Authors - Rowena Ocier Sibayan, Hazel C. Tagalog, Salvacion M. Domingo Abstract - Artificial intelligence (AI)–based writing tools are increasingly integrated into higher education as part of institutional technological‑intelligence infrastructures, providing automated feedback that can improve students’ writing quality and efficiency. This study evaluates AI writing tools as intelligent decision‑support systems and examines their impact on academic performance, student learning behavior, and institutional decisions about AI integration in higher education. A convergent parallel mixed‑methods design was adopted, combining quantitative analysis of writing performance with qualitative insights into student experiences. Data were collected from 100 undergraduate students with prior exposure to AI writing tools; quantitative measures included pre‑ and post‑intervention writing scores, rubric‑based assessments, and usage frequency, while qualitative data were gathered through structured questionnaires and reflective responses. Findings reveal statistically significant, large improvements in writing confidence, perceived clarity, and assignment performance, with mean grades increasing from 68.5% to 73.2%. Students also reported greater perceived independence in writing, although qualitative data indicate variability in engagement, ranging from critical use of AI feedback to more passive reliance. Concerns about data privacy showed minimal change and remained an area of uncertainty, underscoring the importance of governance and risk management in institutional AI deployments. The study concludes that AI writing tools enhance measurable writing outcomes but do not automatically foster deeper cognitive development. Their effectiveness depends on how students interpret and engage with AI feedback, underscoring the need for pedagogically guided and ethically responsible integration of AI in higher education.
Authors - Sowmini Devi Veeramachaneni Abstract - This paper addresses the challenge of balancing economic performance and environmental sustainability in supply chain optimization. We propose a bi-level hybrid optimization framework that integrates Particle SwarmOptimization (PSO) with Linear Programming (LP) for carbonaware business decision making. At the upper level, PSO dynamically optimizes the carbon penalty parameter, while at the lower level, LP ensures optimal and feasible operational decisions under supply chain constraints. The proposed framework automatically learns the trade-off between profit and emissions, eliminating the need for manual parameter tuning. Experimental results on both synthetic and real-world datasets demonstrate that the method effectively identifies Pareto-optimal solutions, achieves stable convergence, and exhibits strong robustness compared to standalone optimization approaches.
Authors - Vinca Valenia, Chelsea Calissta Liman Lim, Ichwan Masnadi Abstract - The swift embrace of artificial intelligence (AI) in the hospitality field has deeply modified the way services are provided and how customers interact, especially in the context of robot waiter systems in restaurant settings. Previous research mainly focused on operational efficiency; however, little has been done to understand how such technologies affect customer experience and their subsequent behaviors. This paper first determines customers' perception factors of AI-based robot waiter systems and their emotional involvement and satisfaction as consequences of the service encounter. Based on the Technology Acceptance Model (TAM), this study examines perceived usefulness and perceived ease of use in their contribution to customer attitudes formation toward AI-enabled services. Furthermore, emotional involvement as the main affective reaction that alters the customer attitudes-satisfaction link has been included in this investigation. Participants were selected based on their familiarity or interest in AI-based service technologies, and the quantitative method was used for the model testing. These results may shed light on the ways in which customer experience and satisfaction can be improved through AI-driven service innovations that take into account the cognitive and emotional aspects of consumer behavior. This paper is a significant addition to the field.
Authors - Brandon Octavianus, Charles Jonathan, Julia Christina, Ichwan Masnadi Abstract - The introduction of AI-driven self-service in restaurants has been swift, fundamentally altering the nature of customer service interactions. Customers’ experiences dining at these AI-enabled restaurants have also revealed that intelligent systems need to be more human-centered. The intention of this research is to discover the influence of Technology Readiness to Attitudes Toward Using restaurant self-order technology device with Perceived Ease of Use, Perceived Usefulness, and Perceived Speed as the mediators. Through a quantitative analysis of 200 respondents located in the JABOTABEK region that have experience using restaurant self-ordering technology. The data was evaluated through PLS-SEM system. This research reveals a positive effect of Technology Readiness on each variable, but it does not have considerable direct impact on Attitude Toward Using. The analysis of mediations revealed that customer attitude was positively impacted by Perceived Ease of Use and Perceived Speed, whereas Perceived Usefulness displayed insignificant effect. Overall, Perceived Speed was revealed as the strongest predictor implying that customers prioritize fast and easy service over useful functionality when interacting with intelligent restaurant systems. This study builds upon existing knowledge with an additional layer of understanding about human-centric AI implementation. Intelligent service technologies are meant to benefit both humans and organizations, but restaurants should also focus on providing quick, seamless, and easy customer experience through this technology. Keywords:
Authors - Agnes Gracia Hosiana, Catherine Puspita Sari, Tiurida Lily Anita Abstract - The rapid expansion of quick-commerce mobile applications has re-shaped how consumers purchase everyday essentials through digital platforms. Unlike traditional e-commerce, quick-commerce operates in a time-sensitive and mobile-first environment, making interface usability and trust particularly important in shaping user adoption. In this research, Technology Acceptance Model (TAM) is extended by adding interface usability and trust into the model with the aim of understand the factors that affect the users' behavioral intention toward the usage of ASTRO mobile application. This research used quantitative methodology through surveys conducted among 258 active users of ASTRO. The pro-posed model in this research was evaluated utilizing Partial Least Square Structural Equation Modeling (PLS-SEM). The findings show that interface usability significantly influences perceived ease of use and perceived usefulness. Further-more, trust positively impacts both attitude toward use and behavioral intention to use. Both perceived usefulness and perceived ease of use also positively impact user attitude. These results confirm that TAM remains relevant in the quick-commerce context, while also demonstrating that interface usability and trust enhance its explanatory power in mobile retail environments. This research offers contributions to the technology adoption literature by providing a context-sensitive ex-tension of TAM for quick-commerce applications and delivers practical recommendations for platform developers to optimize user experience, strengthen trust, and encourage sustained adoption.
