For my research on students’ perceptions of AI tools in linguistics studies, the most suitable qualitative design would be phenomenology. This approach focuses on exploring...
For my research on students’ perceptions of AI tools in linguistics studies, the most suitable qualitative design would be phenomenology. This approach focuses on exploring individuals’ lived experiences, which aligns well with my goal of understanding how students perceive and interact with AI tools in their learning process. Phenomenology allows me to go beyond surface-level responses and examine how students experience AI in terms of usefulness, challenges, and impact on their learning. For example, instead of only measuring satisfaction through surveys, I can explore how students actually feel when using AI tools—whether they feel more confident, dependent, or even confused. One major strength of this design is that it provides rich, in-depth insights into students’ experiences, helping to reveal meanings that quantitative data cannot capture. However, a key challenge is that data collection and analysis can be time-consuming, especially when conducting and interpreting interviews. Additionally, findings may not be easily generalized to a larger population. In practice, I might conduct semi-structured interviews with a group of students who regularly use AI tools. I would ask them to describe specific situations where AI helped or hindered their learning. By analyzing these experiences, I can identify common themes that reflect how AI tools shape students’ learning in linguistics studies.