Search Results (163)

This study explores how Speech and Elocution Training (SET) activates language potential and fosters career-oriented development among higher vocational students through self-efficacy mechanisms. Through qualitative interviews with four vocational graduates who participated in SET 5 to 10 years ago, the research identifies three key findings. First, SET comprises curriculum content (e.g., workplace communication modules such as hosting, storytelling, and sales pitching) and classroom training using multimodal TED resources and Toastmasters International-simulated practices, which spark language potential through skill-focused, realistic exercises. Second, these pedagogies facilitate a progression where initial language potential evolves from nascent career interests into concrete job-seeking intentions and long-term career plans: completing workplace-related speech tasks boosts confidence in career choices, planning, and job competencies, enabling adaptability to professional challenges. Third, SET aligns with Bandura’s four self-efficacy determinants; these are successful experiences (including personalized and virtual skill acquisition and certified affirmation), vicarious experiences (via observation platforms and constructive peer modeling), verbal persuasion (direct instructional feedback and indirect emotional support), and the arousal of optimistic emotions (the cognitive reframing of challenges and direct desensitization to anxieties). These mechanisms collectively create a positive cycle that enhances self-efficacy, amplifies language potential, and clarifies career intentions. While highlighting SET’s efficacy, this study notes a small sample size limitation, urging future mixed-methods studies with diverse samples to validate these mechanisms across broader vocational contexts and refine understanding of language training’s role in fostering linguistic competence and career readiness. Full article

A persistent challenge in training future philology educators is engaging students in deep textual analysis across historical periods—especially in large classes where limited resources, feedback, and assessment tools hinder the teaching of complex linguistic and contextual features. These constraints often lead to superficial learning, decreased motivation, and inequitable outcomes, particularly when traditional methods lack interactive and scalable support. As digital technologies evolve, there is increasing interest in how Artificial Intelligence (AI) can address such instructional gaps. This study explores the potential of conversational AI chatbots to provide scalable, pedagogically grounded support in philology education. Using a mixed-methods case study, twenty-six (n = 26) undergraduate students completed structured tasks using one of three AI chatbots (ChatGPT, Gemini, or DeepSeek). Quantitative and qualitative data were collected via usability scales, AI literacy surveys, and semi-structured interviews. The results showed strong usability across all platforms, with DeepSeek rated highest in intuitiveness. Students reported confidence in using AI for efficiency and decision-making but desired greater support in evaluating multiple AI-generated outputs. The AI-enhanced environment promoted motivation, autonomy, and conceptual understanding, despite some onboarding and clarity challenges. Implications include reducing instructor workload, enhancing student-centered learning, and informing curriculum development in philology, particularly for instructional designers and educational technologists. Full article

This research examined the domain of intelligent transportation systems (ITS) by analyzing the impact of scholarly work and thematic prevalence, as well as focusing attention on vehicles, their technologies, cybersecurity, and related scholarly technologies. This was performed by examining the scientific literature indexed in the Scopus database. This study analysed 2919 documents published between 2018 and 2025. The findings indicated that the highest and most significant journal was derived from IEEE Transactions on Vehicular Technology, with significant standing to the growth of communication and computing on vehicles with edge computing and AI optimization of vehicular systems. In addition, important PST research conferences highlighted the growing interest in academic research in cybersecurity for vehicle networks. Sensor networks, pose forensics, and privacy-preserving communication frameworks were some of the significant contributing fields marking the significance of the interdisciplinary nature of this research. Employing bibliometric analysis, the literature illustrated the multiple channels integrating knowledge creation and innovation in ITS through citation analysis. The outcome suggested an increasingly sophisticated research area, weighing technical progress and increasing concern about security and privacy measures. Further studies must investigate edge computing integrated with AI, advanced privacy-preserving linguistic protocols, and new vehicular network intrusion detection systems. Full article

Medical students face cognitive overload and disengagement due to the rigorous demands of their education. This study evaluates the impact of Medimon Learning Cards, a mnemonic-based trading card game, on engagement, satisfaction, and knowledge retention among students in a medical immunology course. These cards incorporate visual and linguistic mnemonics, coupled with strategic gameplay, to create an interactive learning experience. This study was conducted on 39 first-year medical students enrolled in an immunology course, divided into experimental Learning Card and control groups. The Learning Card group received the Medimon Learning Cards and participated in a structured play session, while both groups received identical in-class instruction. The results from the Situational Interest Survey for Multimedia revealed high engagement and satisfaction among the Learning Card group, with students expressing enthusiasm for expanding the scope of the cards to other topics. However, no significant differences were observed in knowledge retention or exam performance between the groups. These findings suggest that Medimon Learning Cards can serve as a valuable supplementary tool with which to enhance motivation and interest, though their impact on cognitive outcomes requires further investigation. These findings suggest that incorporating mnemonic-based card games such as Medimon Learning Cards can enhance learner motivation and interest, although their impact on cognitive outcomes warrants further study. Full article

