Try : Insurtech, Application Development

AgriTech(1)

Augmented Reality(21)

Clean Tech(9)

Customer Journey(17)

Design(45)

Solar Industry(8)

User Experience(68)

Edtech(10)

Events(34)

HR Tech(3)

Interviews(10)

Life@mantra(11)

Logistics(5)

Manufacturing(3)

Strategy(18)

Testing(9)

Android(48)

Backend(32)

Dev Ops(11)

Enterprise Solution(33)

Technology Modernization(9)

Frontend(29)

iOS(43)

Javascript(15)

AI in Insurance(38)

Insurtech(66)

Product Innovation(58)

Solutions(22)

E-health(12)

HealthTech(24)

mHealth(5)

Telehealth Care(4)

Telemedicine(5)

Artificial Intelligence(153)

Bitcoin(8)

Blockchain(19)

Cognitive Computing(8)

Computer Vision(8)

Data Science(23)

FinTech(51)

Banking(7)

Intelligent Automation(27)

Machine Learning(48)

Natural Language Processing(14)

expand Menu Filters

How to Prepare a User Interview Questionnaire?

5 mins 10 secs read

The User Interview is a qualitative, non-binary, open-ended process that is a crucial contextual inquiry research method. And creating a good questionnaire is like sharpening your weapon to make the best impact.

A well-prepared, well-rehearsed user questionnaire is a must for an insightful interview.

Why make a questionnaire before a User Interview?

To avoid Response Biases*!

Seven-eight response biases influence user interviews greatly; understanding the user persona objectively is crucial for good design decisions. A thorough understanding of the response biases will help you use them or avoid them to get functional responses from the user.

*Note: There will be an upcoming blog on Response Biases and how to use or avoid them in research. Keep following Mantra Labs’s blog posts.

To make interviews more relevant to the research goals:

Planning an interview questionnaire keeps the facilitator on track during interviews. What does this mean? Multiple probing questions are asked during any user interview to gain more information and a deeper understanding of the participant’s behavior. Due to the probing, three situations occur.

1. The facilitator gets carried away in probing and deviates from the central questions under the influence of the participant’s response.

2. The facilitator asks questions spontaneously under the influence of their biases that might not be relevant to the research goals.

3. There is a chance of losing track of interview goals and shifting off-topic. Sometimes it’s a good idea to check all the possibilities that could influence the user’s behavior. However, there should be a clear line at which to stop and come back to the main questions.

To make sure questions are open-ended and revised/reframed before interviews:

“Details are in the story,” and Open-ended questions allow participants to tell the story of their experience. Since participants’ responses should not be influenced or bound to respond in a certain way, it is crucial to plan open-ended questions.

E.g., a pet product company is researching the possibility of their product entering the market and developing some pet grooming products for millennial pet owners. Let’s look at some 

questions to understand how open-ended they are.

Q 1: Would you use shampoo to make your dog smell better?

Q 1.1: What shampoo would you use to make your dog smell better?

Problem with these questions: 

Q 1: It’s a Yes/No question that will not give any qualitative insights.

       This question has little scope to know all the products the participant uses for grooming the dog.

Q 1.1: This is better than Q.1 because it has the scope of answering about shampoo products, but more is needed to know every detail of the pet cleaning-care habits of the user.

This can be a follow-up question to dig deeper, but not the main question.

What would be an excellent open-ended question?

Q. 1: What does your pet grooming process look like, and what products do you use while grooming?

Now this question has scope for the participant to tell the story of their pet’s grooming, including their process, the product they use, and how everything impacts their pet’s grooming pattern.

To avoid repetitive/similar questions:

If the questions still need to be pre-prepared and revised, the facilitator often asks questions similar to prior questions. This scenario causes a waste of time, sometimes irritating the participants as well.

To limit the number of questions and make them effective per the interview time limit.

  1. The time taken in an interview significantly impacts the qualitative information received from the participants. 40-45 minutes is the sweet spot for an interview. More than that, it starts becoming too much for the participant. If the facilitator continues to engage, the participant might get disengaged and be in a hurry to finish the interview.
  2. If it takes less than 40 minutes, you might not get a deep understanding of the question due to needing more probing in the response received from the participant.

