12 Introduction

When it comes to data collection, the approach you take depends entirely on your research paradigm: quantitative, qualitative, or mixed methods. Each has its own logic, guiding principles, and preferred ways of gathering information. What counts as data in your study isn’t just out there waiting to be collected, it’s shaped by your research questions, perspective, and methodology (Merriam, 2009). For example, if you’re working within a qualitative framework, your data might come from interviews, observations, or documents, capturing detailed descriptions and personal insights.

In contrast, a quantitative approach focuses on numbers, measurements, and statistical analysis. The way you gather and interpret information depends on the choices you make as a researcher. Data collection isn’t just about recording what you see or hear, it’s about deciding what matters for your study and how you’ll make sense of it (Merriam, 2009).

In this section, we turn our attention to two widely used methods of data collection: interviews and surveys. These approaches may differ in structure and purpose, but both benefit from the thoughtful integration of AI tools. We’ll explore what each method involves, the typical procedures researchers follow, and how AI can support and improve each step along the way. We begin with interviews, a cornerstone of qualitative research.