Language-in-education policies often serve hidden political and economic agendas, and thus language policy research must examine policies beyond official state discourse. This article critically analyzes Morocco’s Language Alternation Policy (LAP), introduced in 2019, using the historical–structural approach. It examines the broader historical context and structural factors that shape the adoption and implementation of LAP. While the official policy discourse frames LAP as an egalitarian reform aimed at promoting balanced multilingualism by alternating instructional media in science education, its de facto implementation reveals a stark contradiction. The ideological underpinnings of LAP are the resurgence of French as the exclusive medium of instruction in science and technology classrooms. This policy undercuts a decades-long Arabization of science and the promotion of the Amazigh language, as well as denying Moroccans the potential advantages of learning English. The disparity between official policy discourse and implementation reveals the influence of France’s neocolonial agenda, exercised through Francophonie, international clientelism, and financial patronage. Through implementing LAP to align with France’s interests in Morocco, French-trained political actors undermine the country’s decolonization efforts and preserve the long-standing socioeconomic privileges of the francophone elite. We analyze how LAP functions ideologically to resolidify France’s cultural and linguistic hegemony and reinforce pre- and post-independence linguistic and social inequalities. Full article

In recent years, researchers in computational linguistics have shown a growing interest in the style of text, with a specific focus on the text style transfer (TST) task. While numerous innovative methods have been proposed, it has been observed that the existing evaluations are insufficient to validate the claims and precisely measure the performance. This challenge primarily stems from rapid advancements and diverse settings of these methods, with the associated (re)implementation and reproducibility hurdles. To bridge this gap, we introduce a comprehensive benchmark for TST known as TSTBench. TSTBench includes a codebase encompassing implementations of 13 state-of-the-art algorithms and a standardized protocol for text style transfer. Based on the codebase and protocol, we have conducted thorough experiments across seven datasets, resulting in a total of 7000+ evaluations. Our work provides extensive analysis from various perspectives, explores the performance of representative baselines across various datasets, and offers insights into the task and evaluation processes to guide future research in TST. Full article

This study provides a bibliometric analysis of polarity sensitivity research from 1980 to 2023, examining intellectual structure, collaboration patterns, and emerging trends. Analysing 835 documents using Bibliometrix (V.4.1.0), CiteSpace (V.6.1.R6), and VOSviewer (V1.6.18), we identify three evolutionary phases: (1) foundational theoretical development (1980–2000), transitioning from syntactic to semantic-based theories; (2) methodological diversification (2000–2010), incorporating cognitive–pragmatic frameworks and corpus-based studies; and (3) contemporary integration (2010–2023), marked by multidisciplinary approaches. Co-citation analysis reveals three intellectual clusters centred on formal semantics, pragmatic approaches, and minimalist frameworks. Geographic analysis shows the United States as the leading contributor, followed by Germany and the United Kingdom. Collaboration network analysis underscores intensive transatlantic exchanges and emerging computational contributions from Asia. Keyword co-occurrence analysis (165 terms) demonstrates theoretical sophistication and empirical integration, with growing interest in neurocognitive approaches, cross-linguistic variations, and interface phenomena. Challenges include reconciling universal principles with language-specific variations and integrating processing models with formal theories. Promising research directions involve the combination of computational modelling, diachronic studies, and applications in language teaching and natural language processing. This study maps the intellectual landscape of polarity sensitivity research while suggesting future directions toward unified theories that address universal and language-specific patterns. Full article

The results of geotourism development in Slovakia do not correspond much to the idea of geotourism as a social priority, nor to the declared increased interest in all forms of responsible tourism. The development of geotourism, in the strict sense of the word, is a political phenomenon; here, it exists outside the legal framework. This paper examines the question of whether, to what extent, and in what manner the promotion of leading principles (such as the idea of sustainability and its manifestation in various forms of regulated tourism) should be enshrined in positive law, and what specific benefits this might bring for the development of geotourism in Slovakia. Given the questions posed are of a kind that jurisprudence may answer, the methods chosen are drawn from legal science, though also intersecting with several other social sciences. Accordingly, the approach is one of doctrinal interpretation, based on the scientific study of valid law. Slovak law as a whole, specifically as it relates to the implementation of sustainable development and regulated forms of tourism, thus sets the outer limits of the application of these interpretative methods (including linguistic, historical, and logical interpretation of law, among others). The article answers the question in the affirmative way and outlines prospects for positive change should current approaches be changed. Full article

