Currently, educators are looking for new techniques in the teaching–learning process. Previous research has presented various teaching strategies designed to motivate students to learn. Existing research has focused on the use of spatial world design in elementary and secondary education settings. Therefore, it is essential to analyze how these immersive environments, designed and programmed by teachers, provide insights into the motivational perceptions of university students in teaching programming courses. This study employed a single-group, fixed-order, within-subject design (N=41) to examine students’ reported motivation after traditional instruction and a subsequent Roblox-supported activity and also to see if there is any difference in motivation when using Roblox, considering gender. The present study used the RIMMS to measure motivation. To analyze motivation using the teacher’s material and Roblox, the Wilcoxon signed-rank test was used, and to analyze how gender affects it, the Mann–Whitney U test was used. The results revealed a statistically significant difference between both modalities (Z=−2.264, p=0.024, r=0.354, N=41). With regard to gender, the results indicated no statistically significant difference between males (N=31, Mean Rank =21.60) and females (N=10, Mean Rank =19.15), U=136.500, Z=−0.567, p=0.571 (exact p=0.580). The findings indicate that students reported higher motivation scores after the Roblox-supported activity than after the preceding traditional instruction. However, given the fixed-order design and the lack of formal measurement equivalence between the contextualized RIMMS versions, these differences should be interpreted as exploratory descriptive evidence rather than as evidence of an effect attributable specifically to Roblox.
With advances in digital technology, augmented reality (AR) can play a substantial role in project development and sustainable growth in engineering and construction. Utilising AR technology with digital overlays offers a three-dimensional (3D) setting that combines real and virtual environments in real time. This immersive capability enables an improved spatial understanding and real-time data benefits for construction and engineering projects. Researchers have conducted extensive research on user acceptance of technology. There is, however, a lack of qualitative research that provides a more comprehensive view of the factors driving user acceptance of AR technology in engineering and construction projects. This research provides insight into the dynamics of user acceptance in engineering and construction projects through grounded theory in a real-world context. This research collected data through open-ended survey questionnaires and interviews. The obtained data were analysed using an iterative approach based on grounded theory principles in this study. A total of 248 open codes, 49 axial codes and 18 selective codes were generated. This process provided a causal loop diagram identifying the relationships of the user acceptance of AR technology adoption in engineering and construction.
University psychology clinics represent complex coordination environments in which pedagogical requirements, supervision constraints, and institutional governance mechanisms intersect. Traditional coordination practices based on spreadsheets often fail to ensure transparency, consistency, and traceability in internship scheduling. This study addresses this challenge by adopting a Design Science Research (DSR) approach to design and evaluate a governance-oriented decision-support system for internship coordination. The proposed artifact models the scheduling problem as a rule-constrained allocation process and implements a modular architecture integrating a GRASP heuristic with randomized greedy construction, single-pass triadic repair, a post-generation validation layer, role-based access control, and audit logging mechanisms. The system generates feasible schedules while enforcing operational and pedagogical constraints and supporting peer-observation structures, including binary and triadic supervision arrangements. A real-world pilot deployment provided case-based evidence of scheduling feasibility and operational utility, while the controlled computational evaluation demonstrated complete coverage of the institutional instance and no detected structural violations in the evaluated schedules. Role-based access control, persistent audit logging, and the controlled approval/publication workflow were completed and technically verified subsequent to that pilot, extending rather than altering the scheduling and validation components exercised during it. The results provide case-based evidence of the artifact’s utility and illustrate how a governance-oriented decision-support architecture can make rule validation, authorization, and scheduling provenance explicit within an institutional workflow. In the institutional instance evaluated with 30 randomized seeds, the single-pass triadic repair phase reduced the mean number of unassigned requirements from 3.60 to 0 while preserving feasibility across all runs.
Large language model (LLM)-based creative AI systems tend to produce plausible rather than novel research ideas. This study compares two ways of generating research hypotheses: a genetic algorithm (GA) that recombines Method, Problem, and Goal elements under a fitness function rewarding distance from the literature and literature support for each link, and free proposals by an LLM. Papers and hypotheses share one embedding space built from 2856 arXiv papers. Over 30 runs with budget-matched baselines and outcome measures on held-out and later-published papers, the selected hypotheses were more distant from the unseen literature than unsearched ones, but the GA matched rather than outperformed random search and hill climbing. Across two LLM families, two prompts, and two temperatures, and even when choosing from the GA’s concept vocabulary, LLM-proposed hypotheses were less diverse and closer to existing literature, whereas combinatorial search explored more widely. On 9755 ICLR submissions, the literature-distance novelty measure was practically unrelated to reviewers’ judgments (Spearman ρ ≤ 0.06), so it cannot establish originality. In controlled manuscript generation, LLM judges proved unreliable, and their order-consistent verdicts more often favored manuscripts built on LLM-proposed hypotheses, which blinded human raters tended to rank as least useful. Code and data are openly released.
Generative artificial intelligence (AI) use and online information checking may be socially patterned in different ways. This cross-sectional study analysed the 2025 Turkish Statistical Institute ICT survey of 22,502 internet users aged 16–74 using weighted modified Poisson regression with household-clustered standard errors. Overall, 19.2% reported generative AI use, and 48.3% of respondents who noticed doubtful content did not check it. Conditional non-checking (the verification gap) increased with age and decreased with education and reported digital activity, whereas AI use followed the opposite pattern. Compared with ages 16–24, the adjusted prevalence ratio at ages 65–74 was 1.48 (95% confidence interval 1.30–1.68) for non-checking and 0.10 (0.07–0.16) for AI use. Health information seekers and AI users reported less non-checking among those who noticed doubtful content, but neither group had a lower prevalence of noticing without checking among all internet users. Among non-checkers, 87.3% reported already judging the information or source unreliable and 15.2% reported insufficient skills or knowledge; responses could overlap. These findings distinguish participation in AI use, reported noticing of doubtful content, and subsequent checking. Verification support should address reported difficulty separately from decisions not to investigate content already considered unreliable.
