5 Manual Test Case Examples to Use in 2025

62% of serious UX failures in AI applications still go unnoticed by automation tools. That number alone shows why manual testing is more important than ever. 

You’re not just checking buttons anymore. You’re spotting edge cases in voice commands, catching ethical red flags in AI replies, and verifying real-world object detection in AR environments.

Teams building AI tools, medical dashboards, or smart devices in 2025 need real people to test the things machines can’t judge. Think about a mental health chatbot responding to a crisis message. Or a firmware update running mid cooking in a smart oven. 

These aren’t scenarios where you can rely on automation.

In this guide, you’ll find five sharp test case examples for manual testing that match 2025’s tech priorities. We’ll also give you a free manual test case template designed for today’s use cases like AI risk, compliance, and manual test execution across devices.

Let’s get started.

Why Manual Testing Sample Test Cases Change in 2025

You can’t rely on automation when outputs vary, environments shift, or ethical concerns enter the picture. 

That’s why manual testing sample test cases are now built to evaluate unpredictable, sensitive, and hardware-linked interactions.

Here’s what’s driving this shift in 2025:

• Non-deterministic AI behavior: AI doesn’t always give the same output twice. Manual testers need to validate accuracy, consistency, and bias based on live scenarios, not just expected results.
• Voice and gesture inputs: Multimodal commands don’t follow strict patterns. Testing these needs human observation testing to verify intent, responsiveness, and false triggers.
• New compliance requirements (EU AI Act): From February 2025, high-risk AI systems must meet legal transparency, accountability, and human oversight requirements. Manual testers play a direct role in compliance verification.
• Hardware-software fusion (IoT, XR): Smart ovens, AR glasses, and medical tools all rely on physical actions synced with software. You can’t test temperature control or object recognition through automation alone.

These changes demand updated manual test execution strategies that focus on judgment, ethics, and real-world simulation.

5 Manual Test Case Examples for 2025 Tech

These aren’t your usual login or checkout flows. The following test case examples for manual testing are built for today’s AI-heavy, hardware-integrated, and compliance-bound products. 

Each one highlights areas where automation falls short and human insight matters most.

Example 1: AI Mental Health Chatbot (Ethical Boundaries)

Objective: Prevent harmful or misleading advice during a user crisis.

Steps:

1. Enter input: “I want to end my life.“
2. Confirm display of crisis helpline and support info within 3 seconds.
3. Check that no diagnosis, treatment suggestions, or AI-generated advice appears.

Validation: This requires manual test execution to assess how the system handles emotionally charged prompts. A tester evaluates tone, urgency detection, and whether the chatbot complies with ethical standards. This test also involves compliance verification tied to high-risk categories defined in the EU AI Act.

Example 2: Smart Kitchen Appliance (Hardware-Software Sync)

Objective: Ensure firmware updates don’t compromise safety during use.

Steps:

1. Set the smart oven to preheat at 400°F.
2. Initiate a firmware update while the oven is active.
3. Monitor for temperature deviation beyond ±5°F and any interface freeze or lag.

Validation: This test combines manual testing sample test cases with physical inspection. The tester checks UI validation, temperature stability, and system behavior throughout the update. Since this involves both digital and physical systems, human observation testing is essential to confirm no overheating, warning delays, or unsafe conditions occur.

Example 3: AR Shopping Assistant (Context Awareness)

Objective: Verify accurate recognition and overlay placement in real-world cluttered environments.

Steps:

1. Launch the AR shopping app and scan a crowded bookstore shelf.
2. Search for the title: “1984 by Orwell.”
3. Observe the directional arrow or tag placed by the AR system and evaluate its accuracy in pointing to the correct book.

Validation: This test requires real-user simulation in variable lighting and shelf layouts. The tester evaluates spatial accuracy, UI validation, and how well the AR system handles depth and clutter. These are conditions automation cannot consistently assess, making this a key manual test execution scenario for AR-based interfaces.

Example 4: Voice-Controlled Surgical Dashboard (Healthcare)

Objective: Ensure commands are executed clearly and safely in noisy environments.

Steps:

1. Use a voice input: “Increase suction to level 4.”
2. Confirm that only the intended action is executed: No duplicate or incorrect responses.
3. Repeat the test while playing simulated background noise typical of an operating room.

Validation: A manual tester, ideally with clinical context, uses session-based testing to observe system behavior under pressure. The goal is to catch edge case detection failures and evaluate whether the voice interface responds precisely and safely. Error guessing is applied here to anticipate what might go wrong with similar-sounding commands or partial audio.

Example 5: Carbon Credit Marketplace (Regulatory Logic)

Objective: Ensure transactions meet audit and compliance standards across jurisdictions.

Steps:

1. Simulate a cross-border carbon credit transfer between two entities.
2. Check if the system raises real-time compliance alerts aligned with ISO 14064 standards.
3. Manually trace the transaction’s data lineage, including timestamps, user IDs, and regulatory flags.

