Exploring AI Tools and Large Language Modelsfor Students' Performance Enhancement in RiddleBased Logical Reasoning

The emergence of Generation Z (Gen Z) as a cohort characterized by their immersion in technology has prompted significant shifts in educational paradigms, particularly concerning the integration of AI tools. This study aims to investigate the extent and implications of Gen Z's reliance on AI tools within educational contexts. Employing a mixed-methods approach, the research employs convenience sampling to distribute questionnaires to students, acknowledging the critical importance of their availability. Through an examination of various ML and regression methods, the study evaluates how effectively each method identifies relevant features for classification tasks and explains the increase in the dependency of students on AI tools. The findings shed light on the factors driving Gen Z's dependency on AI tools, including curriculum integration, performance enhancement, accessibility, skill development, and dependency factors that pave the way for informed decision-making and strategic planning in educational settings.

This article explores the development and applications of prestressed concrete pavements, highlighting their distinct advantages in structural performance and maintenance compared to conventional concrete pavements. Introduced in the 1930s shortly after the advent of prestressed building structures, prestressed concrete has become a staple in bridge and building construction, yet its integration into pavement technology remains innovative. Prestressed pavements are crucial in overcoming the limitations of plain concrete's tension strength, which is essential given the tensile stresses pavements endure. The article details the evolution of pavement types in the U.S., focusing on their structural characteristics, costs, and the transition towards more sustainable and economically viable materials. With rising concerns about the longevity and efficiency of standard pavement designs, prestressed pavements offer a promising alternative that aligns with rapid construction practices necessary for economic feasibility. Furthermore, the paper discusses the specific applications of prestressed pavements in U.S. highways and airports, emphasizing the substantial cost benefits and performance enhancements over traditional methods. These pavements utilize post-tensioned steel to achieve desired stress levels and are constructed with innovations that reduce material use and energy consumption, aligning with broader environmental conservation efforts. In conclusion, the deployment of prestressed concrete pavements in highway and airport construction has demonstrated significant advantages in terms of structural performance and resource efficiency. This exploration underscores the potential for wider adoption of prestressed technology in pavement applications, promising enhanced durability and cost-effectiveness. (Abstract generated by AI tool ChatGPT 4)

Artificial Intelligence, or AI, encloses within itself, the various techniques and algorithms that facilitate the augmentation of human decision-making by adding computational infrastructure to it. It includes specific methodologies and programs that help make conclusive deductions based on the data available for it to work. The paper attempts to provide an introduction to the different types of AI tools along with their applicability. Furthermore, it attempts to define the role of AI in the enhancement of structural performance. Civil Engineering is a discipline that is highly dependent on the judgement of the Engineer-on-field. AI can assist this judgement by providing meaningful insights on the real world problems based on the data available from the site thereby improving the quality of the work and simultaneously serve as a guideline for novice engineers. This paper highlights the significance of AI in different domains of Civil Engineering and provides a broad picture of the future possibilities. It provides a review of the prominent works that have been carried out in the area and segregates the researches needing immediate attention. The areas of focus include structural performance parameters including prediction of structural properties, structural health monitoring and infrastructure-specific issues in buildings and bridges.

