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7.2 From a Broad Topic to a Research Question and a Testable Hypothesis

◆ Key idea: a research question begins with a problem and a gap.
A good question does not begin with attractive wording. It begins with a clear problem, a
genuine gap, and operational definitions that establish who or what we will study, what
relationship or phenomenon is at stake, and how we will know the answer. AI can widen
the options, but the researcher decides what is important, feasible, and ethical.

Many projects begin with a broad topic such as “AI in health”, “digital marketing”, or “quality

of education”. A topic identifies a field, but it is not enough to design a study. We first need
a problem: what is happening or failing to happen? Who is affected? What are the

consequences? Then we need a gap: what does current knowledge fail to explain, what has
not been tested in this setting, or which decision lacks sufficient evidence?

A gap does not mean the complete absence of any study. It may be theoretical, when
findings exist without a coherent explanation; methodological, when the evidence rests on

a weak measurement instrument; contextual, when we do not know how far findings apply
to a different population; temporal, when the technology or the policy has changed; or
practical, when we do not know how knowledge turns into action. AI can propose types of

gap, but establishing one requires an actual review of the literature.

We then formulate a question that matches the kind of knowledge required. A descriptive
question asks about prevalence or characteristics. A comparative question compares two
groups or two alternatives. An associational question tests a relationship without claiming

causation. A causal question asks about the effect of an intervention or a factor and needs a
design that allows alternative explanations to be excluded. A qualitative question explores
experiences, meanings, and processes. An evaluation question asks how well a program was

carried out, what it achieved, what it cost, and in what context.

Frameworks such as population, intervention, comparison, and outcome can help with
intervention questions. A variant that replaces intervention with exposure suits
observational work, and other frameworks focusing on sample, phenomenon, design,

evaluation, and research type suit some qualitative questions. A framework is not a
compulsory template; its value lies in revealing a missing element, not in forcing every

question into one structure.

The quality of a question is also measured by whether it is feasible, interesting, novel, ethical,
and relevant. A system may generate a question that is linguistically new but not feasible
because of sample size or data access, or unethical because it requires collecting sensitive

information unnecessarily, or weakly relevant because it answers no real decision or
problem.

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