For my research on how AI improves students’ academic writing skills, the phenomenological design best fits my study. Phenomenology focuses on exploring the lived ...
For my research on how AI improves students’ academic writing skills, the phenomenological design best fits my study. Phenomenology focuses on exploring the lived experiences of individuals regarding a specific phenomenon—in this case, students’ use of AI tools (e.g., ChatGPT, Grammarly) for writing tasks. I want to understand not just whether their scores improve, but how they experience the process, what difficulties they encounter, and how they make sense of AI’s role in their learning.
Strengths of this design include its ability to capture rich, detailed descriptions of students’ subjective experiences, revealing themes like frustration, over-reliance, or increased confidence. However, challenges include the need for careful bracketing (setting aside my own assumptions about AI) and the time-intensive process of in-depth interviews and transcription.
Example application: I would conduct semi-structured interviews with 8–10 university students who regularly use AI for writing. I would ask: “Can you describe a specific moment when AI helped you overcome a writing block?” and “What was that experience like for you?” After transcribing, I would identify common themes (e.g., “AI as a brainstorming partner” or “fear of losing my own voice”) to capture the essence of the phenomenon.