AI

A Fast Track Open Access article published in the Health Behavior and Policy Review Journal.

Authors:

Michael Young, PhD, FAAHB
Tonychris Nnaka, PhD, MPH, RN

Objective:

Public discussion of generative artificial intelligence (AI) often emphasizes competition among tools or focuses on detecting and sanctioning AI-assisted writing. Such framing obscures a more productive question for scientific fields, i.e., How AI can be used responsibly to strengthen scholarship? Health behavior research is well positioned to lead this conversation because the field already emphasizes contextual analysis, methodological rigor, and researcher accountability for evidence.

Methods:

This paper reframes debates about AI by examining three common misconceptions: (1) that intelligent systems should not require context; (2) that AI assistance diminishes scholarly value; and (3) that AI use should primarily be detected and penalized.

Results:

Drawing on the authors’ experience using large language models as editorial and analytic assistants, and emerging publication-ethics guidance, we argue that responsible use depends on transparency, human oversight, and adherence to established scientific standards.

Conclusions:

Potential benefits include improving clarity, strengthening theory-measurement alignment, supporting methodological planning, and expanding capacity for researchers with limited resources. Generative AI cannot replace theory, domain expertise, or ethical judgment, but it can reduce barriers to producing high-quality work. Therefore, the central issue is not whether AI is used, but whether it is used well, and in ways that enhance rigor, credibility, equity, and policy impact.

Source: Health Behavior and Policy Review
Publisher: Paris Scholar Publishing Ltd.
Article Link: https://doi.org/10.14485/HBPR.13.2.1