Digital educational environments are expanding toward complex AI and human discourse, providing researchers with an abundance of data that offers deep insights into learning and instructional processes. However, traditional qualitative analysis remains a labor-intensive bottleneck, severely limiting the scale at which this research can be conducted. We present Sandpiper, a mixed-initiative system designed to serve as a bridge between high-volume conversational data and human qualitative expertise. By tightly coupling interactive researcher dashboards with agentic Large Language Model (LLM) engines, the platform enables scalable analysis without sacrificing methodological rigor. Sandpiper addresses critical barriers to AI adoption in education by implementing context-aware, automated de-identification workflows supported by secure, university-housed infrastructure to ensure data privacy. Furthermore, the system employs schema-constrained orchestration to eliminate LLM hallucinations and enforces strict adherence to qualitative codebooks. An integrated evaluations engine allows for the continuous benchmarking of AI performance against human labels, fostering an iterative approach to model refinement and validation. We propose a user study to evaluate the system's efficacy in improving research efficiency, inter-rater reliability, and researcher trust in AI-assisted qualitative workflows.
The emergence of large language models (LLMs) is reshaping how people engage in political discourse online. We examine how the release of ChatGPT altered ideological and emotional patterns in the largest political forum on Reddit. Analysis of millions of comments shows that ChatGPT intensified ideological polarization: liberals became more liberal, and conservatives more conservative. This shift does not stem from the creation of more persuasive or ideologically extreme original content using ChatGPT. Instead, it originates from the tendency of ChatGPT-generated comments to echo and reinforce the viewpoint of original posts, a pattern consistent with algorithmic sycophancy. Yet, despite growing ideological divides, affective polarization, measured by hostility and toxicity, declined. These findings reveal that LLMs can simultaneously deepen ideological separation and foster more civil exchanges, challenging the long-standing assumption that extremity and incivility necessarily move together.
This study critically examines the BBC's COVID-19 discourse on the social media platform X (previously Twitter) to explore how media language shaped public attitudes and health behaviors during the pandemic. The research investigates the BBC tweets posted between January 2020 and May 2023 using a Critical Discourse Analysis (CDA) framework. By analyzing the inclusive pronoun, metaphor, and modality, the study reveals how the BBC crafted messages of collective responsibility, urgency, and empathy to promote public compliance with health guidelines. The methodology follows Fairclough's three-dimensional model which includes textual analysis of linguistic features, discursive practice of intertextuality and framing, and social practice analysis of societal contexts. Tweets were purposively sampled based on engagement metrics and relevance, enabling a comprehensive analysis of the evolving discourse throughout the pandemic's progression. This comprehensive approach highlights how BBC discourse aligned with authoritative sources (including government policies and scientific findings) to reinforce credibility and influence public understanding. Key findings demonstrate the BBC's role in framing health behaviors through emotive storytelling which humanized the pandemic's impact and fostered empathy. Repetition of key slogans strengthened public commitment to collective responsibility and compliance with health measures. The study also uncovers underlying power dynamics and social inequalities reflected in the discourse, emphasizing the media's influence in shaping public responses during the crises. This research contributes to understanding the intersection of language, power, and ideology in pandemic reporting. It underscores the need for ethical media practices and enhanced public media literacy to navigate health emergencies effectively.
Corrosion in infrastructure creates high-risk scenarios, and mitigation strategies are expensive, with significant annual costs globally. This paper advances the discourse of corrosion monitoring and tracking in infrastructure, emphasizing the importance of data analytics, AI, and Digital Twins (DT) for managing the infrastructure lifecycle while reducing risk and costs associated with corrosion. The non-parametric analysis of corrosion data is demonstrated to provide insights into spatial and temporal variations, helping in predictive modeling and decision-making. Strategic sampling and analysis of corrosion data help in making evidence-based maintenance decisions, reducing costs, and improving safety. AI analytics enhances the functionality of corrosion databases and Digital Twins, enabling predictive analytics and real-time simulations for better decision-making. Recommendations are provided for the implementation of AI in engineering applications, including data quantity and training resources, but offer significant potential for improved corrosion management.
Dilfuza M.Makhmudova, Xilola R. Sharipova, Nosirjon K. Hojiyev
et al.
