The digital gambling ecosystem requires combining informational accuracy with emotional management in public policy communication for Responsible Gambling (RG). After auditing the three emails that mark critical moments in a national operator's RG protocol, we compare two redesigns: a human-designed version (derived from a literature review, interviews, and focus groups) and an AI-generated version (derived from regulatory requirements). In an experiment with a mixed (between- and within-subjects) design, participants rated three responsible gambling emails, all in the same format (original, human-designed, or AI-generated), on format, content, appropriateness, and appeal (0–7), as well as emotional valence, reactance, and erroneous beliefs (winning probability, gambler's fallacy, illusion of control). Analyses treating the participant as the unit of inference (participant-level averages across the three emails within each Condition) show that both proposals (human-designed and AI-generated) outperform the original emails on perceived quality; however, strict AI–human equivalence on quality was not established under a conservative margin. Valence was comparable across proposals, while reactance was slightly higher for the AI-generated emails than for the human-designed ones. For beliefs, AI and human versions did not differ on winning probability or gambler's fallacy. By contrast, illusion of control was higher for the AI-generated proposal than for the human-designed proposal and exceeded the originals. In this dataset, the evidence is consistent with a cautious, hybrid (human-in-the-loop) approach to AI-assisted drafting in public policy communication: large language models can produce clear, non-threatening drafts, but expert editing is needed to curb illusion-of-control cues and to monitor reactance. Because AI–human equivalence was not established under the conservative margin and inferential precision is limited, any equivalence-related interpretation should be treated as provisional pending replication with larger samples and repeated-exposure designs.
Mejías-Martínez et al. (Sat,) studied this question.