This paper explores the rising concern of utilizing Large Language Models (LLMs) in spear phishing message generation, and their performance compared to human-authored counterparts. Our pilot study compares the effectiveness of smishing (SMS phishing) messages created by GPT-4 and human authors, which have been personalized to willing targets. The targets assessed the messages in a modified ranked-order experiment using a novel methodology we call TRAPD (Threshold Ranking Approach for Personalized Deception). Specifically, targets provide personal information (job title and location, hobby, item purchased online), spear smishing messages are created using this information by humans and GPT-4, targets are invited back to rank-order 12 messages from most to least convincing (and identify which they would click on), and then asked questions about why they ranked messages the way they did. They also guess which messages are created by an LLM and their reasoning. Results from 25 targets show that LLM-generated messages are most often perceived as more convincing than those authored by humans, with messages related to jobs being the most convincing. We characterize different criteria used when assessing the authenticity of messages including word choice, style, and personal relevance. Results also show that targets were unable to identify whether the messages was AI-generated or human-authored and struggled to identify criteria to use in order to make this distinction. This study aims to highlight the urgent need for further research and improved countermeasures against personalized AI-enabled social engineering attacks.

Fox News White House correspondent Jacqui Heinrich has the latest on concerns over the president's mental and physical fitness on'Special Report.' Don't be surprised if your doctors start writing you overly friendly messages. They could be getting some help from artificial intelligence. New AI tools are helping doctors communicate with their patients, some by answering messages and others by taking notes during exams. Already thousands of doctors are using similar products based on large language models.

Automated coaching messages for weight control can save time and costs, but their repetitive, generic nature may limit their effectiveness compared to human coaching. Large language model (LLM) based artificial intelligence (AI) chatbots, like ChatGPT, could offer more personalized and novel messages to address repetition with their data-processing abilities. While LLM AI demonstrates promise to encourage healthier lifestyles, studies have yet to examine the feasibility and acceptability of LLM-based BWL coaching. 87 adults in a weight-loss trial rated ten coaching messages' helpfulness (five human-written, five ChatGPT-generated) using a 5-point Likert scale, providing additional open-ended feedback to justify their ratings. Participants also identified which messages they believed were AI-generated. The evaluation occurred in two phases: messages in Phase 1 were perceived as impersonal and negative, prompting revisions for Phase 2 messages. In Phase 1, AI-generated messages were rated less helpful than human-written ones, with 66 percent receiving a helpfulness rating of 3 or higher. However, in Phase 2, the AI messages matched the human-written ones regarding helpfulness, with 82% scoring three or above. Additionally, 50% were misidentified as human-written, suggesting AI's sophistication in mimicking human-generated content. A thematic analysis of open-ended feedback revealed that participants appreciated AI's empathy and personalized suggestions but found them more formulaic, less authentic, and too data-focused. This study reveals the preliminary feasibility and acceptability of LLM AIs, like ChatGPT, in crafting potentially effective weight control coaching messages. Our findings also underscore areas for future enhancement.

Advancements in artificial intelligence (AI) over the last decade demonstrate that machines can exhibit communicative behavior and influence how humans think, feel, and behave. In fact, the recent development of ChatGPT has shown that large language models (LLMs) can be leveraged to generate high-quality communication content at scale and across domains, suggesting that they will be increasingly used in practice. However, many questions remain about how knowing the source of the messages influences recipients' evaluation of and preference for AI-generated messages compared to human-generated messages. This paper investigated this topic in the context of vaping prevention messaging. In Study 1, which was pre-registered, we examined the influence of source disclosure on people's evaluation of AI-generated health prevention messages compared to human-generated messages. We found that source disclosure (i.e., labeling the source of a message as AI vs. human) significantly impacted the evaluation of the messages but did not significantly alter message rankings. In a follow-up study (Study 2), we examined how the influence of source disclosure may vary by the participants' negative attitudes towards AI. We found a significant moderating effect of negative attitudes towards AI on message evaluation, but not for message selection. However, for those with moderate levels of negative attitudes towards AI, source disclosure decreased the preference for AI-generated messages. Overall, the results of this series of studies showed a slight bias against AI-generated messages once the source was disclosed, adding to the emerging area of study that lies at the intersection of AI and communication.

Suddenly, the world is abuzz with chatter about chatbots. Artificially intelligent agents, like ChatGPT, have shown themselves to be remarkably adept at conversing in a very human-like fashion. ChatGPT, for instance, recently passed written exams at top business and law schools, among other feats both awe-inspiring and alarming. Researchers at Stanford University's Polarization and Social Change Lab and the Institute for Human-Centered Artificial Intelligence (HAI) wanted to probe the boundaries of AI's political persuasiveness by testing its ability to sway real humans on some of the hottest social issues of the day--an assault weapon ban, the carbon tax, and paid parental leave, among others. Indeed, AI-generated persuasive appeals were as effective as ones written by humans in persuading human audiences on several political issues," said Hui "Max" Bai, a postdoctoral researcher in the Polarization and Social Change Lab and first author on a new paper about the experiment in pre-print.

This study introduces and examines the potential of an AI system to generate health awareness messages. The topic of folic acid, a vitamin that is critical during pregnancy, served as a test case. Using prompt engineering, we generated messages that could be used to raise awareness and compared them to retweeted human-generated messages via computational and human evaluation methods. The system was easy to use and prolific, and computational analyses revealed that the AI-generated messages were on par with human-generated ones in terms of sentiment, reading ease, and semantic content. Also, the human evaluation study showed that AI-generated messages ranked higher in message quality and clarity. We discuss the theoretical, practical, and ethical implications of these results.