This study aims to examine the ethical and legal challenges associated with the integration of Artificial Intelligence in Education (AIEd), focusing on science and mathematics teachers across Central Asia, particularly in Kazakhstan, Kyrgyzstan, and Tajikistan. A mixed-methods approach was employed. Quantitative data were collected through a structured survey from N = 341 educators, stratified by country, gender, age, and AI usage experience. The survey assessed perceptions of legal and ethical issues using a five-point Likert scale. Statistical analysis included ANOVA, Pearson correlation, and effect size calculations (Cohen’s d). Findings revealed statistically significant differences in ethical awareness levels across countries (p < .01), with Kazakhstan showing the highest average score (M = 4.12, SD = 0.67) on AI-related ethical literacy. The effect size was moderate (Cohen’s d = 0.54) when comparing gender-based ethical concerns. Additionally, 64% of respondents expressed serious concerns about student data privacy, while 71% supported the need for formal AI ethics training. Qualitative interviews (N = 18) uncovered recurring themes such as lack of legal frameworks, teacher autonomy dilemmas, and algorithmic bias in grading systems. The study highlights a critical need for policy interventions and professional development targeting ethical and legal dimensions of AIEd in post-Soviet education systems. Findings underscore the urgency of developing culturally responsive guidelines to safeguard equity, transparency, and trust in AI-driven pedagogical environments. These results contribute to the global discourse on AI in education and offer evidence-based insights for local policymakers.
The article explores the literary-philosophical discourse of Iris Murdoch’s texts with regard to its narrative specificity and genre modification. The genre specifics of her novels are examined in the historical and cultural retrospective, with the emphasis on their discourse markers. Previous research indicates that this aspect of her philosophical novel poetics remains insufficiently studied. The paper aims to examine the discourse of Murdoch’s prose within theoretical-historical context, considering its main markers as reflection and dialogism. The discourse analysis basically employs a qualitative methodology that includes the integrative approach comprising the fundamentals of historical poetics, genre study, narratology, receptive poetics and transitivity theory. The findings support the idea that the analysis of dialogues (conversations) in close rereading of Murdoch’s texts contributes to the insight of her philosophical prose dynamic nature due to the specific patterns of complex interplay of voices in her writings (novels, philosophical dialogues). Consequently, we find that the phenomenon of universal dialogism in Murdoch’s prose appears to be qualitatively diverse: ‘dialogic protagonist’, plot dialogism, dialogues with the reader, reflections of characters, discussions on various issues of philosophy, religion, being, morality, etc. Overall, reception links of Murdoch’s philosophical essays and her novels are traced and analysed within the broad context of European literary-philosophical reflection.
We introduce an approach to topic modelling with document-level covariates that remains tractable in the face of large text corpora. This is achieved by de-emphasizing the role of parameter estimation in an underlying probabilistic model, assuming instead that the data come from a fixed but unknown distribution whose statistical functionals are of interest. We propose combining a convex formulation of non-negative matrix factorization with standard regression techniques as a fast-to-compute and useful estimate of such a functional. Uncertainty quantification can then be achieved by reposing non-parametric resampling methods on top of this scheme. This is in contrast to popular topic modelling paradigms, which posit a complex and often hard-to-fit generative model of the data. We argue that the simple, non-parametric approach advocated here is faster, more interpretable, and enjoys better inferential justification than said generative models. Finally, our methods are demonstrated with an application analysing covariate effects on discourse of flavours attributed to Canadian beers.
This study investigates how U.S. news media framed the use of ChatGPT in higher education from November 2022 to October 2024. Employing Framing Theory and combining temporal and sentiment analysis of 198 news articles, we trace the evolving narratives surrounding generative AI. We found that the media discourse largely centered on institutional responses; policy changes and teaching practices showed the most consistent presence and positive sentiment over time. Conversely, coverage of topics such as human-centered learning, the job market, and skill development appeared more sporadically, with initially uncertain portrayals gradually shifting toward cautious optimism. Importantly, media sentiment toward ChatGPT's role in college admissions remained predominantly negative. Our findings suggest that media narratives prioritize institutional responses to generative AI over long-term, broader ethical, social, and labor-related implications, shaping an emerging sociotechnical imaginary that frames generative AI in education primarily through the lens of adaptation and innovation.
