Electrical energy theft presents a serious and significant challenge for utility companies worldwide. It poses substantial risks to energy infrastructure, reduces efficiency, and destabilizes distribution networks. The introduction of the Advanced Metering Infrastructure (AMI) in the last decade has significantly boosted the development of new methods and techniques for detecting electrical energy theft. This advancement is primarily due to the availability of electric consumption and other data with greater granularity (e.g., every 15 min), which has enabled the development of analytic and data-driven approaches as opposed to mass inspections alone. In this context, this paper aims to analyze the landscape of energy theft detection by providing a systematic analysis of the state-of-the-art manuscripts in the form of a tertiary study (i.e., a review of literature reviews and surveys) in accordance with the PRISMA methodology guidelines. Consequently, a comparative framework is presented, along with well-formulated research questions designed to explore the past, present, and future directions of energy theft detection.
Diego Labate, Dipanwita Thakur, Antonella Guzzo et al.· Big Data and Cognitive Compu...· 0 citations
The integration of generative artificial intelligence (AI) chatbots into English as a Foreign Language (EFL) instruction has generated increasing interest, particularly in relation to vocabulary learning and learner autonomy. However, empirical evidence on their impact on productive vocabulary development remains limited, especially in the EFL context in the Middle East. This study investigated the impact of a generative AI chatbot on Saudi EFL students' productive vocabulary development and learner autonomy. Employing a quasi-experimental one-group pretest–posttest design, data were collected from 30 undergraduate EFL learners over a four-week instructional intervention. Participants completed a productive vocabulary test before and after the intervention, and a learner autonomy questionnaire at the end of the study. Paired-samples t-test results revealed statistically significant improvement in the students’ productive vocabulary performance following the chatbot-supported intervention. Descriptive analysis of the questionnaire responses indicated generally positive perceptions of learner autonomy, particularly in goal setting, strategy use, self-monitoring, and responsibility for learning. The findings suggest that generative AI chatbots can serve as effective supplementary tools for enhancing productive vocabulary learning and fostering learner autonomy in EFL contexts. The study offers several pedagogical implications and directions for future research.
Abdulhameed A. Alhuwaydi· Journal of Language Teaching...· 0 citations
This study examined the effects of generative artificial intelligence as instructional scaffolding on secondary school students’ critical thinking and classroom engagement. A quantitative quasi-experimental method with a nonequivalent pretest–posttest control group design was applied to 64 eighth-grade students at SMP Negeri 1 Kota Bima, comprising 32 students in the experimental group and 32 students in the control group. The experimental group participated in eight generative artificial intelligence-supported learning sessions involving problem identification, information exploration, response verification, collaborative discussion, problem-solving, and reflection, while the control group received conventional instruction. Data were collected through a 20-item critical thinking test, structured classroom observations, and documentation. The data were analyzed using descriptive statistics, normality and homogeneity tests, normalized gain analysis, independent-samples testing, and effect-size analysis. The experimental group achieved a higher posttest mean score than the control group, with scores of 93.75 and 78.30, respectively. The normalized gain was 0.57 in the experimental group and 0.22 in the control group. The differences in posttest and normalized-gain scores were statistically significant, with a large effect size of 0.87. Classroom engagement also reached 92.11 percent in the experimental group compared with 71.80 percent in the control group. These findings demonstrate that generative artificial intelligence effectively supports critical thinking and classroom engagement when used as guided instructional scaffolding rather than as a provider of final answers.
Herman Herman, Muh. Nasir, Anggar Putra et al.· Journal La Edusci· 0 citations
tdia sa zameriava na vnmanie umelej inteligencie v kolskom prostred z pohadu pedagogickch a odbornch zamestnancov. Cieom vskumu bolo analyzova, ako respondenti hodnotia iacke pouvanie AI, ak etick rizik s tm spjaj a ak podporu potrebuj pri jeho didaktickom usmerovan. Vskum mal exploratvno-deskriptvny charakter a bol realizovan prostrednctvom anonymnho online dotaznka na vzorke 150 zamestnancov slovenskch kl. Vsledky naznauj, e iacke pouvanie AI je v kolch ben, no jeho formlne ukotvenie zostva nerovnomern. tdia preto zdrazuje potrebu jasnch kolskch pravidiel a praktickho profesijnho rozvoja uiteov.
Mária Bajúzová, Michal MANČÍK· Journal of Technology and In...· 0 citations
Reach audiences
Advertise in front of researchers, engineers, and readers.