Given the recent emergence of artificial intelligence as an important topic that can contribute to improve curricula and lecture delivery, an increasing number of scholars are investigating its impact in various educational fields. The ChatGPT, which is an artificial intelligence model developed by OpenAI, represents a significant advancement in the generative artificial intelligence area. Since its announcement, integrating the ChatGPT into computer programming curricula - and into other scientific curricula - has yielded some challenges. The main challenge is clearly evident in the conduct of in-class tests and in-lab assignments, where the students are given specific tasks to be accomplished within a certain time frame. As they might seek help from ChatGPT, these types of assessments could be considered a potential threat to academic integrity and may be viewed as a form of academic dishonesty. This study aims at integrating ChatGPT into computer programming curricula, exploring its potential to enhance undergraduate education. It follows a mixed methods approach to examine the potential integration of ChatGPT in teaching computer programming as a supplementary tool. A quasi-experimental design is followed, in which an experimental group is allowed to use ChatGPT and compared to a control group that was not. A research sample of 26 undergraduate students (13 males, 13 females) from the College of Education at Sultan Qaboos University participated in the study. The methodology encompassed three research instruments: in-class exams, in-lab assignments, and semi-structured interviews. These research instruments were utilized to assess the impact of ChatGPT on students' academic performance, which served as both independent (use of ChatGPT) and dependent (student performance) variables. The quantitative analysis revealed a significant enhancement in students' performance, while the qualitative analysis of semi-structured interviews indicated that participants view ChatGPT as a valuable support for learning. Feedback from participants suggested combining ChatGPT with traditional teaching methods to optimize learning outcomes. This study highlights the feasibility and educational benefits of incorporating AI tools like ChatGPT into teaching methodologies. It suggests that such integration can provide a more engaging and effective learning environment, potentially revolutionizing computer programming education. This paper supports e-learning practice by integrating AI-driven tools like ChatGPT into the educational framework and advances the e-learning area by demonstrating these technologies' potential to improve student academic performance in the learning environments. However, the study also acknowledges the need for further research to explore the long-term effects of AI integration in educational settings and to address any emerging challenges. These findings propose a promising direction for future curricular enhancements and suggest an effective method for the integration of AI technologies to support and enrich traditional educational frameworks.

This study investigates the impact of AI integration in CRM technology on three critical aspects: AI-based customization, enhanced customer database, and AI integration in the CRM operations. The first research question was to establish whether there is a substantial enhancement of these aspects of CRM systems with the integration of AI. In the present study, the research used a sample of 100 respondents and adopted a quantitative research method with a random sampling technique to collect data. The use of descriptive statistics and Pearson correlation analysis was employed in the analysis of the relationship between the independent variable and the dependent factors. The analysis showed that there were low and insignificant association between the levels of AI integration and the three dependent variables. More particularly, the results revealed that the degree of AI integration was significantly but weakly and negatively related with the extent of AI personalisation (r = -0. 181), AI customer data management (r = -0. 104) and AI automation (r = -0. 134). The results of this study indicate that, counter to assumptions, AI integration did not significantly improve personalization, data handling, or the automation of CRM systems. Therefore, the study concludes that the expected positive effects of AI on the CRM functionalities were not observed in this sample, suggesting deficiencies in existing AI applications or their incorporation into CRM systems. The results point to the need for more research that can help to establish the efficiency of AI tools, how these tools can be fine-tuned and how they can be aligned to the objectives of CRM. If these factors are managed, they might assist with the realization of the potential of AI in redefining CRM systems. Thus, the findings of this research stress the need for further development of the AI tools and the elimination of the practical issues that hinder the enhancement of CRM performance.

