The Effects of Generative Artificial Intelligence on Job Task: Evidence from Job Posts

Generative artificial intelligence (GenAI) has received increasing attention from language researchers and educators due to its capability to influence student language performance. Very limited previous meta-analysis studies have examined instructional factors in their analysis, while it deserves great attention. Task-based language teaching (TBLT) is a widely applied language instructional approach. Hence, this systematic review and meta-analysis study adopts TBLT to investigate how these instructional factors (e.g., task-based variables, learner variables) impact student language learning in GenAI contexts. Using the PRISMA guidelines, we collected literature and included 25 empirical studies involving 2,431 participants. The study confirmed the positive influence of GenAI on language learning and identified three moderators—student AI literacy, task planning, and task assessors—that potentially moderate its effectiveness. This finding likely relates to the GenAI tools employed; specifically, ChatGPT—not primarily designed for language learning—was used most frequently. This general-purpose GenAI may require students to understand how AI works, how to plan their tasks, and the necessity of involving teachers as assessors. We suggest teachers should get more involved in language learning with GenAI, and more language learning-specific GenAI should be developed. These suggestions help institutions and teachers to plan their instructions in the AI era.

<sec><title>Objective</title> As urban design faces increasing demands for contextual responsiveness, iterative optimization, and data-informed reasoning, integrating artificial intelligence into the design process has gained renewed relevance. Among emerging technologies, generative artificial intelligence (GAI) shows strong potential for automating content creation and simulating spatial configurations. This research provides a comprehensive review of recent developments in the application of GAI to urban design. The research identifies representative technical pathways, their respective intervention stages, and the functional mechanisms by which generative models are reshaping the design workflow. This research presents a structured, theory-informed synthesis of how different generative models contribute to tasks such as intention modeling, spatial reasoning, and performance-driven design. Building on design thinking and a descriptive lens informed by the technology acceptance model (TAM), the research examines how model type, data modality, and task characteristics affect GAI’s functional role, usability, and acceptance. Particular attention is given to mapping deployment forms, from isolated tools to coordinated multi-model workflows, and to characterizing cross-cutting challenges of controllability, transparency, and contextual adaptability in urban design settings. </sec><sec><title>Methods</title> Following the PRISMA protocol, the research conducts a multi-stage literature review combining automated search and expert screening. A total of 125 peer-reviewed articles and high-impact preprints are selected from Web of Science, CNKI, arXiv, and selected industry sources, covering the period from 2014 to July 2025. Search terms such as “generative AI”, “AIGC”, “GAN”, “diffusion model”, “variational autoencoder”, “autoregressive model”, “large language model”, and urban-related keywords are used in various combinations. Based on the collected literature, four types of generative models are summarized as image-driven, language-driven, structure-driven, and feedback-optimized models, according to their application characteristics in urban design tasks. These types are aligned with four stages of the design process: preliminary analysis, scheme generation, evaluation and decision-making, and outcome expression. On this basis, a two-dimensional framework to examine how different GAI pathways intervene across tasks is formed. To refine the mapping, each design stage is further broken down into three representative sub-tasks. Preliminary analysis includes public demand analysis, urban data enhancement, case/task framing, and spatial element recognition. Scheme generation covers design intention modeling, spatial layout generation, and 3D form construction. The evaluation and decision-making stage includes multi-objective optimization, scheme evaluation, and scenario prediction. The final expression stage involves textual documentation, 2D representation, and visual rendering. A quantitative analysis is also conducted to show the distribution of model types over design stages, identify common combinations, and trace the evolution of research focus over time. TAM informs a descriptive synthesis of perceived usefulness (PU) and perceived ease of use (PEU) across model types to illuminate adoption patterns. </sec><sec><title>Results</title> The findings reveal that GAI models are increasingly integrated into urban design workflows but exhibit uneven adoption across task types and modalities. Image-driven models dominate in both early-stage analysis and final visual representation due to their high interpretability, usability, and compatibility with existing design practices. Language-driven models are commonly used in public demand analysis, participatory planning, and scenario scripting, enabled by the rise of large language models (LLMs) such as ChatGPT and DeepSeek. Structure-driven models, though less prevalent, show promise in generating street networks, land-parcel layouts, and spatial typologies using graph-based logic. Feedback-optimized models, which rely on reinforcement learning, evolutionary algorithms, and performance simulation are the least adopted, but demonstrate strong potential in multi-objective optimization and iterative decision-making. Recent research indicates an increasing use of multi-model workflows, such as text-to-image pipelines integrated with urban simulation or feedback loops. While GAI applications increasingly support design iteration, their adoption is heavily influenced by the controllability, explainability, and contextual adaptability of models. PU and PEU vary significantly by model type, with image-driven models rated highest and structure-driven and feedback-optimized models facing usability challenges due to complexity and low transparency. </sec><sec><title>Conclusion</title> Although GAI has demonstrated broad applicability across the urban design process, current implementations are largely procedural and auxiliary in nature. Most models recombine existing inputs rather than construct original logic, and few possess autonomous reasoning or normative awareness. This limits their role to content augmentation rather than conceptual guidance in design development. Moreover, issues such as opaque decision logic, lack of domain-specific knowledge embedding, and poor adaptability to local planning norms hinder practical adoption. Addressing these challenges requires multi-level efforts: 1) Construct structured, regionally grounded urban design datasets; 2) improve model interpretability, controllability, and responsiveness to professional input; and 3) develop modular, multi-model systems that support seamless interaction across design stages. Human – AI collaboration mechanisms — especially those based on iterative prompts and semantic feedback-must be enhanced to enable AI not just as a tool, but as an active design partner. This review offers a comprehensive reference for scholars and practitioners seeking to understand how GAI is reshaping the logic, structure, and agency of urban design. </sec>

