Challenges in education have continuously been addressed by integrating gamification, but a gap remains for game design principles that support user engagement. This paper outlines results obtained from integrating challenge-based gamification into an elementary school classroom to examine the emergence of student engagement and learning-related behavior. The approach was applied to logical puzzle quizzes where different gamification adjustments were captured and examined using physics' analogy (called the motion in mind concept). The structural experiment, with a mixed methods design, was designed around the notion of time pressure and the difficulty of gamifying the quizzing experience. This model was constructed to validate and expand the quantitative findings (motion in mind model) by including qualitative explorations (thematic analysis). The results revealed the potential synthesis of motion in mind and flow theory, and its relationships to engagement and learning were identified as a new conceptual scheme.

Cybersecurity has become a prominent issue in regard to ensuring information privacy and integrity in the internet age particularly with the rise of interconnected devices. However, advanced persistent threats (APTs) pose a significant danger to the current contemporary way of life, and effective APT detection and defense are vital. Game theory is one of the most sought-after approaches adopted against APTs, providing a framework for understanding and analyzing the strategic interactions between attackers and defenders. However, what are the most recent developments in game theory frameworks against APTs, and what approaches and contexts are applied in game theory frameworks to address APTs? In this systematic literature review, 48 articles published between 2017 and 2022 in various journals were extracted and analyzed according to PRISMA procedures and our formulated research questions. This review found that game-theory approaches have been optimized for the defensive performance of security measures and implemented to anticipate and prepare for countermeasures. Many have been designed as part of incentive-compatible and welfare-maximizing contracts and then applied to cyber–physical systems, social networks, and transportation systems, among others. The trends indicate that game theory provides the means to analyze and understand complex security scenarios based on technological advances, changes in the threat landscape, and the emergence of new trends in cyber-crime. In this study, new opportunities and challenges against APTs are outlined, such as the ways in which tactics and techniques to bypass defenses are likely to evolve in order to evade detection, and we focused on specific industries and sectors of high interest or value (e.g., healthcare, finance, critical infrastructure, and the government).

In response to the escalating threat of fake news on social media, this systematic literature review analyzes the recent advancements in machine learning and deep learning approaches for automated detection. Following the PRISMA guidelines, we examined 90 peer-reviewed studies published between 2020 and 2024 to evaluate the model effectiveness, identify limitations, and highlight emerging trends. Our analysis shows that deep learning models, particularly transformer-based architectures such as BERT, consistently outperform traditional machine learning methods, often achieving a high accuracy (Acc), precision (P), recall (R), and F1-score (F1). For instance, a BERT-based model reported up to 99.9% accuracy on the Kaggle fake news dataset and above 98% accuracy on other public datasets, including ISOT, Fake-or-Real, and D3. Similarly, the GANM model demonstrated robust performance on the FakeNewsNet dataset by integrating text and social features. Transfer learning and multimodal models that incorporate user behaviour and network information significantly improve detection in diverse, low-resource environments. However, challenges persist in terms of the dataset quality, model interpretability, domain generalisability, and real-time deployment. This review also underscores the limited adoption of few-shot and zero-shot learning techniques, highlighting a promising direction for future research on handling emerging misinformation using minimal training data. To support practical deployment, we advocate the development of explainable, multilingual, and lightweight models with greater emphasis on human-centred evaluation and ethical considerations. Our findings provide a foundation for researchers and practitioners to build scalable, trustworthy, and context-aware fake news detection systems for global use.

The development of games had been seen as an essential field of study in artificial intelligence. Although game concepts have been adopted in various problem domains, understanding the nature of game playing’s underlying mechanism has been limited. This study investigates such a mechanism by adopting a logistic model of game outcome uncertainty where a measure of game refinement had been derived. Application of such a measure to the popular board and sports games have been conducted to determine its implication in determining sophisticated design and has defined the harmonic balance between skill and chance. A theoretical analysis using practical data confirms the effectiveness of the proposed model, where reasonable game length is determined based on the definition of a gamified experience. The model is further expanded with the analogy of physics in mind. The law of motions in the game is formalized by considering the derivatives of the game progress model, where the link between game refinement theory and flow theory is identified and discussed. In essence, the sophisticated game’s underlying mechanism leads to a more natural yet pleasurable human experience.

Advanced persistent threat (APT) refers to a specific form of targeted attack used by a well-organized and skilled adversary to remain undetected while systematically and continuously exfiltrating sensitive data. Various APT attack vectors exist, including social engineering techniques such as spear phishing, watering holes, SQL injection, and application repackaging. Various sensors and services are essential for a smartphone to assist in user behavior that involves sensitive information. Resultantly, smartphones have become the main target of APT attacks. Due to the vulnerability of smartphone sensors, several challenges have emerged, including the inadequacy of current methods for detecting APTs. Nevertheless, several existing APT solutions, strategies, and implementations have failed to provide comprehensive solutions. Detecting APT attacks remains challenging due to the lack of attention given to human behavioral factors contributing to APTs, the ambiguity of APT attack trails, and the absence of a clear attack fingerprint. In addition, there is a lack of studies using game theory or fuzzy logic as an artificial intelligence (AI) strategy for detecting APT attacks on smartphone sensors, besides the limited understanding of the attack that may be employed due to the complex nature of APT attacks. Accordingly, this study aimed to deliver a systematic review to report on the extant research concerning APT detection for mobile sensors, applications, and user behavior. The study presents an overview of works performed between 2012 and 2023. In total, 1351 papers were reviewed during the primary search. Subsequently, these papers were processed according to their titles, abstracts, and contents. The resulting papers were selected to address the research questions. A conceptual framework is proposed to incorporate the situational awareness model in line with adopting game theory as an AI technique used to generate APT-based tactics, techniques, and procedures (TTPs) and normal TTPs and cognitive decision making. This framework enhances security awareness and facilitates the detection of APT attacks on smartphone sensors, applications, and user behavior. It supports researchers in exploring the most significant papers on APTs related to mobile sensors, services, applications, and detection techniques using AI.