The use of algorithmic decision systems is being expanded to high-risk areas like credit, recruiting, and distributing government resources. Despite the fact that these systems are usually claimed to be objective and efficient, there have been apprehensions about the likelihood of structural inequalities being perpetuated by the systems. This paper examines the effect of a fairness-aware pre-processing technique called reweighing on the performance of a predictive system in a controlled simulation environment. Using a synthetically created credit approval dataset with structural disadvantage embedded, we compare the performance of a logistic regression classifier with and without reweighing. Fairness is measured using demographic parity disparity (DPD), disparate impact ratio (DIR), and equalized odds difference (EO), along with predictive accuracy. In a single test scenario (seed = 42), reweighing does not improve all fairness metrics uniformly. However, when analyzed for robustness across 50 independent random seeds, we find modest average reductions in demographic parity disparity and equalized odds difference for reweighing, with little change in predictive accuracy. Threshold sensitivity analysis also shows that fairness metrics are sensitive to decision thresholds. These results show that fairness-aware pre-processing can lead to systematic improvements in expectation, although trade-offs across fairness metrics and performance remain context-dependent.

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This study investigates how three personality traits from the Big Five framework—extraversion, neuroticism, and conscientiousness—influence individuals’ continuance intention to use ChatGPT, with a particular focus on the mediating roles of Perceived Ease of Use (PEOU) and Perceived Usefulness (PU). Drawing on the Technology Acceptance Model (TAM), this research aims to integrate personality-based differences into a well-established framework for understanding technology adoption and sustained usage. Data were collected through an online survey targeting active ChatGPT users and analyzed using structural equation modeling to examine both direct and indirect relationships among variables. The findings indicate that conscientiousness has a strong and positive impact on both PEOU and PU, and indirectly enhances continuance intention through these cognitive evaluations. Extraversion shows a limited but positive effect primarily through perceived ease of use, suggesting that socially oriented individuals may engage with the system when it is easy to navigate. In contrast, neuroticism does not demonstrate any statistically significant relationship with the key variables in the model. Consistent with TAM, PEOU significantly influences PU and continuance intention, with PU emerging as the most influential predictor of sustained usage. Overall, this study highlights the critical role of conscientiousness in fostering long-term engagement with generative AI systems and underscores the importance of cognitive perceptions in mediating personality effects. By integrating personality psychology with technology acceptance theory, the research provides theoretical and practical implications for designing personalized and adaptive AI interfaces.

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This research investigates the dynamic relationship of Artificial Intelligence (AI) and Sociolinguistics through bibliometric mapping in association with keyword content analysis. Utilizing 69 extracted publications (2013–2024) after systematic deduplication, the study combines quantitative trend analysis with keyword-based thematic interpretation. From an initial collection of 98 records obtained from Scopus (n = 64) and Web of Science (n = 34), a subset of 48 publications was sampled further pursuant to their conceptual relevance. Bibliometric analysis with the software ScientoPy and VOSviewer was employed to reveal publication trajectories, top contributors, influential journals, geographic patterns, and knowledge hot spots. This mapping was supplemented with a qualitative examination of the space mapped using five major terms: Computational Sociolinguistics, Natural Language Processing (NLP), ChatGPT, language and machine learning enabling us to track prevalent themes and concepts structuring the field. These results indicate that scholarly interest in the sociolinguistic aspects of AI-mediated communication has grown substantially, especially pertaining to language ideology, identity construction, and algorithmic influence on discourse. Instead of portraying computational methods as passive and neutral tools, the findings imply that technology such as NLP and large language models can be seen as both reproducing and destabilizing linguistic hierarchies, bringing to light critical questions regarding representation, diversity, and equity in digital space. In this work, we map the intersection of AI and Sociolinguistics through a combination of bibliometric mapping and keyword-based interpretation, thus giving an overview of how the field has evolved over time. This finding implies that debates about ethical and culturally inclusive AI design are coalescing into prominence in the literature.