Aim/Purpose: The purpose of this review is to investigate current research on gamification for adult lifelong learning, including tertiary education, work-based learning, and informal learning. Background: As teaching methods still rely on traditional methods, the necessity of a paradigm shift in teaching methods to foster motivation and leverage better learning outcomes remains a challenge. One of the proposed solutions is to make learning more attractive via contextualized designs that follow the principles of gamification. Methodology: To address the research questions, a systematic literature review was conducted, based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) protocol. As part of the systematic literature review, articles published between 2014 and 2024 were sought. The search query consisted of the various Boolean operators and search terms. The search was conducted in ScienceDirect, SpringerLink, ERIC, and Scopus. Of the 232 articles identified, 141 were selected by applying inclusion and exclusion criteria. After reviewing the abstracts of the selected studies, 36 articles were included in the review. Contribution: This study contributes by providing a thorough review of the current state of gamified learning in the field of adult education. It synthesizes previous research and offers insights into technologies, theories, elements, and learning outcomes of gamified educational interventions. Moreover, this review highlights the potential of gamification to enhance learning experiences through the integration of game elements into educational design, resulting in more efficient and productive training models. Findings: The study revealed that gamified learning is applicable in a variety of subjects, predominantly in areas related to business administration, economics, and pedagogical studies, as corroborated by prior research. It is diverse and depends on several factors, including the educational purpose, the learning profile, the established learning objectives, and the desired learning outcomes. Gamified learning yields significant learning outcomes for adult learners. Recommendations for Practitioners: Gamification offers significant potential for enhancing motivation, engagement, and learning in various key areas, including formal education in higher education, corporate training, healthcare, workplace settings, and sustainability. Moreover, when a designer integrates game elements into non-gameful environments, it is crucial to consider specific design parameters. The research indicates that when game elements are thoroughly designed to align with the learning characteristics of adult learners and are based on learning theories related to gamified learning, they enhance the motivation to engage in learning activities. This, in turn, leads to an increase in the learners’ self-efficacy. Recommendation for Researchers: Further investigations into the application of gamified adult education through Learning Management Systems (LMS) are needed. This includes the effectiveness and sustainability of using advanced applications of adaptive and personalized gamification with embedded artificial intelligence in open learning environments, which appears to be a new trend. Impact on Society: The key outcomes are associated with the enhancement of learning performance and the acquisition of practical knowledge and skills that can be applied in real-world scenarios, such as work environments. In these environments, adults will be able to solve practical problems and enhance their work performance and productivity. Future Research: As research continues to verify the effectiveness of gamification in terms of learning benefits, further investigation is needed to explore different educational levels and span the continuum of lifelong learning, as well as to examine new emerging technologies, especially generative AI tools.

The challenges of handling imbalanced datasets in machine learning significantly affect the model performance and predictive accuracy. Classifiers tend to favor the majority class, leading to biased training and poor generalization of minority classes. Initially, the model incorrectly treats the target variable as an independent feature during data generation, resulting in suboptimal outcomes. To address this limitation, the model was adjusted to more effectively manage target variable generation and mitigate the issue. This study employed advanced techniques for synthetic data generation, such as synthetic minority oversampling (SMOTE) and Adaptive Synthetic Sampling (ADASYN), to enhance the representation of minority classes by generating synthetic samples. In addition, data augmentation strategies using Deep Conditional Tabular Generative Adversarial Networks (Deep-CTGANs) integrated with ResNet have been utilized to improve model robustness and overall generalizability. For classification, TabNet, a model tailored specifically for tabular data, proved highly effective with its sequential attention mechanism that dynamically processes features, making it well suited for handling complex and imbalanced datasets. Model performance was evaluated using a novel approach of training synthetic data and testing on real data (TSTR). The framework was validated on the COVID-19, Kidney, and Dengue datasets, achieving impressive testing accuracies of 99.2%, 99.4%, and 99.5%, respectively. Furthermore, similarity scores of 84.25%, 87.35%, and 86.73% between the real and synthetic data for the COVID-19, Kidney, and Dengue datasets, respectively, confirmed the reliability of the synthetic data. TabNet consistently showed substantial improvements in F1-scores compared to other models, such as Random Forest, XGBoost, and KNN, emphasizing the importance of selecting the right synthetic data augmentation techniques and classifiers. Additionally, SHapley Additive exPlanations (SHAP)-based explainable AI tools were used to interpret model performance, providing insights into feature importance and its impact on predictions. These findings confirm that the proposed approach enhances the accuracy, robustness, and interpretability, offering a valuable solution for addressing data imbalance in classification tasks.

