Physical inactivity remains a critical global health issue, yet scalable strategies for sustained motivation are scarce. Conversational agents designed as simulated exercising peers (SEPs) represent a promising alternative, but their long-term impact is unclear. We report a six-month randomized controlled trial (N=280) comparing individuals exercising alone, with a human peer, or with a large language model-driven SEP. Results revealed a partnership paradox: human peers evoked stronger social presence, while AI peers provided steadier encouragement and more reliable working alliances. Humans motivated through authentic comparison and accountability, whereas AI peers fostered consistent, low-stakes support. These complementary strengths suggest that AI agents should not mimic human authenticity but augment it with reliability. Our findings advance human-agent interaction research and point to hybrid designs where human presence and AI consistency jointly sustain physical activity.

Possible side effects of using web job boards in the e-recruitment context, such as candidates dropping out from the hiring process, may emerge if these tools are not transparent about data usage, collection, and processing. In response, we developed a novel web job board designed to enhance transparency, simulating a job-matching recommender system. A qualitative study with 20 Italian participants, combining direct observation of the job board use with the Thinking Aloud protocol and interviews, examines participants’ privacy behaviours in terms of data disclosure and seclusion. Findings indicate a general willingness among participants to share personal data, except for information related to their identity. We found that both the design of the job board and the meanings ascribed by participants to data shaped their privacy behaviours. Features enhancing user understanding of data usage and control of privacy settings were positively received, underscoring the importance of design in fostering thoughtful engagement with job board technologies. We contribute to research on privacy behaviours in the context of job search and we draw suggestions from the study findings on how to design platforms that support data protection and allow safe and purposeful disclosure of personal data, sustaining job seekers throughout the recruitment process.

In recent years, various initiatives from within and outside the HCI field have encouraged researchers to improve research ethics, openness, and transparency in their empirical research. We quantify how the CHI literature might have changed in these three aspects by analyzing samples of 118 CHI 2017 and 127 CHI 2022 papers—randomly drawn and stratified across conference sessions. We operationalized research ethics, openness, and transparency into 45 criteria and manually annotated the sampled papers. The results show that the CHI 2022 sample was better in 18 criteria, but in the rest of the criteria, it has no improvement. The most noticeable improvements were related to research transparency (10 out of 17 criteria). We also explored the possibility of assisting the verification process by developing a proof-of-concept screening system. We tested this tool with eight criteria. Six of them achieved high accuracy and F1 score. We discuss the implications for future research practices and education.