MSc Dissertation: AI Tools and Developer Roles

Problem

AI tools like GitHub Copilot and Claude Code are rapidly changing how developers work, but little research exists on their actual impact on day-to-day development practices and career trajectories.

Solution

Mixed-methods study combining academic literature review with developer interviews to understand real-world transformation patterns.

My Role

Principal Researcher & Author

Methods

• Qualitative Research
• Literature Review
• Thematic Analysis
• Semi-structured Interviews

Overview

"Rewriting the Developer Professional Identity: How AI Tools Transform Software Engineering Roles" examines how AI coding assistants are reshaping the software engineering profession. The research combines analysis of 70 academic papers with 14 in-depth developer interviews (7 hours total) to understand how these tools affect workflows, skill requirements, and career development.

Completed for MSc in Managing Information Systems & Digital Innovation at LSE, achieving Distinction.

Dissertation

Research Approach

Literature Review (70 Papers)

• Analyzed existing research on AI-human collaboration
• Reviewed professional identity theory and technology adoption frameworks
• Examined software engineering practice evolution studies
• Synthesized findings on AI tool impact across industries

Developer Interviews (14 Participants, 7 Hours)

• Semi-structured interviews with developers across experience levels
• Thematic analysis identifying transformation patterns
• Focus on actual workflow changes, not just perceptions
• Explored adoption barriers and success factors

Key Research Findings

Workflow Changes Developers spend less time writing boilerplate code, more time on architecture and problem decomposition. AI tools handle routine tasks while developers focus on system design and requirement translation.

Skill Evolution Traditional coding skills remain valuable but shift in application. New competencies emerging around prompt engineering, AI output validation, and knowing when to use versus avoid AI assistance.

Professional Identity Developers redefining their role from "code writers" to "AI collaborators" and "solution architects." Mixed reactions: some see enhanced capabilities, others worry about skill atrophy and career vulnerability.

Adoption Patterns Productivity gains drive adoption, but concerns about over-reliance and code quality create resistance. Organizational culture and peer influence significantly impact individual adoption decisions.

Why This Matters for Product Management

User Research Skills Conducted 14 in-depth interviews, extracting insights from complex, technical conversations and identifying clear patterns across diverse perspectives.

Data Synthesis Analyzed 70 academic papers to build comprehensive understanding of emerging field, demonstrating ability to quickly master complex domains through research.

Problem Identification Identified gap between AI tool capabilities and developer education, revealing product opportunity for better onboarding and training solutions.

Technical Understanding Deep knowledge of developer workflows, pain points, and decision-making processes—essential for building developer-facing products.

Strategic Thinking Research reveals how technology transforms professional roles, applicable to understanding customer needs in any AI-driven product category.

Practical Applications

• Framework for understanding how AI tools affect knowledge workers (transferable to other domains)
• Insights into developer decision-making valuable for dev tool product teams
• Identified specific workflow friction points where product solutions could add value
• Understanding of what drives adoption versus resistance in technical user bases

Research Quality

• Distinction grade from LSE
• Rigorous mixed-methods approach
• Published methodology and analysis framework
• Supervisor commendation for practical relevance and theoretical contribution

Additional Documentation