Constructing arguments, applying logical reasoning, and developing intellectual skills are fundamental to academic success in postgraduate education and qualitative research. The study objective of this paper aims at critically analyzing argument construction, logical reasoning, and intellectual skill development as fundamental components of postgraduate education and qualitative research. The analysis highlights the importance of these elements in fostering critical engagement, advancing knowledge, and contributing to scholarly discourse. The paper draws on academic literature to offer a nuanced interpretation of these interconnected dimensions and explores strategies to enhance argumentation, reasoning, and intellectual skills in postgraduate education. Argument construction is identified as the cornerstone of academic dialogue, requiring structured claims supported by evidence and reasoning. The analysis supports this assertion using evidence from recent studies highlighting the value of argumentation training in enhancing critical thinking and research writing, advocating for its integration into postgraduate curricula. Notably, findings from previous studies link organized argumentation to students’ analytical skills and ability to interact with complex data. The analysis also reports that logical reasoning is the cornerstone of effective argumentation, offering systematic methods to connect premises to conclusions. Deductive reasoning is highlighted for its role in hypothesis testing and causal analysis, ensuring precise and reliable conclusions. Inductive reasoning, a bottom-up approach, uncovers patterns and trends from specific observations, proving essential for theory development and exploratory research. Abductive reasoning facilitates plausible explanations for poorly defined phenomena, while retroductive reasoning identifies underlying causes to generate alternative theoretical models. Results of the assessment also emphasize the need for postgraduate students to develop intellectual skills, including critical thinking, synthesis, and dialectic reasoning. Notably, student scholars employ this skill to engage deeply with subject matter and navigate complex phenomena. Structured educational interventions, such as formal logic training and argumentative writing activities, significantly enhance higher-order thinking skills. Technology, with its transformative role, is reshaping the landscape of education. AI tools and digital learning platforms offer real-time feedback, interactive tasks, and reflective thinking opportunities, paving the way for a more engaging and practical learning experience. An assessment of extant literature also reveals that experiential learning and problem-based frameworks strengthen intellectual skills by connecting theoretical concepts with real-world applications. Interdisciplinary research, collaborative projects, and dynamic problem-solving contexts prepare students for professional challenges and foster adaptability. These approaches underscore the importance of integrating academic learning with hands-on experiences to enhance intellectual engagement and flexibility. The analysis confirms that constructing arguments, applying logical reasoning, and developing intellectual skills are essential for postgraduate education and qualitative research. Deductive, inductive, abductive, and retroductive reasoning methods provide a viable framework for argumentation, while intellectual skills enable researchers to navigate complex phenomena and contribute effectively to their disciplines. The value of interdisciplinary collaboration is underscored, as it enriches research with diverse perspectives and fosters a more comprehensive understanding of complex phenomena. Future research should explore innovative strategies to support argument construction, interdisciplinary collaboration, and intellectual growth, ensuring the continued evolution of postgraduate education and academic inquiry.

This essay explores the nuanced impact of generative AI technologies on management and business education, framed through three paradoxes: the Expertise Paradox suggests that AI’s adequate performance at lower-level tasks may weaken students’ development of higher-level thinking; the Innovation Paradox states that AI’s creativity aid could stifle original thinking; and the Equity Paradox highlights AI’s potential to provide immense gains to experts but disproportionately harm novices. We take the position that without “sensible” AI use guidelines in management education, AI is likely to have a net-negative effect on learning. This stance is based on our trials with ChatGPT on various cognitive tasks organized around the revised Bloom’s Taxonomy of learning. We identify areas where AI tools can enhance learning, such as comprehending established subject domains, as well as areas where they exhibit significant limitations, such as logical reasoning and critical thinking. We caution against the potential deskilling in critical thinking due to students’ overreliance on AI for basic tasks. To alleviate these challenges, we recommend sensible AI uses by students that support skill development without fostering overreliance. We also suggest how faculty, administrators, and employers may support students in getting the most out of this new tool.

