (security, quality, collaboration) and why?
- If hired, what specific coaching would help them use AI more safely or effectively?
- What would you want to validate in a follow-up round (exercise, deep-dive, reference)?
Decision table (how to act on results)
| Question(s) / area | Score / threshold | Recommended action | Responsible (Owner) | Goal / deadline |
|---|---|---|---|---|
| AI-assisted coding quality (Q1–Q6) | Average <3,0 | Add a short refactor + tests exercise; focus on readability and ownership. | Hiring Manager | Schedule within ≤7 days |
| Code review & quality discipline (Q7–Q12) | Average <3,5 | Run a PR review simulation; require explicit trade-offs and test strategy. | Tech Lead interviewer | Decide within ≤5 days |
| Privacy & security (Q13–Q18) | Any item ≤2 | Escalate to Security interview screen; verify data-handling judgment and escalation behavior. | Security partner + Hiring Manager | Complete within ≤10 days |
| Architecture & documentation (Q19–Q24) | Average <3,0 | Add system-design deep dive; require constraints, risks, and documentation habits. | Senior Engineer interviewer | Within ≤10 days |
| Prompt design & workflow (Q25–Q30) | Average ≥4,0 | Fast-track: treat as “strong signal”; probe for repeatability and coaching ability. | Hiring Manager | Debrief within ≤48 h |
| Collaboration & governance (Q31–Q36) | Average <3,0 for Lead roles | Add governance deep-dive; discuss Betriebsrat, policy design, rollout approach. | Head of Engineering | Within ≤14 days |
| Debugging & incident response (Q37–Q42) | Average <3,0 | Run incident tabletop; require redaction plan and hypothesis-driven debugging. | Reliability/Platform lead | Within ≤10 days |
| Overall signal (Q49 + Q50) | Q50 ≤6 or Q49 <3,5 | Hold hire until one targeted follow-up closes the biggest risk area. | Hiring Manager + HR/People Partner | Decision within ≤14 days |
Key takeaways
- Standardize panel judgment on AI safety, quality, and collaboration.
- Use thresholds to trigger follow-ups, not endless debate.
- Separate “uses AI” from “uses AI responsibly under constraints.”
- Track domain scores to refine hiring and onboarding priorities.
- Make governance discussable without turning interviews into compliance theater.
Definition & scope
This survey measures how candidates use AI coding assistants and LLMs in real engineering work: coding, reviews, privacy/security, documentation, governance, and incident response. It is designed for interview panels hiring junior, mid, senior/staff, and tech lead/engineering manager roles in EU/DACH contexts. It supports hiring decisions, targeted follow-ups, and onboarding/coaching plans.
Scoring & thresholds
Use a 1–5 Likert scale: 1 (Strongly disagree) to 5 (Strongly agree). Treat averages <3,0 as critical risk, 3,0–3,9 as needs follow-up, and ≥4,0 as strong signal. Convert scores into decisions by triggering one targeted follow-up per weak domain, or by defining onboarding actions tied to the weakest 2 domains.
If you already use structured hiring, treat this like a compact scorecard that complements role skills. If you also maintain a skills framework, you can align AI behavior signals with your engineering expectations (see this engineering skills matrix template approach for leveling consistency).
Domain mapping (for reporting)
| Domain | Questions | Suggested weight (Junior / Mid / Senior / Lead) | Rubric shortcut (Basic / Strong / Red flag) |
|---|---|---|---|
| AI-assisted coding & refactoring | Q1–Q6 | 25 % / 25 % / 20 % / 15 % | Basic: accepts AI output; Strong: validates + adapts; Red flag: ships unreviewed AI code |
| AI in code review & quality | Q7–Q12 | 20 % / 20 % / 20 % / 15 % | Basic: finds nits; Strong: finds risk + tests; Red flag: trusts AI over evidence |
| Data, privacy & security | Q13–Q18 | 20 % / 20 % / 20 % / 20 % | Basic: vague rules; Strong: Datenminimierung + escalation; Red flag: pastes secrets/PII |
| Design, architecture & documentation | Q19–Q24 | 15 % / 15 % / 20 % / 20 % | Basic: lists options; Strong: trade-offs + ADRs; Red flag: accepts hallucinated designs |
| Workflow & prompt design | Q25–Q30 | 10 % / 10 % / 10 % / 10 % | Basic: ad-hoc prompts; Strong: reusable patterns; Red flag: prompts leak sensitive context |
| Collaboration & governance | Q31–Q36 | 5 % / 5 % / 5 % / 10 % | Basic: tool opinions; Strong: policy collaboration; Red flag: ignores Betriebsrat/Legal |
| Debugging & incident response with AI | Q37–Q42 | 5 % / 5 % / 5 % / 10 % | Basic: asks AI first; Strong: hypothesis + redaction; Red flag: uploads sensitive logs |
Simple scoring process (5 steps)
Keep it lightweight. Score quickly while the evidence is fresh, then use thresholds to decide the next action.
- Each interviewer scores Q1–Q49 within ≤2 h after their interview.
