The increased availability of chatbots has drawn attention and interest to the study of what answers they provide and how they provide them. Chatbots have become a common sight in museums but are limited to answering only simple and basic questions. Based on the observed potential of chatbots for history education in museums, we investigate how chatbots impact history education and improve the overall experience according to their appearance and language style. For this, we built three models, designed by factors on embodiment and reflection, and 60 sets of answer–questions, designed for the National Museum of Korea. We conducted a study with a total of 34 participants and carried out a variety of analyses covering individual learning styles, museum experience scales, gaze data, in-depth interviews and observations from researchers. We present various results and lessons regarding the effect of embodiment and reflection on the museum experience. Our findings show how people with different learning styles connect with chatbot models and how visitors’ behavior in the museum changes depending on the chatbot model. Specifically, the chatbot model equipped with embodiment and reflection shows its superiority in enhancing the museum experience, in general.

Captions help deaf and hard-of-hearing (DHH) individuals visually communicate voice information to better understand video content. In speech, the literal content and paralinguistic cues (e.g., pitch and nuance) work together to create real intention. However, current captions are limited in their capacity to deliver fine nuances because they cannot fully convey these paralinguistic cues. This paper proposes an audio-visualized caption system that automatically visualizes paralinguistic cues into various caption elements (thickness, height, font type and motion). A comparative study with 20 DHH participants demonstrates how our system supports DHH individuals to be better accessible to paralinguistic cues while watching videos. Particularly in the case of formal talks, they could accurately identify the speaker’s nuance more often compared to current captions, without any practice or training. Addressing some issues on legibility and familiarity, the proposed caption system has potentials to enrich DHH individuals’ video watching experience more as hearing people enjoy.

Immersion plays a crucial role in video watching, leading viewers to a positive experience, such as increased engagement and decreased fatigue. However, few studies measure immersion while watching videos, and questionnaires are typically used in the measurement of immersion for other applications. These methods may rely on the viewer's memory and cause biased results. Therefore, we propose an objective immersion detection model by leveraging people's gaze behavior while watching videos. In a lab study with 30 participants, an in-depth analysis is carried out on a number of gaze features and machine learning (ML) models to identify the immersion state. Several gaze features are highly indicative of immersion and ML models with these features are able to detect an immersion state of video watchers. Post-hoc interviews demonstrate that our approach is applicable to measure immersion in the middle of watching a video, where some practical issues are discussed as well.