Surveys, especially Likert scale questionnaires, are widely used in HCI to capture users’ attitudes and experiences, but numeric ratings alone provide little insight into the rationales behind those ratings. While adding open-ended text fields or post-questionnaire interviews can elicit richer explanations, they often impose extra effort, leading to survey fatigue or recall bias. To address this gap, we proposed Rationalizer, an LLM-supported questionnaire system that generates contextualized rationales to support participants articulate their explanations alongside each Likert item and rating. In a user evaluation comparing it with the traditional questionnaire that included open-ended text fields, Rationalizer increased the percentage of Likert items with rationales, sustained participants’ willingness to provide self-input rationales, and supported them in articulating longer explanations within comparable writing durations as the study progressed. Quality analyses further showed that Rationalizer yielded higher-quality rationales (i.e., justification and relevance) than the traditional questionnaire. These findings highlight the potential of LLM-supported questionnaires to enrich Likert ratings with contextualized, richer explanations.

We present the Eye2Head Gesture (E2H), a gaze-based technique that integrates saccade gestures with eye-head coordination for target selection in virtual reality. It involves a back-and-forth saccadic gesture, aligning the gaze with the head forward direction and then returning to the target to complete the selection. The first study identified the parameters for implementing the E2H technique, including visualization of the relay zone (i.e., the area anchor to the head’s forward direction), relay zone size, and saccade gesture duration. Four variants of the E2H were developed, each differing in the shape and timing of the relay zone. The second study evaluated four E2H variants against Dwell and Eye&Head techniques, revealing that Region-shaped variant of E2H outperformed others in speed, error, and fatigue. This work contributes a gaze-based technique that harnesses eye-head coordination, using saccadic gestures to enhance the efficiency of target selection, particularly for those at large angular distances.

The similarity effect refers to the tendency for people to be more easily influenced by others who resemble them in appearance. This phenomenon has been found to have positive impacts, including on the building of trust, that enrich the quality of communication (e.g., fluency or collaboration performance). While research has shown that the similarity effect occurs in screen-based communication platforms, it remains unclear how this phenomenon impacts user perceptions, especially of others' persuasiveness, in immersive environments such as virtual reality (VR). In this study, we adopted a mixed-methods approach to exploring how interaction with avatars of similar appearance to one's own self-representation influences conversations. Such similarity was operationalized as having three levels: identicality, moderate similarity, and dissimilarity. The study found that avatars of moderate similarity have the greatest persuasiveness; however, in both identicality and moderate similarity conditions, participants felt it was easier to communicate with and lower eeriness rating to avatars than in the dissimilarity condition. Multiple linear regression further revealed that users who had relatively low self-esteem and/or were relatively conscientious were more susceptible to the positive effect of appearance similarity on persuasiveness. We conclude that the similarity effect, especially when the similarity in question is moderate, could be leveraged to support persuasiveness in VR-based communication.