Journal on Interactive Systems

Why Don’t You Let the Light In? Developing Radiant Patterns for a Critical Game Aiming Player Empowerment

2025-10-20T18:51:58+00:00

The exponential growth of the digital games industry has intensified the use of Deceptive Patterns (DPs) – design strategies that, while profitable, undermine players’ psychological well-being by frustrating their needs for autonomy, competence, and relatedness, as defined by Self-Determination Theory (SDT). In response, ethical alternatives, such as Radiant Patterns (RPs), have been proposed; however, they have remained mainly theoretical. This study addresses this gap with a twofold contribution. First, it details the refinement and operationalization of the RPs concept, transforming it from an abstract idea into a structured framework for well-being-oriented design. Second, it presents the development and evaluation of The Good Dev (TGD), the first critical game to implement this refined framework. TGD was evaluated in two phases (Alpha and Beta tests) using a mixed-methods approach, including validated instruments and semi-structured interviews. Findings reveal that while players recognize DPs, they are unaware of the psychological impact these patterns have on player well-being. The game demonstrated high playability and engagement, proving effective in fostering critical reflection on design ethics. Ultimately, this study highlights the potential of combining the RP framework with critical gameplay to empower players and contribute to healthier digital gaming environments.

Conversational-Linguistic Politeness for Chatbot Design: an Evaluation of Parameters and Card-Based Approaches

2025-10-06T14:11:12+00:00

Chatbots often find it challenging to meet user expectations by providing appropriate responses and engaging in natural dialogues. Previous research suggests that incorporating linguistic and conversational strategies, particularly those centered on politeness, can significantly enhance user satisfaction and interaction quality. However, current conversation design practices often lack clear guidance to support these strategies, leaving designers to rely on personal preferences. To enhance the design of polite chatbots, previous work has investigated the use of politeness parameters, which help in understanding how politeness is expressed in chatbot dialogues through language function variety and indirect communication. In this work, our initial focus is on assessing participants' perceptions when exposed to different uses of these parameters. To achieve this, three fictional scenarios were presented to six participants to assess the combined impact of various politeness parameters. Although participants acknowledged different polite design strategies, discussing the parameters during the interactions proved challenging. To investigate the use of these parameters during the design process, we instantiated them in a card-based tool with four categories. A workshop was conducted for evaluation where seven participants first designed polite dialogues without the cards. Then, the participants were presented with the cards to reflect on whether they could have supported the design process. Some benefits mentioned by participants included supporting tailored dialogues, guiding design decisions, and maintaining a consistent tone. In summary, our contributions involve evaluating the effectiveness of politeness parameters as design tools, introducing a set of four cards to assist in formulating politeness strategies (currently used in educational settings), and exploring user perceptions when presented with these cards.

Requirement Prioritization in Software Engineering: a Systematic Literature Review Update

2025-02-02T21:36:29+00:00

Context: Software requirements prioritization is the classification and ordering of requirements given their priority. This ordering can be done using prioritization techniques. Knowing the main prioritization techniques is essential for advancing research in Requirement Engineering. A Systematic Literature Review (SLR) published in 2021 presented evidence on requirements prioritization techniques, their limitations, a list of identified techniques, and the criteria used in
the prioritization process. An assessment of this SLR revealed the need for an update. Purpose: This study aims to update the previous SLR and contribute to the current state of research on this topic. Method: First, we conducted a tertiary study to identify other reviews addressing the same topic. Then, we applied the 3PDF framework to evaluate the feasibility of SLR updating, and finally, we updated the previous SLR. In this update, we consider new research questions and analyses of the extracted data. This update incorporates new research questions and additional analyses of the extracted data. Results: The updated SLR identified a total of 45 relevant studies between 2021 and June 2025, shedding new light on the evolution of requirements prioritization. We identified 32 distinct requirements prioritization techniques, with 23 of these being novel, adding a fresh perspective to the area. Conclusion: This updated SLR provides a comprehensive view of the continuous evolution in requirements prioritization. The review highlights significant advancements in the use of Machine Learning (ML) and Artificial Intelligence (AI) techniques, alongside the enduring popularity of traditional methods such as the Analytic Hierarchy Process (AHP) and MoSCoW. Understanding these trends is crucial for practitioners, as they reflect persistent challenges in decision-making, including the influence of individual preferences and domain knowledge on the prioritization process.

