Future AI + Time and Boundary Management

As remote work becomes more prevalent, workers increasingly bear the responsibility of managing boundaries, time, and productivity without adequate system support. While prior research captured rich qualitative accounts of remote work experiences, making sense of these experiences at scale required careful synthesis to surface patterns across individuals and over time.

This project addressed the challenge of translating messy, longitudinal qualitative data into actionable insights that could inform the design of future productivity and support tools.

I served as the primary researcher responsible for analyzing and synthesizing data from a 14-day diary study and follow-up interviews. The study began with 35 participants, with 25 completing at least 10 days of diary entries. From this group, 15 participants were invited to participate in interviews, and 13 completed semi-structured interviews.

Diary entries were organized using affinity diagramming, and interview transcripts were iteratively coded using ATLAS.ti. Through multiple rounds of thematic analysis, I synthesized raw qualitative data into higher-level themes that captured recurring challenges, strategies, and tradeoffs remote workers faced when managing boundaries and time.

Based on these findings, I authored the thesis and developed a set of design implications and speculative directions for future AI-, ML-, and NLP-supported systems.

This project unfolded during a period of rapid change in the AI landscape—most notably, the public release of ChatGPT. As generative AI tools entered mainstream awareness mid-project, the analysis evolved to account for how emerging AI systems might realistically fit into remote workers' workflows.

Several constraints shaped the scope and execution of this project:

• Time and academic context: As an undergraduate thesis, the project prioritized research design, analysis rigor, and synthesis over large-scale deployment.

• Participant attrition: Longitudinal diary studies require sustained effort; retaining 25 participants for 10+ days represented a strong outcome given the study length and cognitive burden.

• Qualitative depth vs. breadth: The study favored rich, contextual insights over quantitative generalizability, trading scale for interpretive depth.

These constraints informed both the analytical approach and the framing of future system designs.

The analysis surfaced six core themes describing how remote work shifts responsibility onto workers, introduces forms of invisible labor, and creates both opportunities and tensions around autonomy and flexibility.

The resulting thesis provided a structured understanding of boundary and time management challenges in remote work and outlined concrete directions for future technologies that could reduce friction, support healthier routines, and better align organizational systems with human behavior.

This project strengthened my skills in qualitative research, longitudinal analysis, and research synthesis. Leading the analysis of a large, real-world dataset taught me how to move from raw qualitative data to coherent system-level insights, balance nuance with clarity, and translate research findings into design implications.

The experience continues to shape how I think about productivity tools, AI-assisted systems, and human-centered software design—especially in contexts where technology directly mediates daily work and well-being.