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The paper is located within the dynamic relationship between language and computing, with the central objective being an increase in knowledge about how individuals interact with digitally mediated communication. Precisely, the paper attempts to explore the use of emojis as a tool of the digitally mediated communication system from a linguistic perspective, especially within the pragmatic and socio-linguistic framework among Jordanian university students. The main objectives involve four dimensions: determining the usage of emojis by members of this community, explaining subjects' tendencies towards emojis while conducting online discourse, understanding the meanings of a selected list of emojis derived from the subjects' point of view, and exploring possible gender differences within the Jordanian culture. Methodologically, this work followed the triangulation approach, wherein three research instruments were used: an online survey distributed across the sample population, consisting of 500 Jordanian university students from both genders, corpus analysis; and expert interviews. The sociolinguistic aspect of the analysis revealed that demographic focused on gender as a social variable regarding, some effects on emoji preferences and interpretations among participants. Finally, it contextualizes its findings within the general theoretical framework provided by three functional language, in relation to the phatic, conative, and expressive functions situated within a Jordanian cultural context. It finds the end by placing at the forefront of interest the dire need for interdisciplinary collaborations in exploring digitally mediated communication from linguistic vantages, particularly against a changing landscape where language and technology are crossed.

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This review discusses the significant advances in the architecture of memory systems, the enhancement of which has been among the most significant concerns in the process of creating systems with high bandwidth and low latency requirements. It has analyzed various fields that include virtually pipelined systems, millimeter-wave interface, and enhanced memory controllers, which would meet the requirements of high-performance computing (HPC). Among all the developments put in place, die-stacked DRAM (Dynamic Random Access Memory) systems offer a high bandwidth boost with the networks-on-chip scalable and meant to address the issues of congestion and incoherence in interconnecting cores. The literature survey also identifies some of the major developments in the mechanism of passing messages and memory management optimizations in distributed systems that have been found to be critical towards useful data transfer and processing in massive parallel computer systems. The advanced multi-port memory controllers are also observed to improve the bandwidth and efficiency in utilizing the resources. The review, however, points out some of the challenges that are still there, including the limitation of scalability of the centralized memory system, the latency issues of high-radix interconnects and the integration problems of heterogeneous computing systems. The evaluation highlights the need for new ways to tackle the limitations of current memory management techniques. Research will center on the possibilities of using machine learning to anticipate workloads and applying adaptive hybrid memory allocators to allocate across memory types dynamically. The goal of these techniques is to increase performance, bandwidth, latency, and energy efficiency in high-performance computing systems.

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The user interface design of an application is not limited to applications in the field of informatics, but also applies to various fields that utilize information technology. This also applies to educational evaluation. A good evaluation application is the result of a good user interface design. The aim of this study is to demonstrate the user interface design of the JOFF (Justification–Observation–Finalization–Functionalization) evaluation application which is derived from the DIVAYANA (Description–Input–Verification–Action–Yack–Analysis–Nominate–Actualization) evaluation model. The improved version used is Borg and Gall with a focus on three stages, together with: layout development, preliminary trying out, and revision of the initial checking out effects. The number of respondents involved in conducting the initial testing of the user interface design of the JOFF evaluation application was 44 people. Four experts and 40 IT vocational school teachers at several IT vocational schools in Bali. The statistical series device in this study turned into a questionnaire. Questionnaires are used to obtain quantitative data from subjects (respondents) who have completed initial trials. Analysis techniques of quantitative records accumulated from the preliminary trials were then used to analyze the usage of quantitative descriptive strategies. The effects of the study confirmed the great person interface layout of the JOFF evaluation software, which is a derivative of the DIVAYANA evaluation model, is classified as superb with a pleasant percent of 87.81%. The impact of this research on the field of educational evaluation is the existence of new knowledge about the importance of good user interface design.

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This study proposes a dual-dimensional framework to design such technologies in the EdTech industry using the conceptual framework methodology, which is based on thematic content analysis and theory-driven and evidence-based validation using scholarly literature, industry reports, and policies from the top EdTech organizations worldwide. The framework identifies trustworthiness as the key construct that integrates two interrelated dimensions. The governing dimension is operational at the managerial level and includes governance principles based on an extended Unified Theory of Acceptance and Use of Technology (UTAUT) framework and personalization governance principles based on three dimensions, such as intensity, tempo, and boundaries. The designing dimension is operational at the implementation level and identifies the technical requirements related to virtual sales personnel systems and AI promotion systems. The results of the validation process against known platform practices demonstrate a mixed pattern of alignment, with stronger support in regulatory-related areas and weaker support in governance-intensive domains where the framework extends current industry practice. The extension of the UTAUT model from personal acceptance to organizational governance represents a theoretical contribution with a link to existing research in personal acceptance and its expanded applicability. The three-dimensional personalization governance model has more detailed mechanisms than the current one-dimensional approaches. For educational technology organizations, the framework offers systematic guidance in the development of AI-based marketing systems that are trustworthy to users while being effective for organizational goals. Validation results indicate tempo governance and recommendation explainability as areas that need development in the industry for effective engagement with users in a sustainable manner.

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