Recent advancements in Natural Language Processing (NLP) technologies have been driven at an unprecedented pace by the development of Large Language Models (LLMs). However, challenges remain, such as generating responses that are misaligned with the intent of the question or producing incorrect answers. This paper analyzes various Prompt Engineering techniques for large-scale language models and identifies methods that can optimize response performance across different datasets without the need for extensive retraining or fine-tuning. In particular, we examine prominent Prompt Engineering techniques including In-Context Learning (ICL), Chain of Thought (CoT), Retrieval-Augmented Generation (RAG), Step-by-Step Reasoning (SSR), and Tree of Thought (ToT), and we apply these techniques to leading LLMs such as Gemma2, LlaMA3, and Mistral. The performance of these models was evaluated using the AI2 Reasoning Challenge (ARC), HellaSwag, Massive Multitask Language Understanding (MMLU), TruthfulQA, Winogrande, and Grade School Math (GSM8k) datasets across metrics such as BLEU, ROUGE, METEOR, BLEURT, and BERTScore. The experimental results indicate that the most suitable Prompt Engineering technique can vary depending on the characteristics of each dataset. Specifically, for datasets emphasizing mathematical and logical reasoning, Prompt Engineering strategies centered around CoT, SSR, and ToT were found to be advantageous. For datasets focusing on natural language understanding, ICL-centric strategies were more effective, while RAG-based strategies were beneficial for datasets where factual accuracy is crucial. However, it was also observed that the optimal combination of Prompt Engineering techniques could differ depending on the specific LLM, indicating that fine-tuning the Prompt Engineering approach to the model and dataset is essential for achieving the best performance. The findings indicate that as LLMs become more advanced, their reliance on Prompt Engineering (PE) techniques diminishes, yet the magnitude of their performance improvement when PE strategies are applied increases. Furthermore, these advanced models tend to depend less on ICL techniques while exhibiting a greater reliance on RAG strategies. It is also evident that implementing RAG with PE-based preprocessing yields superior performance enhancements compared to the mere application of RAG on raw data.

In the information age, semantic parsing technology drives efficiency improvement and accelerates the process of intelligence. However, it faces complex understanding, data inflation, inappropriate evaluation, and difficult application of advanced large models. This study analyses the current challenges and looks forward to the development trend of the technology. Specific approaches include: this study adopts a systematic review method and strictly follows the PRISMA framework, deeply analyzes the key ideas, methods, problems, and solutions of traditional and neural network methods, and explores the model performance, API application, dataset, and evaluation mechanism. Through literature analysis, the technology is classified according to its application scenarios. Then, the practical application contributions are summarized, current limitations such as data size, model performance, and resource requirements are analyzed, and future directions such as dataset expansion, real-time performance enhancement, and industrial applications are envisioned. The results of the study show significant advances in semantic parsing technology with far-reaching impacts. Traditional and neural network methods complement each other to promote theoretical and practical innovation. In the future, with the continuous progress and in-depth application of machine learning technology, semantic parsing technology needs to further deepen the research on logical reasoning and evaluation, to better cope with technical challenges and lead the new development of natural language processing and AI.

AI-driven writing assistants in EFL classrooms have revolutionized academic writing pedagogy, offering students immediate feedback on grammar, coherence, and organization. Although AI tools have demonstrated efficacy in improving linguistic precision, their influence on developing critical thinking remains ambiguous, especially among varying competency levels. Current study predominantly emphasizes AI's impact on grammatical corrections, although there is a paucity of knowledge on its effect on higher-order cognitive involvement, including argumentation and reasoning abilities. This study examines the interaction between EFL students and AI feedback, assessing its impact on promoting or obstructing critical thinking. The study reveals that, through examining pre-test and post-test writing evaluations, student reflections, and AI feedback patterns, lower-proficiency students (B1) predominantly depend on AI for superficial adjustments. In contrast, advanced learners (C1) interact with AI-generated ideas more critically. Nonetheless, AI's constraints in assessing argument strength and logical reasoning demonstrate that it cannot entirely supplant human feedback. These findings indicate that AI should be deliberately integrated with teacher support to optimize its advantages while reducing over-dependence. Future studies should investigate AI-human hybrid feedback models to improve language proficiency and critical thinking skills in academic writing.