- HR/People Partner calculates domain averages and highlights any item ≤2.
- Panel agrees on 1 “top strength” and 1 “top risk” per candidate.
- If any domain average is <3,0, add 1 targeted follow-up, not a full extra round.
- Capture the final decision and rationale in your ATS notes for auditability.
- Hiring Manager defines pass/fail thresholds per role level before interviews start (≤2 days).
- Tech Lead adds a short exercise for the weakest domain when Average <3,0 (≤7 days).
- Security partner runs a 20-minute screen when any Q13–Q18 item ≤2 (≤10 days).
- HR/People Partner publishes a one-page debrief summary to the panel (≤48 h).
Follow-up & responsibilities
Scores only help if you route signals fast. Treat “privacy/security risk” differently from “needs coaching.” Use explicit owners and short deadlines so panels don’t drift.
For process consistency, align this with your broader recruiting workflow: structured criteria up front, documented evidence, and a repeatable debrief format that reduces bias.
Routing rules (If–Then)
If a candidate triggers any Q13–Q18 item ≤2, you do not “average it out.” You add a Security/Datenschutz deep-dive and document the outcome. If the biggest weakness is quality (Q1–Q12) at Average <3,0, you run one constrained coding/review exercise and retest the same behaviors.
- Hiring Manager owns the final hire/no-hire decision and logs rationale within ≤24 h of debrief.
- HR/People Partner owns scoring hygiene, aggregation, and artifact retention within ≤48 h.
- Tech Lead interviewer owns follow-up exercise design and evaluation rubric within ≤7 days.
- Security/Datenschutz partner owns the sensitive-data screen and outcome note within ≤10 days.
- Head of Engineering owns governance checks for Lead roles (Q31–Q36) within ≤14 days.
What to do with “critical” open-text feedback
If an interviewer notes risky behavior (for example, “would paste production logs into a public tool”), treat it like a critical signal even if the numeric score looks fine. Respond within ≤24 h, capture the quote, and verify it in the next step. Avoid long email threads; resolve it in one follow-up conversation with clear questions.
Fairness & bias checks
These are ai interview questions for software engineers, but your process can still become unfair. Candidates have unequal access to paid tools, private sandboxes, or “AI-first” workplaces. So you score behaviors and judgment, not tool ownership. You also check outcomes by group to spot unintended bias.
How to slice results (without overfitting)
| Slice | Minimum group size | Flag threshold | What you do next |
|---|---|---|---|
| Experience level (Junior/Mid/Senior/Lead) | n ≥10 per level | Domain average differs by ≥0,5 | Review whether questions match level expectations; adjust weights, not standards. |
| Remote vs. onsite candidates | n ≥10 per group | Q25–Q30 average differs by ≥0,5 | Check interview format: did one group get less time or fewer artifacts? |
| Internal vs. external candidates | n ≥10 per group | Q31–Q36 average differs by ≥0,5 | Ensure externals get enough context on governance; avoid insider advantage. |
| Country/region (EU/DACH vs non-EU) | n ≥10 per group | Q13–Q18 average differs by ≥0,5 | Clarify privacy expectations early; focus on principles, not local legal trivia. |
Common patterns and how to respond
Pattern 1: Juniors score low on Q31–Q36. That’s normal; down-weight governance for junior roles and test coachability instead. Pattern 2: Seniors score high on speed but low on Q13–Q18. That’s a real risk; add a data-handling scenario and require a redaction plan. Pattern 3: Candidates from smaller companies score lower on Q25–Q30. Don’t punish them; test whether they can learn prompt patterns quickly.
- HR updates interviewer training to emphasize “behavior over tool access” within ≤30 days.
- Hiring Manager removes any “must have Copilot” requirement from job criteria within ≤14 days.
- Tech Leads add one standardized privacy scenario to every loop within ≤21 days.
- Panel lead checks for inconsistent probing across groups and corrects within ≤7 days.
Examples / use cases
Use these mini-cases to calibrate what “good” looks like. Keep them short and close to real engineering work.
Use case 1: Strong speed, weak safety
A senior candidate scores ≥4,0 on Q1–Q12 but gets a ≤2 on Q15 (secrets exposure). The panel pauses the decision and runs a 20-minute scenario: “You need help debugging; what do you paste into the tool, what do you redact, what do you do locally?” After the follow-up, the candidate shows a clear redaction workflow and escalation to Security. The hire moves forward with a 30-day onboarding focus on tool policy.
Use case 2: Good safety, weak ownership
A mid-level candidate scores ≥4,0 on Q13–Q18 but averages 2,8 on Q1–Q6. In a refactor exercise, they accept AI suggestions without adapting to codebase conventions. The follow-up asks them to rewrite the same function to match team style and add tests. If they improve quickly, you treat it as coachable; if not, you decline due to ownership risk.