Generative artificial intelligence (GenAI) is reshaping language education through its capacity to generate linguistic and multimodal learning materials. In low-resource contexts such as Vietnamese as a Foreign Language (VFL), however, AI outputs may produce culturally generalized or inaccurate representations because culturally grounded training data remain uneven. This mixed-methods questionnaire study examines how 54 VFL teachers perceive the pedagogical usefulness, applicability, and ethical implications of GenAI in experiential language learning. Building on the limitations of TPACK, the study proposes the Technological–Pedagogical–AI Ethical Knowledge (TPAEK) framework as a context-sensitive analytical lens that treats ethical knowledge as a mediating dimension of pedagogical decision-making. Quantitative findings indicate positive perceptions of GenAI’s pedagogical value and applicability, whereas ethical awareness emerged as a distinct but not directly predictive dimension in this exploratory model. Open-ended responses identified recurrent teacher-reported forms of AI-induced cultural distortion, particularly cross-cultural blending, visual inaccuracy, cultural misinformation, and symbolic stereotyping. Overall, the study suggests that teachers act as interpretive mediators who evaluate, contextualize, and regulate AI-generated content to preserve cultural validity in experiential language learning.
Lương Thị Hiền, Nguyen Thi Hong Ngan, Nguyen Duc Long· Journal of Language Teaching...· 0 citations
Agent-driven instructional models are spreading rapidly in language education, yet the question of for whom such models work—and for whom they carry risks—has received little attention. Using the cognitive offloading-human-AI co-creation continuum as an analytic lens, this study compared two contrasting learner populations under the same agent-driven Business English project-based learning (PBL) model: first-year undergraduates (n = 30) and MBA students (n = 30). The two cohorts were taught by the same instructor with the same textbook and identical agent roles and interaction rules, and completed parallel versions of the same end-of-semester questionnaire (with minor wording adaptations for undergraduates), supplemented by open-ended responses. Analyses combined between-group comparisons, a seven-dimension profile comparison, an exploratory typological cluster analysis of offloading and co-creation, and hierarchical regression. Results showed that the two populations were strikingly similar in offloading containment and across all outcome dimensions: no between-group difference survived Holm correction (|d| <= 0.52), and the only medium-sized item-level difference was confidence in detecting errors in agent output (higher among MBA students, d = 0.67). The typological analysis indicated that the sample divided into a “deeply engaged” type (56.7%) and a “prudent self-reliant” type (43.3%) that cut across both cohorts, with high co-creation and elevated dependence signals co-occurring and the type distribution independent of cohort (p = .193). After controlling for gender, prior generative-AI experience, and use frequency, the incremental explanatory power of cohort approached zero (dR-squared <= .021). The findings suggest that under one and the same rule-governed design, “level is not destiny”: learner level should remain an explicit consideration in the design and scaling of agent-based pedagogy, but the unit of differentiation should descend to learner types, and differentiated design guidelines are derived accordingly.
Since the public release of ChatGPT in late 2022, higher education institutions have experienced an increase in academic misconduct allegations related to suspected generative AI (GenAI) misuse. A necessary part of these processes is the inclusion of evidence to support and strengthen allegations. To date, no systematic framework exists for understanding trends and patterns in GenAI student academic misconduct and classifying the types and probative value of evidence presented in these allegations. This study addresses that gap through a mixed-methods analysis of 1,162 GenAI-related misconduct case records spanning January 2023 to December 2025 at one regional Australian university. Analysis confirmed an increase in case volumes over the study period and produced an empirically derived 15-code evidence taxonomy, which was applied across the 1,855 evidence items against three probative quality credentials drawn from legal evidence scholarship: relevance, credibility, and inferential force. Results point to patterns across time that differ across evidence types, including an increasing use of fabricated references and student admissions of AI use, and a decrease in allegations being raised without support. These findings highlight a critical gap in current misconduct policies: institutions lack explicit criteria for determining what constitutes reliable evidence in GenAI misconduct cases. To address this, the study offers three contributions: first, an empirically derived taxonomy that categorises the types of evidence used in GenAI misconduct proceedings. Second, a structured framework for assessing the quality and reliability of that evidence, designed for direct application in institutional decision-making. Third, the first empirical analysis at scale of how evidence is currently gathered and evaluated in GenAI misconduct cases.