Valerio La Gatta, Marco Postiglione, Jeremy Gilbert
et al.
Numerous social movements (SMs) around the world help support the UN's Sustainable Development Goals (SDGs). Understanding how key events shape SMs is key to the achievement of the SDGs. We have developed SMART (Social Media Analysis & Reasoning Tool) to track social movements related to the SDGs. SMART was designed by a multidisciplinary team of AI researchers, journalists, communications scholars and legal experts. This paper describes SMART's transformer-based multivariate time series Discourse Evolution Engine for Predictions about Social Movements (DEEP) to predict the volume of future articles/posts and the emotions expressed. DEEP outputs probabilistic forecasts with uncertainty estimates, providing critical support for editorial planning and strategic decision-making. We evaluate DEEP with a case study of the #MeToo movement by creating a novel longitudinal dataset (433K Reddit posts and 121K news articles) from September 2024 to June 2025 that will be publicly released for research purposes upon publication of this paper.
The literature on ethics and sustainability in mathematics and its education is increasingly complex and fragmented, potentially leading to communication breakdowns between different scholarly traditions. To address this, the paper introduces the "Ethical and Sustainable Concerns Triangle," a framework that maps discourses based on their relative concern for three areas (represented as three vertices in the triangle): "Mathematics", "Community", and "Society/Planet". By integrating a systems theoretic perspective, we analyse discourses as dynamic systemic reactions to external irritations. Our analysis reveals that the field's fragmentation can be explained by the "location effect": the phenomenon whereby a discourse's position within the triangle shapes its perception and acceptance of other scholarship. By mapping key discourses and educator archetypes, the framework functions as a meta-heuristic tool. Ultimately, it serves not only to facilitate critical reflection but also as a call for the epistemic humility and dialogue needed to advance the field.
Underground infrastructure such as pipelines and tunnels can be vulnerable to transient ground deformation (TGD) generated by earthquakes, traffic, and other vibration sources. Current design methods rely on simplified analytical models that idealize soil movement as a traveling sinusoidal wave, neglecting system inertia and relative soil-structure displacement. As shown in this study, such assumptions may be inadequate for large-diameter buried pipelines and tunnels, where accurate dynamic analysis under axial and transverse TGD is required. This paper introduces a new semi-analytical model for the dynamic response of buried Timoshenko beams on Winkler foundation subjected to transverse TGD. A closed-form solution of the governing differential equation shows that the vibration spectrum is divided into four parts, separated by three transition frequencies that depend on the system's mechanical and geometric properties. These transitions govern changes in modal behavior and significantly influence dynamic amplification. The model is verified through a case study of a buried 107 cm (42 in) steel water pipeline with varying lengths and operating conditions. Analytical predictions show excellent agreement with finite-element modal and dynamic analyses. Additional validation using high-resolution free-vibration measurements and multi-point shaking-table experiments further confirms the accuracy and robustness of the formulation. Frequency-response analysis highlights conditions under which dynamic amplification becomes significant, particularly when forcing frequencies approach the system's fundamental frequency, which may lie within the range of dominant seismic frequencies. The proposed methodology provides a rigorous analytical framework for understanding the key factors governing the dynamic behavior of buried beams under diverse sources of ground vibration.
This research investigates the integration of Urban Environmental Quality, Materials and Resource Management, and Green and Sustainable Environment within the international maritime education framework at Sekolah Tinggi Ilmu Pelayaran (STIP Jakarta/ Maritime Institute of Jakarta). The study explores cadet experiences in Nautical, Technical, and Port and Shipping Management Majors, analysing theoretical foundations, curriculum satisfaction, practical training, and the incorporation of Environmental Science elements. Utilizing a qualitative descriptive approach, data is gathered through document analysis and self-reported reflections from 100 randomly selected cadets. The findings reveal distinct dynamics across majors, indicating a strong alignment between theoretical foundations and curriculum satisfaction. However, variations exist in the emphasis on practical training and the integration of Environmental Science elements. Port and Shipping Management Majors stand out with a notable commitment to sustainability principles, while Nautical and Technical Majors show potential areas for enhancement. The implications for future research underscore the need for ongoing refinements in curricula to address the evolving demands of the maritime industry. The study contributes to the scholarly discourse on international maritime education, providing insights for academic institutions to foster environmentally conscious and globally competent maritime professionals.