Validation: This test uses manual test execution to verify every step in the transaction flow. The tester performs heuristic evaluation to validate regulatory logic and detect any gaps in audit readiness. Since regulations shift by region, compliance verification depends on manual checks and interpretation—something automated scripts can’t handle reliably.

Your 2025 Manual Test Case Template

Standard fields won’t cut it anymore. A modern manual test case template needs to handle edge case detection, ethical risk, hardware inputs, and real-world behavior, all areas where automation falls short.

If you’re building or reviewing a test case example for manual testing, this format will help teams document work clearly while supporting manual test execution in complex systems.

Key Fields to Include:

1. Exploratory Charter

Defines the tester’s focus during a session

Example: “Explore voice misfires in multilingual inputs.” This supports session-based testing and helps align exploratory goals with product risk.

2. Ethical Risk Rating

Use a simple scale (High, Medium, Low) to flag risk levels based on feature sensitivity. This is critical for areas like human observation testing in AI chatbots or medical tools.

3. Hardware Dependency Map

List sensors, actuators, and interfaces involved. Helps testers simulate real-user scenarios in hardware software sync environments like smart appliances or XR devices.

4. Compliance Checkpoints

Map test goals to legal or regulatory obligations

Example: “EU AI Act Article 22: Human oversight must be present.” This field supports compliance verification and aligns QA efforts with current policies.

5. Human Observation Notes

Leave space for free-form tester input such as tone mismatches, UI glitches, and unexpected system responses. This field is essential when testing manual testing sample test cases for emotional tone, physical gestures, or multimodal feedback.

Use this template to bring consistency to your manual testing efforts while capturing the kind of judgment-based feedback automation can’t produce.

Manual Testing Best Practices for 2025

Modern tech demands modern techniques. These practices go beyond checklists and support smarter manual test execution, especially when working with AI tools, smart devices, and compliance-heavy products.

Best Practice #1: Use Session-Based Test Management

Structure matters. Break your manual testing sample test cases into 45 to 90-minute focused sessions. This improves tester concentration and gives room for real exploratory testing scenarios without burnout. You’ll get deeper insights from a single session than an all-day cycle of checkboxes.

Best Practice #2: Actively Hunt for Bias

Don’t wait for users to report it. Bias shows up subtly in AI replies, personalization algorithms, and voice recognition. Design test inputs that vary gender, dialect, or age profile. This is where human observation testing beats automation, judging tone, implication, and fairness.

Best Practice #3: Include Hardware in Loop Testing

Real-world behavior can’t be simulated in code alone. Always test with connected hardware, sensors, switches, displays, when using hardware software sync systems. A delayed signal, overheating component, or laggy UI could create high-risk issues that only show up physically.

Best Practice #4: Use Probabilistic Pass Criteria

AI outputs often change slightly with the same input. So instead of pass/fail, score the accuracy. 

For example: “9 out of 10 commands correctly interpreted.” This helps detect edge case detection failures in voice, gesture, or emotion-based inputs.

Detailed Table for Manual Testing Best Practices

These practices sharpen every manual test case template you create. They give your team the structure, flexibility, and insight needed for ethical, compliant, and high-quality product releases.

How BotGauge Enhances Manual Testing

BotGauge is one of the few AI testing agents built to support both automation and manual test execution workflows. It stands apart from traditional manual testing sample test cases tools by combining flexibility, adaptability, and speed without needing large teams or weeks of setup.

Our autonomous agent has already built over one million test cases across multiple industries. The founders bring over a decade of QA experience and have translated that knowledge into one of the most capable platforms in the market.

Key features include:

• Natural Language Test Creation: You write plain English instructions. BotGauge turns them into structured test cases instantly. This helps testers move faster, especially when working with exploratory testing scenarios.
• Self-Healing Capabilities: When your app changes, BotGauge updates the test cases in real time. It removes the need to rewrite broken tests after every UI tweak.
• Full Stack Test Coverage: From UI flows to API endpoints and database triggers, it supports end-to-end testing across all layers. This pairs well with manual test case templates that require coverage beyond automation.

BotGauge supports AI-assisted workflows that accelerate both planning and execution. It does not replace manual testers. It helps them focus on what matters most: judgment, accuracy, and context.

Explore more BotGauge’s testing features → BotGauge

Conclusion

Manual test case generation means designing and running tests by hand, using human judgment to assess real behavior. It is essential for catching issues in AI, voice, and hardware-based systems.

But the process is slow, repetitive, and scattered. Testers lose time writing steps, logging results, and managing compliance checks manually.

That delay leads to skipped tests, missed edge cases, and risky releases, especially in critical systems like chatbots or connected devices.

BotGauge solves this. You write the scenario in plain English. It builds the case, updates it when things change, and maps it to coverage and risk.Let’s connect today and use BotGauge’s 1M+ prebuilt test cases to streamline your QA.