Research

This case study presents IF: CARGO, an experimental puzzle game that uses a large language model as a semantic compiler rather than an autonomous game-playing agent. Players author IF/THEN rules in natural language, which the model translates into a constrained command schema for deterministic validation and execution by the game engine. This architecture creates a playable loop of expression, execution, observation, and revision, framing AI interaction as semantic debugging. A mixed-methods playtest with 24 participants across eight levels examined player attempts, thinking time, perceived controllability, adjustability, and interpretations of the AI’s role. Results suggest that players generally understood the model as a translation intermediary and could revise their strategies through feedback, while periodic commands, multi-robot coordination, and rule-priority mechanics created greater cognitive and diagnostic demands. The study proposes a practical pattern for AI-native gameplay: constrain natural-language input, preserve player authorship, and ensure deterministic execution.

In light of the ethical debates surrounding fairness in Artificial Intelligence (AI) systems and their growing use in digital games, it becomes essential to investigate the presence and the propagation of structural biases in the content generation for this field. This study analyzes how six of the most widely used LLMs construct characters and narratives, contrasting the structures of the Hero's and Heroine's Journeys. Through a qualitative analysis of 270 generated narratives, 45 produced by each LLM, character trait codes and thematic axes were extracted. For each model, these 45 narratives were generated in three prompt formats: 15 with no gender identification for the protagonist, 15 with a female protagonist, and 15 with a male protagonist, to explore whether their formulation influences bias. The results reveal that although LLMs generate more female protagonists, masculinity is coded as proactive and resourceful. In contrast, female characters are restricted to emotional containment, the romantic support stereotype, and journeys of protection.

We present TT4D, a large-scale, high-fidelity table tennis dataset. It provides 140+ hours of reconstructed singles and doubles gameplay from monocular broadcast videos, featuring annotations like high-quality camera calibrations, precise 3D ball positions, ball spin, time segmentation, and 3D human meshes over time. This rich data provides a new foundation for virtual replay, in-depth player analysis, and robot learning. The dataset's combination of scale and precision is achieved through a novel reconstruction pipeline. Prior methods first partition a game sequence into individual shot segments using the 2D ball track, and only then attempt reconstruction. However, 2D-based time segmentation collapses under occlusion and varied camera viewpoints, preventing reliable reconstruction. We invert this paradigm by first lifting the entire unsegmented 2D ball track to 3D through a learned lifting network. This 3D trajectory then allows us to reliably perform time segmentation. The learned lifting network also infers the ball's spin, handles unreliable ball detections, and successfully reconstructs the ball trajectory in cases of high occlusion. This lift-first design is necessary, as our pipeline is the only method capable of reconstructing table tennis gameplay from general-view broadcast monocular videos. We demonstrate the dataset's fidelity through two downstream tasks: estimating the racket's pose & velocity at impact, and training a generative model of competitive rallies.

This Systematic Literature Review (SLR) synthesizes how Generative Artificial Intelligence (Generative AI) is implemented in learning environments, with and without gamification, and what studies report about students’ motivation and engagement. Following the PRISMA 2020 guidelines, structured searches in Scopus, IEEE Xplore, and ScienceDirect identified 792 records, and 116 full-text reports were assessed. Sixty-nine peer-reviewed journal articles were included: 21 on AI-supported gamified or game-based learning and 48 on Generative AI and motivation or engagement without gamification. In the gamified subset, four implementation forms appear (AI characters and role-play partners, LLM feedback and adaptive difficulty, AI-generated assessment items, and Generative AI as a game design tool), and all eight comparison studies reported higher or favorable motivation, engagement, or related outcomes. Across the whole corpus, two meta-analyses and one systematic review report positive overall effects of ChatGPT and Generative AI on engagement or motivation, with moderators and mixed emotional evidence. Individual studies are not uniformly positive: one found that videos outperformed a Generative AI assistant on knowledge gains and curiosity, one found no significant effect on behavioral engagement, and one found that Generative AI use may reduce active learning and motivation. The evidence is concentrated in higher education (48 of 69 articles) and in 2025 (52 of 69); the most frequently stated limitations are small or single-context samples (20 articles) and short interventions with novelty effects (12), and the most frequent recommendations are longer studies (18) and larger and more diverse samples (17). Future research should isolate the AI component's contribution and test it over time. Keywords: Generative AI, Gamification, Gamified learning, Student motivation, Systematic review of literature.

Introduction and aims The increasing availability of smartphones and internet access in Somalia has contributed to the growing use of mobile games among children. Mobile gaming may influence cognitive development positively or negatively depending on gaming habits and supervision. This study aimed to assess the influence of mobile games on cognitive development among Somali children aged 6-15 years. Methods A descriptive cross-sectional survey was conducted among 200 Somali children, with their accompanying parents or guardians, recruited from selected primary and secondary schools in Mogadishu by multistage sampling. A structured questionnaire recorded gaming habits, parental supervision, and perceived positive and negative cognitive effects on a 5-point Likert scale. Data were summarised as frequencies, percentages, means, and standard deviations (SD). Results More than half of the children (55.0%) played daily, and 70.0% played for 1-2 h per day. Action (45.0%) and sports (35.0%) games predominated, educational games were played by 5.0%, and only 25.0% of children were always supervised. Positive-effect items had an overall mean of 3.71; agreement was highest for “games make learning more interesting” (4.00 ± 0.95). Negative-effect items had an overall mean of 3.56. Among perceived cognitive and academic difficulties (domain mean 3.54), reduced study time scored highest (3.80 ± 1.13). Indicators of problematic gaming had a domain mean of 3.66, and 60.0% of children found it difficult to stop playing. Fatigue and reduced school engagement had a domain mean of 3.55. Discussion Somali children in this sample perceived mobile gaming as having both beneficial and harmful effects on cognitive development. The evidence is descriptive and self-reported and does not establish causation. Balanced gaming, closer parental supervision, and greater use of educational games are recommended, and longitudinal studies with objective cognitive measures are needed.

While gamification has been associated with increased engagement in technology-enhanced learning, limited evidence exists regarding its relationship with students’ psychological safety, confidence, and inclusion in face-to-face, language-rich classrooms. This study examined Grade 9 students’ perceptions of ClassPoint-supported gamification in Arabic and Islamic Studies classrooms in the United Arab Emirates. Using a quantitative, cross-sectional survey design, data were collected from 575 students across three domains: engagement and motivation, confidence and psychological safety, and inclusion and equitable participation. Descriptive statistics, Spearman’s rank-order correlations, and the Friedman test were employed. Findings revealed predominantly positive perceptions of engagement and motivation, followed by confidence and psychological safety, while inclusion and equitable participation received comparatively less favorable ratings. Strong positive associations emerged between engagement and confidence, alongside moderate to strong associations involving inclusion. The Friedman test identified statistically significant differences across the three domains. These findings suggest that gamified classroom interaction may support student participation and confidence, although positive engagement does not necessarily ensure equitable inclusion. The study highlights the importance of teacher-mediated gamification design in creating supportive, participatory learning environments and emphasizes the need to examine psychological safety and student voice alongside engagement.