ChatGPT’s memory feature is designed to provide users with greater control and more helpful responses. Yet, it remains unclear how users perceive this feature in relation to privacy. To address this gap, we conducted interviews with 20 ChatGPT users from diverse backgrounds. Our findings revealed four major characteristics that distinguish ChatGPT’s memory from human memory: perceived unforgetfulness, detailedness, accuracy, and lack of emotions, highlighting the machine-like nature of AI memory. Moreover, both ChatGPT’s memory and human memory were perceived as beneficial for relationship building. Notably, most participants experienced negative expectancy violations after learning what ChatGPT remembered about them. They expressed a strong need for greater visibility, accessibility, transparency, and user control in the design of future memory features. Drawing on users’ suggestions and theoretical frameworks on privacy management, we provide design implications for developing a more transparent, responsible, and user-aligned memory experience that helps them navigate privacy-personalization trade-offs when interacting with LLM-based memories.

The use of ICTs to enhance education is challenging, especially when institutions lack the resources to build efficient technologies. The use of ICTs, however, can facilitate processes and communication—elements that are essential to create a supportive school environment and enhance student learning motivation. In this study, we assess the effectiveness of OdontoApp,1 a tool created using a free-service provider with the goal of improving communication and centralizing processes in a dentistry program at a university in Ecuador, using Self-Determination Theory and the Motivational Technology Model as theoretical frameworks. Through a survey study (N = 114), we found that features of the App that enhance a sense of relatedness increase students’ learning motivation. However, the benefits of the app are contingent on students’ acceptance of learning technologies and how promptly they receive answers to their queries.

Fear of artificial intelligence (AI) has become a predominant term in users’ perceptions of emerging AI technologies. Yet we have limited knowledge about how end users perceive different types of fear of AI (e.g., fear of artificial consciousness, fear of job replacement) and what affordances of AI technologies may induce such fears. We conducted a survey (N = 717) and found that while synchronicity generally helps reduce all types of fear of AI, perceived AI control increases all types of AI fear. We also found that perceived bandwidth was positively associated with fear of artificial consciousness, but negatively associated with fear of learning about AI, among other findings. Our study provides theoretical implications by adopting a multi-dimensional fear of AI framework and analyzing the unique effects of perceived affordances of AI applications on each type of fear. We also provide practical suggestions on how fear of AI might be reduced via user experience design.

Artificial intelligence (AI) applications have become an integral part of our society. However, studying AI as one entity or studying idiosyncratic applications separately both have limitations. Thus, this study used computational methods to categorize ten different AI roles prevalent in our everyday life and compared laypeople’s perceptions of them using online survey data (N = 727). Based on theoretical factors related to the fundamental nature of AI, the principal component analysis revealed two dimensions that categorize AI: human involvement and AI autonomy. K-means clustering identified four AI role clusters: tools (low in both dimensions), servants (high human involvement and low AI autonomy), assistants (low human involvement and high AI autonomy), and mediators (high in both dimensions). Multivariate analyses of covariances revealed that people assessed AI mediators the most and AI tools the least favorably. Demographics also influenced laypeople’s assessments of AI. The implications of these results are discussed.

Is the current state of fitness applications effective at motivating and satisfying the needs of Hispanic users? With most mHealth research conducted with a predominantly white population, the answer to this question is lacking. In this study, we address this question through a survey study with Hispanic users of fitness applications (N= 211) and use the Motivational Technology Model (MTM) and Self-Determination Theory (SDT) as theoretical frameworks. We found that using interactivity features is essential to inspire more autonomous forms of motivation to use fitness applications. This is because interactivity helps satisfy users’ needs for relatedness. However, interactivity also decreased autonomy and competence suggesting the need to design fitness applications that increase relatedness without compromising autonomy. Implications for the design of fitness applications for the population at large and Hispanics, in particular, are discussed.

Studies show that users do not reliably click more often on headlines classified as clickbait by automated classifiers. Is this because the linguistic criteria (e.g., use of lists or questions) emphasized by the classifiers are not psychologically relevant in attracting interest, or because their classifications are confounded by other unknown factors associated with assumptions of the classifiers? We address these possibilities with three studies—a quasi-experiment using headlines classified as clickbait by three machine-learning models (Study 1), a controlled experiment varying the headline of an identical news story to contain only one clickbait characteristic (Study 2), and a computational analysis of four classifiers using real-world sharing data (Study 3). Studies 1 and 2 revealed that clickbait did not generate more curiosity than non-clickbait. Study 3 revealed that while some headlines generate more engagement, the detectors agreed on a classification only 47% of the time, raising fundamental questions about their validity.

Online users' attitudes toward privacy are context-dependent. Studies show that contextual cues are quite influential in motivating users to disclose personal information. Increasingly, these cues are embedded in the interface, but the mechanisms of their effects (e.g., unprofessional design contributing to more disclosure) are not fully understood. We posit that each cue triggers a specific "cognitive heuristic" that provides a rationale for decision-making. Using a national survey (N = 786) that elicited participants' disclosure intentions in common online scenarios, we identify 12 distinct heuristics relevant to privacy, and demonstrate that they are systematically associated with information disclosure. Data show that those with a higher accessibility to a given heuristic are more likely to disclose information. Design implications for protection of online privacy and security are discussed.

Clickbait is an attractive yet misleading headline that lures readers to commit click-conversion. Development of robust clickbait detection models has been, however, hampered due to the shortage of high-quality labeled training samples. To overcome this challenge, we investigate how to exploit human-written and machine-generated synthetic clickbaits. We first ask crowdworkers and journalism students to generate clickbaity news headlines. Second, we utilize deep generative models to generate clickbaity headlines. Through empirical evaluations, we demonstrate that synthetic clickbaits by human entities and deep generative models are consistently useful in improving the accuracy of various prediction models, by as much as 14.5% in AUC, across two real datasets and different types of algorithms. Especially, we observe an improvement in accuracy, up to 8.5% in AUC, even for top-ranked clickbait detectors from Clickbait Challenge 2017. Our study proposes a novel direction to address the shortage of labeled training data, one of fundamental bottlenecks in supervised learning, by means of synthetic training data with reinforced domain knowledge. It also provides a solution for distinguishing between bot-generated and human-written clickbaits, thus aiding the work of moderators and better alerting news consumers.