Evaluation of Immersion in the Flow State from the Variation of the Instagram Web Interface

2025-08-12T14:34:10+00:00

Background: Social media interfaces employ design strategies that encourage continuous engagement, which may influence users’ immersion and flow state. Investigating how interface design elements contributes to inducing a flow state in users is essential to promote ethical and mindful design practices. Purpose: This study aims to explore the impact of Instagram’s interface design on user behavior, with a particular focus on indications of the flow state. Methods: We developed a browser extension based on the Hook model to carry out interventions in the interface, aiming to remove triggers, reduce rewards, and limit user actions. A case study was conducted with undergraduate students who navigated both the modified and standard Instagram web interfaces. The Online Flow Questionnaire was used to measure flow state levels and, additionally, participants who used the extension took part in a brief interview to gather qualitative insights about their experience. Results: Findings indicate a subtle variation in the flow state between the modified and standard interfaces, suggesting that interface changes influence user’s flow state. Results also suggest that the device type can modulate the flow state and that individual factors, such as technological familiarity, can influence user interaction. Conclusion: Interface design elements encourage users to remain engaged for longer periods, providing an initial indication of the impact of design on user flow state. The findings reinforce the need for ethical design approaches that balance user retention with mental well-being.

Prediction and Analysis of Cybersickness in VR Games Using Symbolic Machine Learning

2025-10-14T12:56:25+00:00

Cybersickness (CS) is one of the main challenges for the adoption of Virtual Reality (VR), manifesting through symptoms such as nausea, dizziness, and eye strain, particularly in Head-Mounted Display (HMD) devices. Although subjective measures, such as questionnaires, are widely used to assess CS, they do not allow for real-time user feedback. This study investigates the role of biosignals in identifying the causes and indicators of CS in VR games, employing Symbolic Machine Learning (SML) to classify the most relevant factors. Our approach combines Electrocardiogram (ECG), Electrodermal Activity (EDA), and Accelerometer (ACC) data with game metrics and user profile attributes. Data were collected from two VR games: a car game and a flying game. Decision Trees and Random Forests were used to build interpretable models, and the results showed that integrating biosignals and game data significantly improves CS prediction, with Random Forest achieving an AUC of 0.95. The findings highlight that exposure time, motion intensity, and electrodermal activity are among the key predictors of CS, reinforcing the importance of physiological monitoring in VR research. Furthermore, the study demonstrates the potential of SML in creating explainable models, contributing to more effective strategies for mitigating CS.

Network Modeling of Rational Value Systems Interaction: Justification of the Basic Principles and Methodology

2025-04-11T11:45:20+00:00

Background: the article is devoted to the study of the interaction of rational-value systems, thus being an integral part of the theory of rational systems. Purpose: the purpose of the article is to develop a methodology for network modeling of rational-value systems (RVS) based on the analysis of their interaction, proposing a classification of networks (egocentric, communicative, convergent and cause-and-effect) and substantiating their application. Methods: the main method of this work is network analysis used in social sciences. Results: the article substantiates the possibility of network modeling of rational-value communication. Both general principles and examples of a network model and a data window, as well as special cases in the form of network types are presented. The most promising include the egocentric and cause-and-effect network model. Conclusion: in the conclusion, conclusions and generalizations are made, further directions for the study of rational systems are formulated. This article briefly describes the basic principles of rational-value communication, provides an example of network modeling and windows of calculation data provided in a previously published article, but not included in it. In addition, this article expands the author’s ideas in the direction of network modeling options.

Gov.br Portal: Contrasting the Results of an Accessibility Assessment by Automated Tools with the Analysis of a Person with Low Vision

2025-08-12T14:05:45+00:00

Governments worldwide have increasingly adopted centralized models for delivering public services to citizens. In 2019, Brazil launched the Gov.br portal, which consolidates the digital channels of all federal government agencies and provides unified access to information and services. This study aims to assess the accessibility of the Gov.br portal using three automated evaluation tools (ASES, AccessMonitor, and TAW) and a contrast verification tool (Contrast Checker), in addition to a manual evaluation conducted by a low vision web accessibility specialist. This qualitative and exploratory analysis reveals that, despite Gov.br achieving favorable scores in automated evaluations, significant challenges remain regarding the user experience of individuals with low vision. The most frequent issues identified include the portal’s lack of responsiveness when displayed at maximum zoom on smartphones, insufficient color contrast, and the absence of contextual information in links. As a contribution, the study proposes corrective measures to enhance the website’s accessibility, thereby promoting inclusive access for all users.