This study investigates the impact of introducing conversational pauses and self-adaptor gestures in robots to enhance human-likeness during English conversation practice. As globalization increases the demand for English proficiency, there is increasing interest in using conversational robots for language practice. However, conventional robots often lack natural disfluencies, making interactions feel overly artificial, especially for beginners. This study focuses on enhancing the robot's non-verbal behaviors by implementing conversational pauses and self-adaptor gestures such as fidgeting at predetermined intervals during scripted conversational scenarios. The experiment involved 22 men and women in their 20s. An experimental evaluation revealed that approximately 90% of participants perceived an increased sense of human-likeness when the robot exhibited these pausing and hesitation behaviors. These results suggest that strategically incorporating naturalistic disfluencies and human-like self-adaptor gestures into a conversational robot's non- verbal repertoire can significantly increase its perceived anthropomorphism and potentially improve user engagement in language learning contexts.

Fostering creativity in programming tasks is a challenging endeavor, especially when working alone. Traditional programming environments often lack support for stimulating creative thinking and idea generation. This paper presents an interactive augmented reality (AR) system that aims to enhance creativity in Internet of Things (IoT) programming tasks. The proposed system leverages Interactive Evolutionary Computation (IEC) and AR technologies to facilitate the collaborative exploration and evolution of IoT device programs (MESH programs). Users can create, evaluate, and iteratively refine MESH programs through an immersive AR interface, while being inspired by the system's suggestions and other users' creations. The system employs a genetic algorithm to evolve MESH programs based on user evaluations, and utilizes natural language processing to generate program descriptions that can trigger new ideas. A user study (n=16) was conducted to evaluate the system's effectiveness in stimulating creativity and promoting collaboration. Quantitative analysis revealed a significant increase in idea generation over time and a greater impact on inspiration for novice programmers. Qualitative findings highlighted the system's ability to foster a creative and collaborative environment. The insights gained from this study inform the design of future tools and experiences that support creative thinking and collaboration in the IoT domain.

As English becomes increasingly important globally, many agent-based conversation practice environments struggle to maintain learner motivation due to a lack of personalized behavior. This study proposes optimizing a conversational agent's non-verbal cues—such as nodding and voice characteristics—through interactive evolutionary computation to enhance learners' motivation. Participants engaged in role- play scenarios across eight settings, providing feedback after each interaction. The agent's behavior was iteratively optimized, and approximately 90% of participants reported increased willingness to interact, suggesting that personalizing non-verbal behavior can significantly improve motivation in language learners.

Language learning resources, such as dialogue scenarios, are important for supporting second language (L2) learning by engaging learners in simulated conversations or role-plays. However, there are often barriers to the wider dissemination of these resources, including the lack of a standard format for distributing them, the difficulties of maintaining and updating them over time, and the need for effective means of tracking their use and impact. This paper presents an integrated framework for fostering the wider dissemination of L2 learning resources, with a focus on dialogue scenarios. The framework is designed to address the above challenges by providing a novel approach for creating and distributing dialogue scenarios, as well as a set of guidelines to ensure that they are pedagogically sound and compatible with learning management systems (LMS). The paper also discusses the potential benefits of the framework for L2 learners and educators and outlines future directions for the project.

We propose an embodied conversational agent based on the willingness to communicate (WTC) model in L2 to help increase WTC in the context of English as a Foreign Language (EFL) by providing users with various daily conversation contexts. To simulate realistic and efficient conversations, we adopted a semantic approach in the response generation and created a system with flexible and adaptable domain knowledge, user's intent detection, and mixed-initiative conversation strategy. Our evaluation of the proposed system demonstrated its potential to increase WTC in the EFL context.