Background: Chinese Americans, the largest Asian American subgroup in the U.S., face linguistic, cultural, and socio-economic barriers to dementia prevention. To promote brain health in this population, a culturally tailored community approach is essential. This study evaluates a culturally tailored community brain health education program to enhance brain health knowledge and motivate lifestyle changes to prevent the risk of dementia among Chinese Americans aged 50 or older. Methods: The program was developed and evaluated in four phases. First, we assessed participants’ interests in brain health topics, availability, and preferred delivery modes. Next, experts on the identified topics developed educational content and outcome assessments. The third phase focused on implementing a six-session program covering general knowledge about Alzheimer’s disease and related dementias, diet, sleep, physical exercise, health checks, and mindfulness. Finally, we evaluated the program’s feasibility and effectiveness using pre–post surveys, feedback questionnaires, and focus groups. Results: Seventy-seven participants registered for the program, and sixty-nine (90%) attended at least four sessions. The quantitative results, based on paired t-tests, showed significant increases in brain health knowledge, sleep quality, and behavioral motivation for lifestyle changes, and a decrease in depressive symptoms, with two-tailed p-values lower than 0.05. The qualitative results further revealed promising feasibility and acceptability, as well as the perceived benefits of the program. Conclusions: The findings highlight the feasibility, acceptability, and potential effectiveness of a culturally tailored community education approach for promoting brain health and lifestyle changes. Sustained community outreach and education efforts among Chinese Americans are needed. Full article

Social media has increasingly relied on memes as a tool for expressing opinions, making meme sentiment analysis an emerging area of interest for researchers. While much of the research has focused on English-language memes, under-resourced languages, such as Bengali, have received limited attention. Given the surge in social media use, the need for sentiment analysis of memes in these languages has become critical. One of the primary challenges in this field is the lack of benchmark datasets, particularly in languages with fewer resources. To address this, we used the MemoSen dataset, designed for Bengali, which consists of 4368 memes annotated with three sentiment labels: positive, negative, and neutral. MemoSen is divided into training (70%), test (20%), and validation (10%) sets, with an imbalanced class distribution: 1349 memes in the positive class, 2728 in the negative class, and 291 in the neutral class. Our approach leverages advanced deep learning techniques for multimodal sentiment analysis in Bengali, introducing three hybrid approaches. SentimentTextFormer is a text-based, fine-tuned model that utilizes state-of-the-art transformer architectures to accurately extract sentiment-related insights from Bengali text, capturing nuanced linguistic features. SentimentImageFormer is an image-based model that employs cutting-edge transformer-based techniques for precise sentiment classification through visual data. Lastly, SentimentFormer is a hybrid model that seamlessly integrates both text and image modalities using fusion strategies. Early fusion combines textual and visual features at the input level, enabling the model to jointly learn from both modalities. Late fusion merges the outputs of separate text and image models, preserving their individual strengths for the final prediction. Intermediate fusion integrates textual and visual features at intermediate layers, refining their interactions during processing. These fusion strategies combine the strengths of both textual and visual data, enhancing sentiment analysis by exploiting complementary information from multiple sources. The performance of our models was evaluated using various accuracy metrics, with SentimentTextFormer achieving 73.31% accuracy and SentimentImageFormer attaining 64.72%. The hybrid model, SentimentFormer (SwiftFormer with mBERT), employing intermediate fusion, shows a notable improvement in accuracy, achieving 79.04%, outperforming SentimentTextFormer by 5.73% and SentimentImageFormer by 14.32%. Among the fusion strategies, SentimentFormer (SwiftFormer with mBERT) achieved the highest accuracy of 79.04%, highlighting the effectiveness of our fusion technique and the reliability of our multimodal framework in improving sentiment analysis accuracy across diverse modalities. Full article

Previously, it was suggested that the “persona-driven” approach can contribute to producing sufficiently diverse synthetic training data for Large Language Models (LLMs) that are currently about to run out of real natural language texts. In our paper, we explore whether personas evoked from LLMs through HCI-style descriptions could indeed imitate human-like differences in authorship. For this end, we ran an associative experiment with 50 human participants and four artificial personas evoked from the popular LLM-based services: GPT-4(o) and YandexGPT Pro. For each of the five stimuli words selected from university websites’ homepages, we asked both groups of subjects to come up with 10 short associations (in Russian). We then used cosine similarity and Mahalanobis distance to measure the distance between the association lists produced by different humans and personas. While the difference in the similarity was significant for different human associators and different gender and age groups, neither was the case for the different personas evoked from ChatGPT or YandexGPT. Our findings suggest that the LLM-based services so far fall short at imitating the associative thesauri of different authors. The outcome of our study might be of interest to computer linguists, as well as AI/ML scientists and prompt engineers. Full article

