The rise of large language models for client-facing conversational search in healthcare necessitates evaluation frameworks that enable the assessment and comparison of these tools. Most such frameworks centre around the automated calculation of performance-related metrics and benchmarks. Though necessary, this focus fails to account for the human factors that impact the development, use, and adoption of these systems, as well as the factors specific to the healthcare context. Human evaluation frameworks attempt to address these drawbacks, but few such frameworks have been developed so far, and even fewer are those based on expert insight. In this work, we conduct semi-structured interviews with eleven healthcare professionals in health lifestyle care. From these interviews, we contribute a two-part healthcare domain expert evaluation framework, (K) Knowledge and (I) Interaction, which organises seven evaluation metrics. Our results reveal key understudied metrics for evaluation like (I1) Context-Seeking, (I2) Empathy, and (I3) Trustworthiness.

The rise of large language models for client-facing conversational search in healthcare necessitates evaluation frameworks that enable the assessment and comparison of these tools. Most such frameworks centre around the automated calculation of performance-related metrics and benchmarks. Though necessary, this focus fails to account for the human factors that impact the development, use, and adoption of these systems, as well as the factors specific to the healthcare context. Human evaluation frameworks attempt to address these drawbacks, but few such frameworks have been developed so far, and even fewer are those based on expert insight. In this work, we conduct semi-structured interviews with eleven healthcare professionals in health lifestyle care. From these interviews, we contribute a two-part healthcare domain expert evaluation framework, (K) Knowledge and (I) Interaction, which organises seven evaluation metrics. Our results reveal key understudied metrics for evaluation like (I1) Context-Seeking, (I2) Empathy, and (I3) Trustworthiness.

Increased levels of user control in learning systems is commonly cited as good AI development practice. However, the evidence as to the effect of perceived control over trust in these systems is mixed. This study investigated the relationship between different trust dimensions and perceived control in postgraduate student burnout support chatbots, and modelled the moderating factors therein. We present an in-between subject controlled experiment using simulated therapy-goal learning to study the effect of perceived control (as manipulated by feedback incorporation) on perceived agent benevolence, competence, and trust. Our results showed that perceived control was moderately correlated with benevolence (r = 0.448, BF10 = 7.150), and weakly correlated with competence and trust.