🔸 F-IE-3: User Survey DataDive

User Survey and Data Collection for AI Code Assistants

Foundational Track - Introductory AI Explorations



Objective

The primary focus of this project is to develop students’ skills in User Survey and Data Collection, crucial for careers in STEM and AI fields. It also introduces UX research fundamentals, emphasizing user interviewing and persona building.

Students will engage with Generative AI-powered code assistants like AWS CodeWhisperer, GitHub Copilot, and Google Duet AI. They will use interviews, surveys, and trend analysis to understand user behaviors and motivations comprehensively.

Leveraging AI tools such as ChatGPT for data analysis, students will transform insights into actionable personas and design strategies. This practical application of AI technologies aims to streamline data processing and improve product development, culminating in the creation of intuitive, user-centered products.


Learning Outcomes

Upon successful completion of this project, students will:

  • Proficiency in Data Collection Techniques: Master diverse data collection methods, including user interviews and surveys, to gather comprehensive insights into trends and user experiences and motivations.

  • Data Transformation Proficiency: Gain proficiency in converting raw data from user interviews into structured, actionable insights. Employ AI technologies to assist in identifying patterns and deriving insights, thereby streamlining the transformation process from data collection to actionable outputs that inform product development and user-centered design.


Steps and Tasks

1. Understanding UX Research and Data Collection

Begin by exploring the fundamentals of UX research, focusing on how it integrates into broader data collection and analysis efforts in product development. Learn about user experience principles and how they help gather actionable insights. Start with the What is UX Research? from the Interaction Design Foundation.

2. Designing the Data Collection Framework

Set clear goals for your user interviews and surveys, emphasizing the collection of data that reveals user interactions with AI-powered tools. Develop a structured interview guide that aligns with these objectives, ensuring questions are targeted to uncover deep insights into user behaviors and needs as well as trends. For guidance on creating effective interview guides, check How to Write an Interview Guide.

3. Conducting Interviews and Surveys

Engage with users through structured interviews and widely distributed surveys to collect diverse data on user experiences with Generative AI code assistants. Use platforms like online forums to find participants. Learn more about conducting effective interviews at How to Conduct User Interviews.

:bulb: Note: If you find that you haven’t collected adequate data, consider joining the Code-Along Discussions. This will allow you to combine your data with that of other participants, enhancing the depth of insights you can generate.

4. Data Analysis and Synthesis

After collecting data, employ AI-assisted tools to transcribe and analyze the information. Use software to identify patterns and themes. This stage is crucial for translating raw data into a format that can be used to make informed decisions about product development.

5. Creating Actionable Personas

From the analyzed data, construct detailed user personas that represent various user segments. These personas should include demographic details, user goals, preferences, and pain points. Utilize these personas to guide the design and development of solutions that meet actual user needs. Refer to User persona examples for templates and inspiration.

6. Developing User Journey Maps

Illustrate the complete user interaction with the AI-powered code assistants through journey maps. Each map should capture key touchpoints, emotional experiences, and potential areas for enhancing user satisfaction. This visual tool helps in understanding and improving the overall user experience. See Journey map examples for how to create effective maps.


Evaluation

Self Evaluation for AI Mentor/Evaluator Conversation

Prepare for your discussion with the AI mentor by reflecting on these key areas, tailored to enhance your understanding of data collection and analysis within the project:

  • Data Collection Techniques:

    • Methodology Selection: Reflect on your choice of data collection methods (interviews, surveys, trend analysis) and how effectively these methods aligned with the project goals.

    • Execution and Adaptability: Evaluate the execution of your data collection strategies. Discuss any challenges you faced and how you adapted your methods in response to these challenges.

    • Quality of Data Gathered: Consider the quality of the data collected. How did you ensure the reliability and validity of the data? Discuss any improvements you implemented to enhance data quality.

  • Data Analysis Proficiency:

    • Technique Application: Detail the analytical techniques used to process and interpret the collected data. Highlight how you utilized AI tools like ChatGPT for data analysis.

    • Pattern Recognition and Insight Generation: Reflect on your effectiveness in identifying patterns and deriving insights. Were there specific instances where data analysis led to unexpected findings or challenges?

  • Application of Insights:

    • From Data to Design: Discuss how the insights obtained from data analysis were translated into valuable insights and actionable strategies.

This structure will help you thoroughly evaluate your role and contributions in data-centric aspects of the project, setting a solid foundation for your mentor evaluation conversation.


Resources and Learning Materials

  • What is UX?
    An article by the Interaction Design Foundation that explains the basics of user experience research and its role in uncovering problems and design opportunities.

  • UX Research Methods by the Nielsen Norman Group.
    A comprehensive guide on UX research methods from the Nielsen Norman Group, a globally recognized UX research consulting firm.

  • Secondary Research
    An insightful article from dscout’s People Nerds blog that emphasizes the importance of secondary research in forming a foundational understanding of the research domain before moving on to primary, generative research.

  • How to Write an Interview Guide
    A detailed resource from the Nielsen Norman Group on crafting effective interview guides for user interviews.

  • Generative Research Guide
    A complete guide from dscout’s People Nerds blog that explains the concept of generative research, its importance, and how to conduct it.

  • Primary vs Secondary Research
    An article from Guide2Research that contrasts primary and secondary research, detailing when and why each type is used.

  • How to Design a Survey
    A guide from Pew Research Center that provides best practices and methodologies for designing effective surveys.

  • How to Conduct User Interviews
    Another valuable resource from the Nielsen Norman Group that shares effective strategies and techniques for conducting user interviews.

  • Affinity Mapping
    A resource by the Nielsen Norman Group that explains what affinity mapping is, and how it can be used to organize and analyze complex data in UX research.


This project aims to set the groundwork for students’ final internship by equipping them with a solid foundation in user interviewing and persona building, and their crucial role in the design and enhancement of a user-centered AI-powered code assistant.


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