This study examines the impact of a culturally responsive, garden-based STEM program designed for Latina girls (grades 5–6) and their parents. The “Our Plot of Sunshine” project integrates Family Project-Based Learning with garden education to create meaningful STEM engagement opportunities. Drawing on the science capital, science identity, and community cultural wealth frameworks, the program leverages families’ cultural and linguistic resources while developing science knowledge and identity. Nineteen families from low socioeconomic schools participated in three pilot implementations across two Western U.S. cities. Using a mixed-methods approach with repeated measures over 19 weeks, the study tracked changes in participants’ science identity, interest, and career aspirations. Results showed significant increases in science identity and career aspirations, with effects maintained at three-month follow-up. While interest/enjoyment showed positive trends, changes were not statistically significant. Parent ratings of program elements were consistently higher than daughter ratings, though both groups reported strong engagement. The successful integration of bilingual instruction emerged as a particularly valued program component. These findings suggest that family-centered, culturally responsive garden education can effectively support Latina girls’ STEM identity development and future orientation, while highlighting the potential of leveraging family and cultural resources in STEM education. Full article

#BookTok, the TikTok sub-community for readers, has reshaped publishing and digital reading trends where marginalized readers find space to promote diverse books and stories beyond mainstream norms. This paper explores how three international #BookTokers with diverse cultural and linguistic backgrounds have found community, identity, and activism within this space, highlighting #BookTok’s role in fostering inclusive and affirming literary communities amidst rising censorship challenges. This case study used thematic analysis to analyze participant interviews through open and axial coding to explore #BookTok engagement, framed through affinity spaces, transformative potential, and critical digital pedagogies. #BookTok fosters belonging by connecting readers through niche interests, with the algorithm curating content aligned with identities. Participants reported shifts in reading behaviors and identities, with multilingual users expanding language repertoires to access and engage with diverse, identity-affirming texts. Content creation deepened connections, enabling advocacy for equity and justice. #BookTok is experienced as an affirming community where diverse texts and content creation can foster critical connections and promote justice-oriented actions beyond personal enjoyment of reading. Full article

As people’s environment awareness increases and the “double carbon” policy is implemented, the new energy vehicle (NEV) becomes a popular form of transformation and more and more car manufacturers start to produce NEVs. Thus, how to choose an appropriate type of NEVs from many brands is an interesting topic for customers, which can be regarded as a multiple-attribute decision-making (MADM) problem because customers often concern several different factors such as the price, endurance mileage, appearance and so on. This paper proposes a possible degree-based D–S evidence theory method for helping customers select a proper type of NEVs in the probabilistic linguistic environment. In order to derive decision information reflecting customer demands, online customer reviews (OCRs) are crawled from multiple websites and converted into five-granularity probabilistic linguistic term sets (PLTSs). Afterwards, by maximizing deviation and minimizing the information uncertainty, a bi-objective programming model is built to determine attribute weights. Furthermore, a possible degree-based D–S evidence theory method in the PLTS environment is proposed to rank alternatives in each website. For fusing these ranking results, a 0–1 programming model is set up by maximizing the consensus between the comprehensive ranking and individual ones in each website. At length, a case study of selecting a type of NEVs is provided to show the application and validity of the proposed method. Full article

Complaint processing is of great importance for companies because it allows them to understand customer satisfaction levels, which is crucial for business success. It allows them to show the real perceptions of users and thus visualize the problems, which are regularly processed from oral or written natural language, derived from the provision of a service. In addition, the treatment of complaints is relevant because according to the laws of each country, companies have the obligation to respond to these complaints in a specified time. The specialized literature mentions that enterprises lost USD 75 billion due to poor customer service, highlighting that companies need to know and understand customer perceptions, especially emotions, and product reviews to gain insight and learn about customer feedback because of the importance of the voice of the customer for an organization. In general, it is evident that there is a need for research related to computational language processing to handle user requests. The authors show great interest in computational techniques for the processing of this information in natural language and how this could contribute to the improvement of processes within the productive sector. This work searches in indexed journals for information related to computational methods for processing relevant data from user complaints. It is proposed to apply a systematic literature review (SLR) method combining literature review guides by Kitchenham and the PRISMA statement. The systematic process allows the extraction of consistent information, and after applying it, 27 articles were obtained from which the analysis was conducted. The results show various proposals using linguistic, statistical, machine learning, and hybrid methods. We find that most authors combine Natural Language Processing (NLP) and Machine Learning (ML) to create hybrid methods. The methods extract relevant information from complaints of the customers in natural language in various domains, such as government, medical, banks, e-commerce, public services, agriculture, customer service, environmental, and tourism, among others. This work contributes as support for the creation of new systems that can give companies a significant competitive advantage due to their ability to reduce the response time of the complaints as established by law. Full article