Albert Munoz, Mercedez Hinchcliff, Cameron Langfield et al.· International Journal for Ed...· 0 citations
Somewhere between overhead projectors and generative AI, I’ve spent 18 years at Harding helping students learn how to think—now I’m figuring out how to do that with tools that can think with them. As a mid-career professor (experienced enough to know better, curious enough to keep trying), this presentation will discuss my efforts to re-design an undergraduate course assignment to be AI-friendly, teaching students to leverage this technology to support their learning. With skepticism and optimism, I'll share the nuts and bolts of my flipped assignment, what my students thought about it, and what I'll do different next time. If you're looking for an expert in AI, this session is not for you. If you're also an AI-beginner trying to figure out how to remain relevant and keep teaching students how to think for themselves, come on in and we'll talk together.
Melanie Meeker· Scholar Works at Harding (Ha...· 0 citations
Artificial intelligence has become increasingly central to translation and interpreting education, shaping classroom practice, feedback, assessment, and professional preparation. Yet the literature remains fragmented across work on machine translation, computer-assisted translation, post-editing, automatic speech recognition, generative AI, large language models, and AI-supported assessment. This bibliometric review examines how research in this area has developed from 2014 to May 2026, with particular attention to the movement from tool-oriented technology training toward AI-mediated competence. Bibliographic records were retrieved from Web of Science and Scopus and analysed using VOSviewer and CiteSpace. The analysis focuses on publication trends, collaboration patterns, keyword co-occurrence, thematic clusters, and keyword bursts. The results show limited output before 2018, steady growth between 2019 and 2021, and rapid expansion after 2022. The keyword evidence points to a shift from machine translation, post-editing, and translation technology training toward AI literacy, evaluative judgement, output verification, feedback practices, ethical responsibility, professional agency, and human-AI collaboration. Interpreting-related research is still less developed than translation-oriented research. The findings suggest that AI integration in translation and interpreting education is not simply a matter of adopting new tools, but part of a broader process of competence development.
Qing Zheng, Mansour Amini· Journal of Language Teaching...· 0 citations
Abstract Large language models (LLMs) are generative artificial intelligence (AI) models that are rapidly reshaping the practice of biomedical science. Their ability to synthesize literature, generate analytical code, and interface with multimodal data offers a new framework for accelerating discovery. Yet their integration into scientific workflows remains irregular, and the field lacks clear guidance for reliable and productive use. We review emerging evidence on researcher adoption, highlight common failure modes such as so-called hallucinations (i.e. confabulations) and overgeneralization, and provide practical recommendations for domain-informed use of LLMs in basic biomedical research. We structure this review around four domains in which LLMs increasingly augment biomedical science: administrative tasks, literature search and synthesis, data analysis, and scientific writing. For each domain we provide practical guidance, illustrative use cases, and examples of free or low-cost tools that researchers can readily adopt. Finally, we discuss the organizational and cultural changes for biomedical science to leverage LLMs responsibly, including transparent reporting, human-in-the-loop validation, and alignment with scientific rigor and reproducibility standards. Together, these recommendations provide a path for integrating LLMs into biomedical research in ways that enhance, rather than replace, human expertise and accelerate the path from biological insights to beneficial human impact.
Kaleigh F. Roberts, Srinivas Koutarapu, Justin Melendez et al.· npj Dementia· 0 citations
Large language models (LLMs) are increasingly relevant to Business Process Management (BPM), particularly when process knowledge is dispersed across documents, conversations, and other unstructured sources. Their probabilistic outputs, however, raise questions about validation, traceability, and accountability. This paper develops a lifecycle-based conceptual framework for allocating and governing LLM use across the six stages of the BPM lifecycle. The framework separates generative interpretation from formal, empirical, and expert validation. It comprises five interdependent layers and six operational principles, implemented through a stage-risk-validation matrix, a principle-intensity map, and four evaluation dimensions. Governance requirements increase as outputs approach live execution or decisions that are difficult to reverse, with controls aligned with the NIST AI Risk Management Framework, the EU AI Act, and the GDPR. A customer complaint-handling scenario demonstrates how the framework can be applied. An illustrative stress test using the BPI Challenge 2017 event log and ten independent LLM generations instantiates the validation layer under information-asymmetric conditions. Although all generated models were structurally valid, the event log revealed incomplete activity coverage and control-flow mismatch. This illustrates the value of an external referent but does not establish comparative performance or a general difference in error detectability between LLM-generated and process-mining artefacts. The framework therefore positions LLMs as tools for turning unstructured information into preliminary process knowledge, while established BPM methods and human expertise remain responsible for validating consequential outputs.
A new method, called CW-Net, translates the reasoning process of an autonomous vehicle’s AI system into understandable concepts that explain its behavior.