Despite the popularization of generative AI technology since the advent of ChatGPT, the reality of insufficient systematic education has amplified the importance of prompt engineering training. This study explored the effects of generative AI prompt engineering education on students' core competencies in a university liberal arts education setting. Education on eight prompt engineering techniques was provided to 71 students enrolled in a liberal arts course at J University. Changes in their confidence in AI utilization, perception of prompt engineering, and eight core competencies (problem essence identification, systematic planning/thinking, critical thinking/evaluation, integrative thinking, problem-solving, logical reasoning, information structuring, and efficient information search/utilization) were measured before and after the training. The collected data were comprehensively analyzed using paired samples t-tests, descriptive statistics, Pearson correlation analysis, and content analysis of open-ended responses. The results showed that after the training, students' confidence in AI model utilization (from 2.68 to 4.22) and their perceived helpfulness of prompt engineering for academic (from 2.93 to 4.40) and work (from 2.78 to 4.08) purposes significantly improved. All eight competencies showed significant improvement, with particularly notable increases in 'efficient information search/utilization' (3.00→4.30), 'information structuring' (2.92→4.25), and 'systematic planning/thinking ability' (2.90→4.25). Techniques involving clear and structured instructions, such as role assignment and Fukatsu prompting, were found to be effective and showed a strong correlation with the improvement of core competencies. Students applied the learned content to their actual studies, experienced its effectiveness, and expressed a demand for more advanced learning. In conclusion, prompt engineering education goes beyond simple AI tool utilization to substantially strengthen students' core competencies, providing important implications for future AI literacy curriculum development and education in higher-order thinking skills.

This paper presents strategies for effectively integrating AI tools into programming education and provides recommendations for enhancing student learning outcomes through intelligent educational systems. Learning computer programming is a cognitively demanding task that requires dedication, logical reasoning, and persistence. Many beginners struggle with debugging and often lack effective problem-solving strategies. To address these issues, this study investigates PyChatAI—a bilingual, AI-powered chatbot designed to support novice Python programmers by providing real-time feedback, answering coding-related questions, and fostering independent problem-solving skills. PyChatAI offers continuous, personalised assistance and is particularly beneficial for students who prefer remote or low-pressure learning environments. An empirical evaluation employing a Solomon Four-Group design revealed significant improvements across all programming skill areas, with especially strong gains in theoretical understanding, code writing, and debugging proficiency.

With the rapid development of Generative Artificial Intelligence (GAI) technology, programming education has emerged as a core application domain. Through a systematic literature review of 45 relevant studies from the Semantic Scholar database from 2023-2025, this study examined the current applications of GAI as an auxiliary learning tool in programming education, and its impact on learning outcomes. The findings reveal that GAI-assisted instruction demonstrates significant effectiveness across seven learning indicators: programming knowledge and skills, computational thinking and logical reasoning, problem-solving ability, programming self-efficacy, learning achievement, code quality, and learning behaviors and engagement. While the majority of studies confirm that GAI enhances student performance in various areas such as task completion, test performance, code structure and quality, and promoting self-directed learning, some studies indicate that GAI use may reduce learning depth and lead to over-dependence in specific tasks or complex reasoning contexts. From a pedagogical perspective, GAI prompts a transformation in teachers’ roles from knowledge transmitters to learning facilitators and guides, necessitating corresponding adjustments in curriculum design and assessment approaches. Based on the empirical findings, this study constructs an integrated conceptual model for GAI-assisted programming education integrating four core dimensions: implementation context factors, core influencing factors, learning performance indicators, and learning outcomes. The study identifies AI tool selection, students’ foundational abilities, and task complexity as key variables affecting learning effectiveness, and synthesizes seven patterns of student learning behavior changes under GAI assistance, providing concrete theoretical foundations and implementation guidelines for educational practice.