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Paul Sherman

People and AI, At Work and Home

Research proposal for an ongoing qualitative investigation into how people use AI in their work and personal lives.

Introduction

People are increasingly integrating AI into their work and personal lives. This research examines the patterns, drivers, and effects of AI adoption and continued use.

Research Goals

This research examines how and why people integrate AI into their work, home, and social lives, and how they think and feel about AI as it becomes more deeply woven into daily routines. It explores these questions:

  • Why and how people decide to use AI in their work, personal, and social lives
  • How AI is changing their work life, home life, and social interactions
  • What benefits people are deriving from using or interacting with AI
  • What negative consequences people experience from using or interacting with AI
  • What concerns people have about AI use at work, at home, and in social settings

Core Research Questions

Drivers of AI Adoption

  • What motivates people to explore AI tools in the first place?
  • What AI tools have people tried, and what influenced those choices?
  • What factors lead people to adopt a tool for regular use vs. abandon it?
  • How do people decide whether to use AI for a specific task or situation?
  • What role do peers, organizational culture, or social pressure play in adoption decisions?

Evolution of AI Usage

  • How has people's AI usage changed over the past few months?
  • What patterns emerge in how people move from experimentation to regular use?
  • What triggers changes in how or how much people use AI?

Cognitive and Emotional Effects

  • How do people perceive AI has affected their thinking and problem-solving?
  • What benefits do people experience (e.g., faster work, expanded capabilities, reduced mental load)?
  • What concerns do people have (e.g., skill atrophy, dependency, reduced critical thinking)?
  • What emotions do people experience when using AI (e.g., excitement, anxiety, guilt, confidence)?
  • Have people had the experience of discovering they were interacting with AI only after the fact? How did they discover it? What reactions and emotions did they experience after discovering hidden AI? Did the experience cause them to change their attitude towards the organization that deployed the hidden AI? Towards AI in general? Did people change the way they use AI based on this experience?
  • How do these perceptions and feelings influence people's attitudes toward AI?

Changes to Work Practices

  • What specific tasks are people using AI for at work?
  • What work have they stopped doing, started doing, or do differently because of AI?
  • How has AI changed their daily workflows and routines?
  • What has AI enabled that wasn't possible or practical before?

Changes to Home Life

  • How are people using AI in their personal lives?
  • How has AI changed their daily activities, hobbies, or personal projects?
  • What effects (positive or negative) has AI had on their home life?

Social Dynamics in Work Settings

  • How do people navigate AI-generated work in collaborative contexts?
  • Do people disclose when they've used AI? When, how, and why (or why not)?
  • How does AI affect communication and coordination with colleagues?
  • What happens when people receive AI-generated work from others?
  • What informal rules or expectations are emerging around AI usage in their workplace?

Social Dynamics in Personal Settings

  • How has AI affected interactions with family, friends, or others in personal contexts?
  • Do people disclose AI usage differently in personal vs. work settings?
  • What social situations or relationships does AI enhance vs. complicate?

Benefits and Value

  • What concrete benefits have people experienced from using AI?
  • What value has surprised them or exceeded expectations?
  • How do they describe AI's impact on their productivity, creativity, or capabilities?

Challenges and Frustrations

  • What problems or frustrations have people encountered with AI?
  • What happens when AI doesn't work as expected or produces poor results?
  • What limitations have they discovered through use?
  • What concerns do they have about their own AI usage or AI's broader role?

Trust and Verification

  • How do people decide when to trust AI output?
  • How do they verify or validate AI-generated work?
  • What experiences have increased or decreased their trust in AI?
  • How has their approach to verification evolved over time?

Method

Sample

Target: Ongoing in-depth interviews

Duration: 30 minutes each

Format: Semi-structured interviews with video recording and pre-interview survey

Recruitment Criteria

Participants should have:

  • At least 6 months of active AI usage (work, personal, or both)
  • Experience with multiple AI tools or features
  • Diversity across: organization size (startup, growth-stage, enterprise), role type (individual contributor, manager, etc), industry/domain, age/generation (Gen Z, Millennial, Gen X), technical vs. non-technical roles, and level of AI usage (power users, moderate users, novice users)

Interview Structure

The interviews follow a moderator guide that covers:

  1. AI tool exploration and adoption journey
  2. Concrete workflow changes (work and personal)
  3. Organizational adoption landscape
  4. Successes and challenges
  5. Trust development and verification practices
  6. AI resistance and social dynamics
  7. Disclosure norms and professional identity
  8. Useful techniques and tacit AI expertise
  9. Skill erosion concerns
  10. Emotional responses and generational perspectives
  11. Gaps between current AI capability and actual needs

The guide uses behavioral interviewing techniques, asking for specific stories and examples rather than general opinions. See the Moderator Guide for the full protocol.

Data Collection

  • Interviews are recorded using Google Meet
  • Each recording is labeled with date, participant ID, and context notes
  • Google Meet transcriptions are collected for analysis
  • Key quotes and examples are flagged during review

Analysis

Thematic Analysis: Code transcripts for recurring themes across the core research questions. Identify patterns in tool adoption decisions, usage evolution, and social dynamics. Look for surprising or counter-intuitive findings. Note differences across segments (org size, role, generation, etc.).

Pattern Identification: Map the factors that influence tool adoption vs. abandonment. Categorize types of work practice changes. Identify emerging norms around AI disclosure and collaboration. Document trust calibration patterns and inflection points.

Hypothesis Generation: Surface specific questions and testable hypotheses for future studies. Identify segments or user types that warrant deeper exploration.

For details on how these analysis methods are implemented, see the Analysis Workflow.

Outputs

Deliverables

  • Synthesis with key findings and themes
  • Framework for understanding AI tool adoption patterns
  • Typology of work practice changes
  • Map of emerging social norms around AI usage and disclosure
  • List of questions and hypotheses for future research

Content Assets

  • A set of compelling narrative examples
  • Quote bank for social media and articles
  • Self-serve data exploration tools
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