How to make a good User Interview questionnaire?

Now that we know why, a good questionnaire is crucial before the research interview. There are some parameters and ways to write a good questionnaire.

Defining the business goal and user goal:

Keep stakeholder analysis as the first priority before writing the questionnaire. It’s crucial to know the business goals of the critical stakeholders to lay the foundation for your research interview. Also, try to understand and write the User Goal according to key stakeholders and their target personas.

Doing secondary research and gaining more information about the products, services, and business: 

It helps the facilitator make questions more relevant to the topic. The planning of the questions would be about the product and the service, which would help to find impactful insights. Here are some matters to focus on while doing secondary research:

  •  Analytics of the website (whatever possible, Google Analytics, Hotjar, Similarweb), target personas, user journey, pain points, what are your most trafficked pages, which site pages rank high in SERPs, visits from organic sources, traffic referrals from other sites and channels, Traffic from direct URL into the search bar, Devices used by the traffic ETC.)
  • Top performing keywords
  • How long do people typically spend on your website?
  • Page load time of the site?
  • Competitors analysis
  • User Personas
  • Geolocations of major user bases
  • Industry trends
  • The existing research paper regards the context
  • Existing customer journey map (If any)

Write the User Interview objective and key result properly:

This is the foundation of any research. Defining the OKR of the interview will keep your approach more constructive. The researcher should define why they are interviewing and what they want to achieve. Later, the questions should be formed in order to get the relevant information with regard to the interview objective.

The questions need to be framed in four categories that go in order one after another during the interview:

Intro Question and Ice Breakers

* Hi, How does your day/week go in Mantra Labs?

* Could you tell us about your role, followed by what your team does at Mantra Labs?

* How do you plan for your certain work to achieve your ‘X’ target?

Topic Specific Questions

* How do clients approach you and vice-versa? 

* Could you describe a couple of scenarios where you failed to perform the task and how it happened?

* How do you overcome obstacles when performing such tasks?

Opportunity specific questions

* What is making a good impact on existing clients during the project?

Opinionated questions

* In your opinion, where would you suggest this service should improve?

Conclusion

User Interview Research in UX is crucial to making informed decisions to solve complicated problems for any product or service. As it is essential, it needs to be understood. 

What does it mean? How should it be done? Also, when should it be done? But above all, the biggest problem with interviewers is a need for more technical and experiential knowledge of how to prepare Research Interview Questionnaires.

To do that, Interviews need to understand fundamental response biases, which can destroy the creation of the questionnaire and interview insights. Do thorough secondary research about the matter. Next, review the business goals and write the interview research goals and OKRs. And finally, have a structured questionnaire covering all types of questions. 

About Author,

Vijendra is currently working as a Sr. UX Designer at Mantra Labs. He is passionate about UXR and Product Design.

Cancel

Knowledge thats worth delivered in your inbox

AI Code Assistants: Revolution Unveiled

AI code assistants are revolutionizing software development, with Gartner predicting that 75% of enterprise software engineers will use these tools by 2028, up from less than 10% in early 2023. This rapid adoption reflects the potential of AI to enhance coding efficiency and productivity, but also raises important questions about the maturity, benefits, and challenges of these emerging technologies.

Code Assistance Evolution

The evolution of code assistance has been rapid and transformative, progressing from simple autocomplete features to sophisticated AI-powered tools. GitHub Copilot, launched in 2021, marked a significant milestone by leveraging OpenAI’s Codex to generate entire code snippets 1. Amazon Q, introduced in 2023, further advanced the field with its deep integration into AWS services and impressive code acceptance rates of up to 50%. GPT (Generative Pre-trained Transformer) models have been instrumental in this evolution, with GPT-3 and its successors enabling more context-aware and nuanced code suggestions.

Image Source

  • Adoption rates: By 2023, over 40% of developers reported using AI code assistants.
  • Productivity gains: Tools like Amazon Q have demonstrated up to 80% acceleration in coding tasks.
  • Language support: Modern AI assistants support dozens of programming languages, with GitHub Copilot covering over 20 languages and frameworks.
  • Error reduction: AI-powered code assistants have shown potential to reduce bugs by up to 30% in some studies.

These advancements have not only increased coding efficiency but also democratized software development, making it more accessible to novice programmers and non-professionals alike.

