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.
The studying of opinion dynamics and its propagation within social networks is crucial for addressing a wide range of challenges, including political polarization, public health, and marketing strategies. In this work, we study the problem of opinion dynamics by proposing a framework based on Friedkin-Johnsen (FJ) to identifies influential users and study their impact on dynamics opinions of community. The FJ model assume each individual have two opinions: initial and expressed. Through a series of initial opinion manipulation experiments, the proposed framework assesses the impact of influential versus random users on the overall community opinion. The proposed framework is validated using a tweet dataset representing the U.S. presidential election. The results shows that influencers with highest influencing score, significantly shift the overall community opinion. Moreover, the results shows that the impact of influencers not limited to direct neighbors, but beyond it, to their neighbors of neighbors. This study demonstrates how digital influencers on social media can shape public opinion regarding a subject or cause.
This study consists of advanced text mining and Natural Language Processing (NLP) technique to analyse user reviews on commercial diet tracking apps, focusing on enhancing user engagement and satisfaction through persuasive System Design (PSD) model. By systematically categorising user feedback into areas such as primary task, dialogue, social and credibility, the research identifies key patterns and factors impacting user interactions, as well as provide deeper insight into how app features influence sustained user engagement and adherence to health goals. The categorised user feedback provides a distinct user reviews into four categorises which allow developers to pin point specific areas of where users are satisfied or requires specific refinements according to the four PSD models. The findings illustrate the diverse influences of PSD elements on user satisfaction and engagement. This methodological approach not only addresses a significant gap in understanding user feedback but also serves as pioneer attempt of using NLP incorporated with PSD model to refine health app features, thereby improving user outcomes and retention in specific areas of persuasive design models.
Previous studies have shown that providing donation education through smartphone applications (apps) significantly enhances knowledge and improves users' attitudes. In this study, two hundred forty participants were surveyed to determine the perceived persuasiveness of the Eye Donor Aust app. SPSS software was employed to test the correlations between demographic variables and analyze data distribution, and Structural Equation Modeling was created to explore the multivariate causal relationships among constructs. The findings indicate that three PSD principles (Credibility Support, Dialogue Support, and Social Support) significantly enhanced the app's persuasiveness, while participants' age and gender were significantly associated with the app's persuasiveness. These findings can inform the development of high-quality educational apps employing PSD principles that effectively persuade users.
This study evaluates the effectiveness of the digital intervention delivered using the MoM mobile app. The app uses different behavior as change techniques to bring about change via an explicit model of motivation as a mechanism of action for physical activity behavior. For a two-arm, single-blind experimental trial, 41 participants were randomly assigned to intervention (n = 20) and control (n = 21) groups. The Intervention group participants used a model-based personalized and adaptive app (MoM) for 40 days. Control participants used the same app without the motivation model, which was neither fully personalized nor adaptive. 9 days of baseline data and 40 days of treatment period data were collected for both intervention and control groups. Based on the linear mixed effect model, participants in the intervention group demonstrated more significant increases in steps per day (95
Addiction is one of the most important health issues around the world. It affects individuals, families, and societies while traditional approaches struggle with relapse and drop-out rates, accessibility problems, and aftercare support. However, behavior change support systems can offer substantial support by integrating evidence-based counseling strategies with Persuasive Systems Design. This paper presents a framework for mapping psychological approaches (Cognitive Behavioral Therapy, Motivational Interviewing, Minnesota Model, Contingency Management, and Family/Couples Therapies) with specific Persuasive Systems Design principles. This approach fills the gap between treatment and technology by showing how counseling strategies and persuasive software features can support addiction recovery together at different stages of recovery.
