Learning is an essential human need, contributing to personal and intellectual growth influenced by various factors, including motivation. Gamification, the incorporation of game elements into non-game contexts, has been widely explored as a method to enhance learning and increase engagement and motivation to learning. While extensive research has examined the effectiveness of gamified learning on student performance, limited attention has been given to the role of working memory capacity and individuals gaming disorder tendency. Gamification could hypothetically consume memory or trigger a desire for gaming, potentially impacting learning. This paper is based on an experiment with 45 participants (53.33
Interactive agents are an essential element of many persuasive applications. Their design and development have so far required extensive human effort to model their appearance and behavior. However, recent advances in the generative capabilities of Large Language Models (LLMs) might pave the way to build persuasive agents capable of autonomous, open-ended interactions without requiring the traditional investment in agent development. In this paper, we investigate the creation of an LLM-based embodied agent aimed at interacting with users in real-time to coach them in performing slow and deep breathing. In the approach we followed, the LLM uses a text-based context to generate a composition of predefined behaviors for interacting with the user through both verbal and nonverbal communication. The text-based context provided to the LLM described essential details, like the user's respiratory rate, to monitor the exercise. Information about actual user's breathing was provided to the LLM-model through a physiological sensor. The LLM-based breathing coach managed to follow the exercise structure and generated believable contingent behavior compositions. However, as we describe in the paper, building and evaluating the system allowed to highlight limitations of using only LLMs to create agents capable of real-time user interactions. The identified limitations suggest a need for hybrid approaches.
Repetitive Strain Injuries (RSIs) are a leading cause of workplace disability and absenteeism, particularly among office workers engaged in prolonged computer-based tasks. This study evaluates the effectiveness of EMotivA, a persuasive system designed to promote active breaks and mitigate the risk of RSIs. A quantitative pretest-postest design was conducted with 20 office workers over one workweek, during which EMotivA dispatched persuasive notifications every 40 min to encourage brief, light physical activities. The system aimed to increase the frequency and regularity of active breaks and reduce musculoskeletal discomfort, measured using the Cornell Musculoskeletal Discomfort Questionnaire (CMDQ). Results showed a significant increase in active breaks and a marked improvement in time dedicated to physical activities. CMDQ scores revealed a significant reduction in musculoskeletal discomfort post-intervention (t(19) = 3.152, p = 0.005, d = 0.705). These findings demonstrate that EMotivA effectively fosters healthier work habits and reduces physical discomfort, emphasizing its potential as a tool for occupational health management.
Persuasive technologies drive behavioral changes across domains like healthcare, education, and e-commerce but face ethical challenges around consent, autonomy, privacy, and transparency. This study explores ethical design considerations in Human-Computer Interaction through expert focus groups and interviews. The study provides guidelines for the design of ethical and trustworthy persuasive technology. Key findings highlight the need for clear consent mechanisms, visual aids to reduce consent fatigue, customizable notifications to enhance autonomy, and transparent communication with algorithmic clarity. Participants also emphasized the importance of mechanisms for rectifying user errors and promoting equitable navigation. These insights offer practical guidance for fostering trust and ethical engagement, with future research needed to explore diverse demographics and long-term effectiveness.