Use case 3: Lead candidate avoids governance
A tech lead candidate scores well on code and prompting but averages 2,7 on Q31–Q36. They frame governance as “Security’s job” and ignore Betriebsrat involvement. The panel adds a deep-dive: “Design an AI tool rollout with logging, retention, and opt-in rules.” If they still avoid shared ownership, you treat it as a leadership gap for that level.
- Hiring Manager documents the “one follow-up per weak domain” rule and enforces it next loop (≤7 days).
- HR creates a shared library of calibrated example answers by level (≤45 days).
- Security partner provides a 1-page redaction checklist for interview scenarios (≤30 days).
Implementation & updates
Roll this out like a product change: pilot, learn, then scale. You will also need a clear stance on whether AI is allowed in exercises, and under which data rules.
A talent platform like Sprad Growth can help automate survey sends, reminders and follow-up tasks, but the core requirement is simpler: one consistent scorecard, stored with the hiring packet.
Interview blueprints (timeboxed)
| Blueprint | Who it fits | What you run | What you score |
|---|---|---|---|
| 15–20 min AI block | Junior / Mid | 1 scenario + 1 small code change + short Q&A | Q1–Q6, Q13–Q18, Q25–Q30, Q43–Q46 |
| 30–40 min AI + governance deep-dive | Senior / Staff / Lead | PR review simulation + privacy scenario + rollout discussion | Q7–Q12, Q13–Q18, Q31–Q36, Q37–Q42 |
| 10–15 min leadership screen | Tech Lead / Engineering Manager | Policy trade-offs + team norms + incident learning loop | Q31–Q36, Q44–Q49 |
Update cadence (3 steps)
AI tools change fast, but your evaluation criteria should stay stable: ownership, validation, data handling, and collaboration. Update the wording once per year, then run a quick quarterly check to remove outdated tool-specific references.
- Pilot with 1 engineering team for 4 weeks and review outcomes (hire quality, panel friction).
- Roll out to all engineering hiring loops with a 45-minute interviewer training.
- Review annually; adjust thresholds if your hiring bar or tool policy changes.
- HR owns the pilot setup, scoring sheet, and storage location (≤14 days).
- Head of Engineering owns the “AI allowed in interviews” policy statement (≤21 days).
- Security/Legal/Datenschutz review the redaction rules and retention approach (≤30 days).
- Panel leads run a calibration session after 10 candidates and record changes (≤60 days).
- HR tracks KPIs monthly: completion rate, time-to-debrief, follow-up rate, offer rate.
Conclusion
Using ai interview questions for software engineers as a scored interviewer survey makes AI use visible and comparable, instead of anecdotal. You spot risk earlier (especially around Datenminimierung, IP, and secrets), you improve debrief quality because everyone speaks in the same domains, and you can turn weak areas into clear follow-ups or onboarding plans.
To start, pick 1 pilot loop, copy Q1–Q49 into your interview scorecard tool, and agree on thresholds like Average <3,0 triggers a follow-up. Then assign owners for Security/Datenschutz screens and for governance deep-dives in lead hiring. After your first 10 candidates, run a 30-minute calibration to tighten what “Strong” and “Red flag” mean for your team.
FAQ
How often should we update these ai interview questions for software engineers?
Do a light check every quarter and a full review 1× per year. Quarterly, remove anything that depends on a specific vendor UI or feature. Yearly, review whether your engineering standards changed (testing bar, PR rules, incident process) and adjust question weights by level. Keep the domains stable; change the examples, not the principles.
What should we do when scores are very low (Average <3,0) in one domain?
Trigger one targeted follow-up, not a full extra interview loop. Pick the lowest domain, design a 20–40 minute scenario, and rescore only that domain. If the candidate improves with clear reasoning, treat it as coachable. If they repeat the same risky behavior (for example, no validation or unsafe data sharing), decline and document the rationale.
How do we handle critical comments in open-text notes?
Treat comments as evidence claims that need verification. If someone writes “would paste production logs into a public tool,” route it within ≤24 h to a Security/Datenschutz follow-up and ask the candidate to walk through a redaction plan. Keep the tone neutral. Your goal is to understand judgment under constraints, not to catch people out.
How do we avoid discrimination if some candidates had no access to paid AI tools?
Don’t score tool ownership. Score behaviors: how they validate outputs, how they protect data, how they explain trade-offs, and how they collaborate. Offer the same interview artifacts to everyone, and allow a “no-AI” path for exercises if needed. Calibrate your panel to avoid punishing candidates from smaller companies that had stricter tool bans.
Do we need a formal framework for AI risk to support this process?
You don’t need a heavy framework, but a shared vocabulary helps. Many teams borrow concepts like “risk identification, mitigation, and monitoring” from the NIST AI Risk Management Framework (AI RMF) and translate them into practical interview scenarios. Keep it high-level and non-legal, especially in EU/DACH settings with Betriebsrat involvement.
Jürgen Ulbrich
Jürgen Ulbrich has more than a decade of experience in developing and leading high-performing teams and companies. As an expert in employee referral programs as well as feedback and performance processes, Jürgen has helped over 100 organizations optimize their talent acquisition and development strategies.
