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Can AI Generate Useful Messages for Smoking Cessation Campaigns? A Test with Different Emotional Appeals and Source Attribution

Year: 2025

Type: article

Abstract: This study investigates the viability of using ChatGPT 3.5 to produce smoking cessation messages featuring different emotional appeals. The effect of source attribution to Artificial Intelligence (AI) vs. human experts is also examined. A sample of current smokers (N = 480) was recruited from Prolific and randomly assigned to read one of five ChatGPT-generated messages reflecting a 2 (appeal: threat vs. humor) × 2 (source: AI vs. human experts) factorial design plus an irrelevant message control condition. Exposure to the smoking cessation messages led to a pattern of cognitive and emotional responses largely consistent with expectations based on previous research. Compared to control, the smoking cessation messages generated greater risk perceptions on the featured health effects but did not produce significantly stronger intentions to quit. Human experts as the source produced greater perceived source credibility than AI, but there was no source effect on other outcomes. No interaction between message appeals and source attribution was observed. Implications of the findings for tobacco education campaigns are discussed.

Source: International Journal of Environmental Research and Public Health

DOI: 10.3390/ijerph22101540

Authors: Wan-Lun Chang, Xiaomei Cai, Xiaoquan Zhao

Institution: George Mason University

Language: English

Topic: Smoking Behavior and Cessation

Subfield: Physiology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: gold

Estimated APC paid: $3,016