Authors - Tanvi Pawar, Sachin S. Pande, Emmanuel M Abstract - Sarcasm detection in social media text is a NLP challenge, as sarcastic statements inverse meaning of the statement as sarcastic statements hide the real meaning. This problem intensified on platforms like Reddit by informal phrasing, community-specific references, and implicit cultural knowledge. This paper introduces a RoBERTa-based classification framework which addresses three core issues: contextual impoverishment of isolated comments, unstable training caused by random initialization, and catastrophic forgetting during fine-tuning. These are handled via inline textual metadata fusion (encoding subreddit identity and upvote score into the input sequence), a structured multi-layer classification head, and a biphasic two-stage training method with differential learning rates. Trained on a balanced 500,000-sample subset of the SARC dataset, the model achieves 68.36% accuracy with stable, monotonic convergence across all training epochs. Near-symmetric false positive and false negative rates shows that the model does not favor a single class. Future directions include knowledge graph integration, model distillation, multi-class sarcasm taxonomy, and multilingual extension.
Authors - Augustus Abbey, Benjamin Ghansah, Stephen Opoku Oppong, Joseph Kwabena Essibu, Charles Buabeng-Andoh, Christopher Yarkwah, Mathias Abgeko Abstract - The adoption of Learning Management Systems (LMSs) in higher education has transformed teaching and learning by enhancing digital content delivery, assessment processes, and collaborative engagement. Despite their widespread use, variations in students’ learning experiences and academic outcomes suggest that the effectiveness of LMS platforms is influenced by both system features and learner characteristics. This study investigates the extent to which specific LMS functionalities contribute to students’ academic performance and examines how demographic and learner-related factors moderate LMS usage and learning outcomes. A cross-sectional survey design was employed, involving 381 students from the University of Education, Winneba. Data were collected using structured questionnaires and analyzed through descriptive statistics, correlation analysis, and multiple regression techniques. The findings reveal that key LMS dimensions, including content delivery mechanisms, communication and interaction tools, navigation usability, and system accessibility, significantly influence students’ academic performance and learning experiences. Further-more, demographic and learner-specific variables such as age, socioeconomic back-ground, language proficiency, and learning preferences were found to shape the effectiveness and utilization of LMS platforms. The study underscores the importance of inclusive and user-centered LMS design approaches that accommodate diverse learner needs and promote equitable access to digital learning environments. The findings con-tribute to the growing discourse on technology-enhanced learning by providing empirical insights for educational institutions, LMS developers, and policymakers seeking to optimize the accessibility, usability, and pedagogical effectiveness of LMS platforms in higher education.
Authors - Sowmini Devi Veeramachaneni Abstract - Modern supply chain systems must balance economic efficiency with environmental sustainability. Traditional optimization approaches, such as linear programming (LP), provide optimal solutions but often struggle with scalability in large-scale networks. This paper proposes a clustering-based framework to reduce the computational complexity of supply chain optimization while preserving solution quality. The method groups suppliers and demand points using feature-aware clustering based on cost and emission profiles, and solves a reduced transportation problem using LP. Experimental results on a real-world dataset demonstrate that the proposed approach achieves near-optimal performance, with less than 7% deviation in profit and less than 2% deviation in emissions, while reducing computation time by nearly an order of magnitude. An ablation study further highlights the trade-off between computational efficiency and solution fidelity controlled by the number of clusters. The proposed framework provides a practical and scalable solution for large-scale, sustainability-aware supply chain optimization.