Current Adoption and Maturity: Metrics Defining the Landscape

The landscape of AI code assistants is rapidly evolving, with adoption rates and performance metrics showcasing their growing maturity. Here’s a tabular comparison of some popular AI coding tools, including Amazon Q:

Amazon Q stands out with its specialized capabilities for software developers and deep integration with AWS services. It offers a range of features designed to streamline development processes:

  • Highest reported code acceptance rates: Up to 50% for multi-line code suggestions
  • Built-in security: Secure and private by design, with robust data security measures
  • Extensive connectivity: Over 50 built-in, managed, and secure data connectors
  • Task automation: Amazon Q Apps allow users to create generative AI-powered apps for streamlining tasks

The tool’s impact is evident in its adoption and performance metrics. For instance, Amazon Q has helped save over 450,000 hours from manual technical investigations. Its integration with CloudWatch provides valuable insights into developer usage patterns and areas for improvement.

As these AI assistants continue to mature, they are increasingly becoming integral to modern software development workflows. However, it’s important to note that while these tools offer significant benefits, they should be used judiciously, with developers maintaining a critical eye on the generated code and understanding its implications for overall project architecture and security.

AI-Powered Collaborative Coding: Enhancing Team Productivity

AI code assistants are revolutionizing collaborative coding practices, offering real-time suggestions, conflict resolution, and personalized assistance to development teams. These tools integrate seamlessly with popular IDEs and version control systems, facilitating smoother teamwork and code quality improvements.

Key features of AI-enhanced collaborative coding:

  • Real-time code suggestions and auto-completion across team members
  • Automated conflict detection and resolution in merge requests
  • Personalized coding assistance based on individual developer styles
  • AI-driven code reviews and quality checks

Benefits for development teams:

  • Increased productivity: Teams report up to 30-50% faster code completion
  • Improved code consistency: AI ensures adherence to team coding standards
  • Reduced onboarding time: New team members can quickly adapt to project codebases
  • Enhanced knowledge sharing: AI suggestions expose developers to diverse coding patterns

While AI code assistants offer significant advantages, it’s crucial to maintain a balance between AI assistance and human expertise. Teams should establish guidelines for AI tool usage to ensure code quality, security, and maintainability.

Emerging trends in AI-powered collaborative coding:

  • Integration of natural language processing for code explanations and documentation
  • Advanced code refactoring suggestions based on team-wide code patterns
  • AI-assisted pair programming and mob programming sessions
  • Predictive analytics for project timelines and resource allocation

As AI continues to evolve, collaborative coding tools are expected to become more sophisticated, further streamlining team workflows and fostering innovation in software development practices.

Benefits and Risks Analyzed

AI code assistants offer significant benefits but also present notable challenges. Here’s an overview of the advantages driving adoption and the critical downsides:

Core Advantages Driving Adoption:

  1. Enhanced Productivity: AI coding tools can boost developer productivity by 30-50%1. Google AI researchers estimate that these tools could save developers up to 30% of their coding time.
IndustryPotential Annual Value
Banking$200 billion – $340 billion
Retail and CPG$400 billion – $660 billion
  1. Economic Impact: Generative AI, including code assistants, could potentially add $2.6 trillion to $4.4 trillion annually to the global economy across various use cases. In the software engineering sector alone, this technology could deliver substantial value.
  1. Democratization of Software Development: AI assistants enable individuals with less coding experience to build complex applications, potentially broadening the talent pool and fostering innovation.
  2. Instant Coding Support: AI provides real-time suggestions and generates code snippets, aiding developers in their coding journey.

Critical Downsides and Risks:

  1. Cognitive and Skill-Related Concerns:
    • Over-reliance on AI tools may lead to skill atrophy, especially for junior developers.
    • There’s a risk of developers losing the ability to write or deeply understand code independently.
  2. Technical and Ethical Limitations:
    • Quality of Results: AI-generated code may contain hidden issues, leading to bugs or security vulnerabilities.
    • Security Risks: AI tools might introduce insecure libraries or out-of-date dependencies.
    • Ethical Concerns: AI algorithms lack accountability for errors and may reinforce harmful stereotypes or promote misinformation.
  3. Copyright and Licensing Issues:
    • AI tools heavily rely on open-source code, which may lead to unintentional use of copyrighted material or introduction of insecure libraries.
  4. Limited Contextual Understanding:
    • AI-generated code may not always integrate seamlessly with the broader project context, potentially leading to fragmented code.
  5. Bias in Training Data:
    • AI outputs can reflect biases present in their training data, potentially leading to non-inclusive code practices.