Social engineering (SE) typically involves persuasive elements that influence victims to take risky security actions. Among the recognised principles of persuasion are Cialdini's principles: social proof, likeability, authority, commitment and consistency, reciprocity, and scarcity. Research has shown differences in the prevalence of persuasive techniques in SE attempts, but whether these techniques differ in manipulative power in general, and specifically when security risks are present and known, remains unexplored. More broadly, does the technique matter as much as the readiness to be persuaded and take risks? Can individuals be grouped by their receptiveness to persuasion across all Cialdini principles in the context of SE attempts? To explore this topic, we presented participants with a social media scenario in which a member requests volunteers to install an app and provide feedback. We designed 12 scenarios, highlighting the presence or neutralisation of each principle. Our online study involved 329 participants from the Arab Gulf Cooperation Council (GCC) countries and 223 from the United Kingdom. Using K-Means clustering, we identified distinct cluster profiles in both samples. Clustering revealed that, across both regions, participants in each cluster exhibited consistent susceptibility levels to all principles. For example, in the Arab sample, we identified three clusters reflecting low, medium, and high susceptibility. This study concludes that vulnerability to persuasion in potential SE attacks appears consistent, regardless of the technique. This suggests that susceptibility to persuasion and readiness to take risks may have a more significant impact than the specific persuasion technique used.
Antimicrobial resistance (AMR) is projected to cause 8.22 million deaths annually by 2050, impacting 12 of the 17 Sustainable Development Goals (SDGs) and posing a major global health threat. Despite this, medical students often feel unprepared and lack confidence in prescribing antimicrobials, highlighting the need for improved education on AMR and stewardship. This paper presents the design and pilot testing of persuasive technology in AMRageddon v1, a gamified learning tool developed for final-year medical students to address these gaps. The tool integrates social cues from persuasive technology, employing physical (interactive graphics), psychological (problem-solving puzzles), language (positive reinforcement), social dynamics (achievement recognition), and social roles (role-play as doctors solving clinical cases) cues. Usability and user experience were assessed using the System Usability Scale (SUS), the shortened User Experience Questionnaire (UEQ-S), and focus group discussions (FGDs) structured by Bowen’s feasibility framework. Twenty-three final-year Norwegian medical students participated, yielding a high usability score (SUS: 78.75) and exceptional hedonic qualities, placing AMRageddon v1 among the top 10
In this paper, we report on a user study with 50 participants in-the-wild, followed by a semi-structured interview with 20 participants after a week of using an app designed for caregivers of individuals experiencing suicidal thoughts, called LifeLink. The app was designed iteratively following a user-centered design approach involving caregivers. Results show that LifeLink is user-friendly, elicits a positive user experience and effectively empowers caregivers. The use of the features was found to be persuasive in influencing caregiver behaviors toward supporting individuals experiencing suicidal thoughts. Our research findings contribute to advancing knowledge regarding the development of persuasive technology designed for suicide prevention, focusing on creating user-friendly, impactful tools that deliver a positive user experience.
This work presents a personalised AI coaching system to enhance occupational health and safety using a bottom-up design approach inspired by Lean UX and Agile principles. Leveraging Large Language Models and Computer Vision, the pilot integrated automated reporting and role-playing simulations to address safety challenges. Prototyping with existing and adapted AI tools demonstrated feasibility, with positive feedback from managers highlighting its potential to improve compliance and the need for staff involvement. The study underscores AI's role as a collaborative coaching mediator and the effectiveness of bottom-up design in aligning solutions with user needs, while naturally integrating persuasive technology principles.
Context. Information and Communication Technologies (e.g., search engines, AI) disseminate a large volume of information, the quality of which can vary, particularly when the search topics are controversial. To manage both the quantity and quality of the information they process, users must adopt effective search strategies while maintaining epistemic vigilance. Objectives. This study examines the effects of the learning context (cooperation vs. competition) on search strategies (exploration-exploitation), online epistemic vigilance, as well as knowledge gain and attitude change regarding the controversial topic of animal protein consumption (meat, milk, eggs). Method. Forty-seven participants have currently taken part in the study, with data collection still ongoing. Twenty-six participants were assigned to the competition condition (preparing a debate), and twenty-one to the cooperation condition (preparing a discussion). All participants had 20 min to search for information to prepare for their respective exchanges. Results. The main findings show that a cooperative context leads users to pursue mastery goals, prompting them to adopt deeper content exploitation strategies and acquire more knowledge by the end of the search compared to individuals in a competitive context. Those in the competitive context tend to explore more, learn more superficially, pursue performance goals, and more frequently re-exploit the same content in their queries to find arguments that corroborate their viewpoint. Surprisingly, competitors still shift their attitudes toward animal suffering following the information search, whereas cooperators tend to polarize their initial attitudes on this issue. Conclusion. This study has implications for the domain of traditional ICTs and generative AI as persuasive technologies.