Abstract The global pandemic has dramatically reshaped public discourse, with social media emerging as a pivotal platform for these discussions. This study delves into evolving sentiments, emotions, and prevalent topics in online discussions spanning the years 2020, 2021, and 2022, drawing from a dataset of 2.65 million tweets from the Twitter Platform. A multifaceted approach that combines quantitative and qualitative methods are developed for dissecting evolving discourses, with a particular focus on the lens of nationalism. The quantitative part disassembling sentiment & emotion, topics, and Co-Occurrence Network yields a nuanced understanding of the textual content. A qualitative theoretical analysis named Evolving Discourse Framework Analysis are designed to unravel the textual discourse. Study results in the development of an adaptable framework. Findings expose nationalist orientations and the framing elements present within online public discourse, which are categorized into three distinct frames: ‘feeling,’ ‘identity,’ and ‘action.’ Importantly, the ‘feeling’ frame interconnects with the ‘identity’ frame, ultimately shaping responses within the ‘action’ frame. The frames shine a light on a complex and interconnected web of nationalist narratives that exist within the online sphere, subtly influencing public opinion and behavior. This study serves as a reminder that beneath the surface of seemingly unified global discourse, there exists a segmented world, ripe for further exploration and understanding.
History of scholarship and learning. The humanities, Social Sciences
Research Problem: Women’s activism through social media in South Asia, particularly in Sri Lanka and Afghanistan, is shaped by a complex interplay of religion, public policy, and socio-political contexts. While social media offers opportunities for empowerment and advocacy, its role remains contested due to societal barriers, political constraints, and religious influences.
Research Purposes: This study aims to explore the dynamics of women’s online activism in Sri Lanka and Afghanistan, examining how socio-economic, political, and religious factors influence their digital engagement. It seeks to identify key challenges and highlight the comparative impact of social media on women’s advocacy in these two countries.
Research Methods: The study employs a qualitative approach, using content analysis of secondary data, including reports, academic studies, and media articles. Comparative analysis is applied to evaluate the socio-political and religious factors shaping women’s digital activism in Sri Lanka and Afghanistan.
Results and Discussion: In Sri Lanka, women benefit from higher literacy rates and a relatively open societal structure, enabling them to use social media effectively for addressing issues like corruption and economic mismanagement. However, ethnic and religious divisions limit cohesive activism. In Afghanistan, women demonstrate resilience by leveraging platforms like Facebook and WhatsApp despite facing severe socio-religious constraints and the Taliban’s repressive policies. The analysis underscores the critical role of social media as both a tool for advocacy and a contested space for control and suppression.
Research Implications and Contributions: The study highlights the necessity of addressing structural barriers to enhance women’s digital participation. Regional collaboration, improved digital literacy, and international advocacy are recommended to amplify women’s voices in both countries. The findings contribute to the broader discourse on the intersection of religion, public policy, and digital activism, providing insights for policymakers and activists working toward inclusive social progress.
Inscribed within the theoretical framework of socio-political discourse analysis, this study takes as its subject a verbal sequence, Mali kura, which arose in the context of popular protest against the regime of Ibrahim Boubacar Kéita in 2020 and has become a common denominator of the discourses produced during the political transition underway in Mali. Using discursive data collected from the press and from the speeches of political actors from 2020 to 2024, we examine the formality of the sequence Mali kura. Linguistic description of the sequence’s two signifying forms (Mali kura, in bamanankan, and le Mali nouveau, in French) enabled us to establish their figment. Analysis of the sequence’s discursive functioning revealed its character as a social referent, through the notions of circulation, paraphrase, reformulation and lexicological productivity. As a social referent, it appeared that the sequence was given rhetorical and polemical modes of use. The study therefore concludes that the verbal sequence Mali kura/le Mali nouveau is a formula in Malian socio-political discourse during the period under consideration.