While AI code assistants offer significant productivity gains and economic benefits, they also present challenges that need careful consideration. Developers and organizations must balance the advantages with the potential risks, ensuring responsible use of these powerful tools.

Future of Code Automation

The future of AI code assistants is poised for significant growth and evolution, with technological advancements and changing developer attitudes shaping their trajectory towards potential ubiquity or obsolescence.

Technological Advancements on the Horizon:

  1. Enhanced Contextual Understanding: Future AI assistants are expected to gain deeper comprehension of project structures, coding patterns, and business logic. This will enable more accurate and context-aware code suggestions, reducing the need for extensive human review.
  2. Multi-Modal AI: Integration of natural language processing, computer vision, and code analysis will allow AI assistants to understand and generate code based on diverse inputs, including voice commands, sketches, and high-level descriptions.
  3. Autonomous Code Generation: By 2027, we may see AI agents capable of handling entire segments of a project with minimal oversight, potentially scaffolding entire applications from natural language descriptions.
  4. Self-Improving AI: Machine learning models that continuously learn from developer interactions and feedback will lead to increasingly accurate and personalized code suggestions over time.

Adoption Barriers and Enablers:

Barriers:

  1. Data Privacy Concerns: Organizations remain cautious about sharing proprietary code with cloud-based AI services.
  2. Integration Challenges: Seamless integration with existing development workflows and tools is crucial for widespread adoption.
  3. Skill Erosion Fears: Concerns about over-reliance on AI leading to a decline in fundamental coding skills among developers.

Enablers:

  1. Open-Source Models: The development of powerful open-source AI models may address privacy concerns and increase accessibility.
  2. IDE Integration: Deeper integration with popular integrated development environments will streamline adoption.
  3. Demonstrable ROI: Clear evidence of productivity gains and cost savings will drive enterprise adoption.
  1. AI-Driven Architecture Design: AI assistants may evolve to suggest optimal system architectures based on project requirements and best practices.
  2. Automated Code Refactoring: AI tools will increasingly offer intelligent refactoring suggestions to improve code quality and maintainability.
  3. Predictive Bug Detection: Advanced AI models will predict potential bugs and security vulnerabilities before they manifest in production environments.
  4. Cross-Language Translation: AI assistants will facilitate seamless translation between programming languages, enabling easier migration and interoperability.
  5. AI-Human Pair Programming: More sophisticated AI agents may act as virtual pair programming partners, offering real-time guidance and code reviews.
  6. Ethical AI Coding: Future AI assistants will incorporate ethical considerations, suggesting inclusive and bias-free code practices.

As these trends unfold, the role of human developers is likely to shift towards higher-level problem-solving, creative design, and AI oversight. By 2025, it’s projected that over 70% of professional software developers will regularly collaborate with AI agents in their coding workflows1. However, the path to ubiquity will depend on addressing key challenges such as reliability, security, and maintaining a balance between AI assistance and human expertise.

The future outlook for AI code assistants is one of transformative potential, with the technology poised to become an integral part of the software development landscape. As these tools continue to evolve, they will likely reshape team structures, development methodologies, and the very nature of coding itself.

Conclusion: A Tool, Not a Panacea

AI code assistants have irrevocably altered software development, delivering measurable productivity gains but introducing new technical and societal challenges. Current metrics suggest they are transitioning from novel aids to essential utilities—63% of enterprises now mandate their use. However, their ascendancy as the de facto standard hinges on addressing security flaws, mitigating cognitive erosion, and fostering equitable upskilling. For organizations, the optimal path lies in balanced integration: harnessing AI’s speed while preserving human ingenuity. As generative models evolve, developers who master this symbiosis will define the next epoch of software engineering.

Cancel

Knowledge thats worth delivered in your inbox

Loading More Posts ...
Go Top
ml floating chatbot