We investigated the relationship between the effect of persuasion techniques on the individual and their gender, conceptualized as a three-value variable where participants could identify as male, female, or non-binary. While previous research has primarily examined the role of binary genders in persuasion, this study is the first to compare susceptibility to persuasion across binary and non-binary individuals. A total of 1,995 participants evaluated the persuasive impact of 30 statements representing 10 persuasion techniques (e.g., framing, social proof, flattery) across three contexts. Additionally, participants’ personality traits and dysfunctional attitudes were assessed using the TIPI and DAS scales. Our findings revealed that non-binary participants were significantly less susceptible to persuasion, consistently assigning lower scores than both male and female participants across all techniques and contexts. The difference between non-binary individuals and binary genders was an order of magnitude greater than that between male and female participants, even after controlling for age, education, TIPI personality traits, and DAS dysfunctional attitudes. Mediation analysis indicated that 34.1
Persuasive technology (PT) uses techniques such as goal-setting and feedback to promote behavioral changes, and its effectiveness in mitigating various health conditions has been demonstrated in numerous studies. However, no systematic or scoping review has explored the application of PT in general health risk management. Existing reviews tend to focus on specific risk factors or health conditions, leaving a gap in exploring the broader application of PT in mitigating different health risks. This paper aims to expand the scope of existing reviews to include a wide range of health conditions managed through PT. A scoping review was performed following the approach outlined by Tranfield et al. and the PRISMA guidelines for reporting systematic/scoping reviews. The search was conducted across 5 databases. From an initial pool of 688 studies, 126 were removed due to duplication. Of the 21 studies included in the final analysis, 7 (33.33
Deceptive Design, also known as "Dark Patterns", manipulates users through interface design that exploits cognitive biases, psychology, and design standards to extract money, data, or attention. As governments act against such practices, it is crucial to distinguish them from benevolent design approaches like Persuasive Technology, which aims to influence user behavior positively. By condensing the past decade of Dark Pattern taxonomies with Straussian Grounded Theory (SGT), we have created a set of five heuristics diagnostic of manipulative design. We present the results of a small study using these heuristics and propose that our heuristic evaluation (HE) process can enhance the assessment of Persuasive Technology.
Mobile games have the potential to increase physical activity (PA). However, not all games can capture and sustain interest in PA, as it is challenging to maintain user engagement once the initial novelty diminishes. This research explores how augmented reality (AR) and idle game design, combined with persuasive strategies, can reduce sedentary behavior. We designed, developed and evaluated PetBuddy, a mobile health game aimed at promoting PA. Sixty-five young adults (between 18–35 years old) played the game for 10 days and completed a questionnaire about their experience. This is followed by an interview of 17 participants. Results revealed significant increases in PA with users meeting the World Health Organization’s recommended activity levels and adopting healthier behavioral changes. Participants reported that the persuasive strategies implemented as game features (particularly competition) and the game experience motivated them to participate in PA. The findings from this research provide a deeper understanding of how mobile games can be designed to encourage PA.
Achieving a sustainable future requires behaviour change on a large scale. One possible approach is to improve persuasive technology by mirroring each users' personality, but research on this is very limited. In this work, we explore whether the Big Five personality traits underpin differences in persuasive text for sustainability, both in terms of content and linguistics. We also investigate whether personality scores can be reliably predicted from persuasive text, using machine learning (ML) techniques. Our results show that personality traits appear to influence some aspects of the content, but not linguistics; however, predicting personality is more successful through linguistics than content. We provide a follow-on analysis of which features are most informative to predict personality scores. Based on these results, we provide recommendations for automatic personality recognition and synthesis in persuasive technologies.
