Honestly, it’s getting trickier to spot AI-generated regression results these days, but there are a few telltale signs if you know where to look. One big giveaway is the lack of depth in interpretation. You might see perfectly formatted tables with all the right numbers—coefficients, p-values, R-squared—but the explanation around them feels hollow, like it's just describing the stats instead of connecting them to the research question in a meaningful way.

Another thing is the language. AI tends to use very polished, formal phrasing but misses the nuance or curiosity you’d expect from a human researcher. It might explain what happened, but not why it matters.

Also, watch for missing pieces, no mention of assumptions being tested, no discussion of model fit beyond just listing numbers, or no sensitivity checks. That’s a red flag. Most experienced researchers will at least mention potential limitations or unusual findings, even briefly.

Finally, if something just feels... too perfect, too generic, or disconnected from the context, it’s worth questioning. AI can produce technically correct output, but it often lacks that spark of insight or real-world messiness that comes from digging into your data with a specific purpose.

Warm regards,

Koteshwar Ramesh Rakesh

Examine model assumptions, such as linearity, normal distribution, and homogeneity of variance, to ensure they are consistent with typical regression analysis. Then, analyze the presented statistical measures, such as the R-squared coefficient, p-values, and coefficients, checking for consistency and logical interpretation. It is essential to verify the model's validity using techniques such as cross-validation.

Factors need to be considered for best ways to identify whether a regression result is AI-generated:

Check for Consistency and Plausibility

Inspect the Explanation and Methodology

AI-specific frameworks

Evaluate the Residuals and Diagnostics

1). Check for Unusual Patterns or Anomalies

2).Look for Common AI Artifacts.

3).Examine the Statistical Output Itself.

4).Cross-Check with Reproducibility.

5).Use AI-Detection Tools.