Conversational agents are increasingly used in education for learning support. An application is “learning by explaining”, where learners explain their understanding to an agent. However, existing research focuses on single roles, leaving it unclear how different pedagogical roles influence learners’ interaction patterns, learning outcomes and experiences. We conducted a between-subjects study (N=96) comparing agents with three pedagogical roles (Tutee, Peer, Challenger) and a control condition while learning an economics concept. We found that different pedagogical roles shaped learning dynamics, including interaction patterns and experiences. Specifically, the Tutee agent elicited the most cognitive investment but led to high pressure. The Peer agent fostered high absorption and interest through collaborative dialogue. The Challenger agent promoted cognitive and metacognitive acts, enhancing critical thinking with moderate pressure. The findings highlight how agent roles shape different learning dynamics, guiding the design of educational agents tailored to specific pedagogical goals and learning phases.

Due to the human-like nature, large language models (LLMs) often express uncertainty in their outputs. This expression, known as ”verbalized uncertainty” , can appear in phrases such as ”I’m sure that [...]” or ”It could be [...]” . However, few studies have explored how this expression impacts human users’ feelings towards AI, including their trust, satisfaction and task performance. Our research aims to fill this gap by exploring how different levels of verbalized uncertainty from the LLM’s outputs affect users’ perceptions and behaviors in AI-assisted decision-making scenarios. To this end, we conducted a between-condition study (N = 156), dividing participants into six groups based on two accuracy conditions and three conditions of verbalized uncertainty. We also used the widely played word guessing game Codenames to simulate the role of LLMs in assisting human decision-making. Our results show that medium verbalized uncertainty in the LLM’s expressions consistently leads to higher user trust, satisfaction, and task performance compared to high and low verbalized uncertainty. Our results also show that participants experience verbalized uncertainty differently based on the accuracy of the LLM. This study offers important implications for the future design of LLMs, suggesting adaptive strategies to express verbalized uncertainty based on the LLM’s accuracy. • Medium verbalized uncertainty leads to higher trust, satisfaction, and performance. • At different accuracy levels, the main factor influencing users’ trust varies. • Users perceive medium verbalized uncertainty as better low verbalized uncertainty. • Future LLMs should express verbalized uncertainty using adaptive strategies.

Learning hash functions have been widely applied for large-scale image retrieval. Existing methods usually use CNNs to process an entire image at once, which is efficient for single-label images but not for multi-label images. First, these methods cannot fully exploit independent features of different objects in one image, resulting in some small object features with important information being ignored. Second, the methods cannot capture different semantic information from dependency relations among objects. Third, the existing methods ignore the impacts of imbalance between hard and easy training pairs, resulting in suboptimal hash codes. To address these issues, we propose a novel deep hashing method, termed multi-label hashing for dependency relations among multiple objectives (DRMH). We first utilize an object detection network to extract object feature representations to avoid ignoring small object features and then fuse object visual features with position features and further capture dependency relations among objects using a self-attention mechanism. In addition, we design a weighted pairwise hash loss to solve the imbalance problem between hard and easy training pairs. Extensive experiments are conducted on multi-label datasets and zero-shot datasets, and the proposed DRMH outperforms many state-of-the-art hashing methods with respect to different evaluation metrics.