IntroductionThis study investigates faculty perceptions of academic integrity policies addressing traditional and Generative Artificial Intelligence (GenAI) plagiarism at an internationalized higher education institution in the UAE.MethodologyEmploying a mixed-methods approach, quantitative data were collected from 71 faculty members representing 37 nations via an online survey, supplemented by qualitative insights from 17 semi-structured interviews. The research assessed perceptions across five dimensions—availability, visibility, clarity, adequacy, and effectiveness—based on Bretag et al.’s (2011) framework.ResultsResults revealed significant disparities between traditional and GenAI policies, with both rated only moderately effective. Faculty demonstrated a preference for educative over punitive approaches while identifying workload constraints, insufficient institutional support, detection challenges, cultural leniency, and systemic limitations as barriers to policy enforcement. A hierarchical perception of GenAI plagiarism emerged, with direct content copying considered most serious and AI-assisted practices involving minimal student contribution viewed less severely. Lower response rates to Gen-AI scenarios reflected faculty uncertainty amid insufficient guidelines.DiscussionThe findings underscore the necessity for agile, comprehensive policies that address technological advancements while emphasizing faculty engagement and contextual support. This research contributes novel insights into underexplored scenarios including language assistance, translation, peer idea sharing, and citation errors, illuminating the evolving landscape of academic integrity in digital and collaborative environments. The study advocates for balanced frameworks integrating educative strategies, technological tools, and cultural sensitivities to maintain academic integrity standards in the AI era.

The advent of Generative Artificial Intelligence (GenAI) has had a profound impact on higher education, offering numerous benefits, including the enhancement of teaching quality and efficiency, the promotion of educational equity, and the facilitation of personalised learning. Nevertheless, this technology is not without its challenges, including concerns about data privacy and security, over-reliance on GenAI, content accuracy, and potential impacts on employment. It is incumbent upon governments and higher education institutions to adopt a proactive stance by continually developing and refining policy frameworks, enhancing education systems, and cultivating students' critical thinking skills. Adopting this approach will optimise the potential benefits of GenAI while mitigating the potential risks, thus fostering excellence in higher education in the AI era.

<title>Abstract</title> As generative artificial intelligence (GenAI) becomes widespread in education, its influence on students’ academic practice raises concern. We conducted pre-registered analyses of 4,820 empirical reports written by 2,000 psychology undergraduates (2016-2025). Following ChatGPT’s 2022 release, prevalence of ChatGPT-associated lexical markers (e.g., delve) surged until 2024, then declined in 2025, possibly due to students masking GenAI traces. Writing style (indexed by lexical diversity/density/sophistication, nominalisation, readability) became increasingly formal post-ChatGPT, diverging from predicted/historical trends. Consistent with GenAI’s positivity bias, sentiment also became more positive, independent of topic or result’s statistical significance, raising questions about its potential impact on students’ critical engagement. Despite the stylistic changes, writing quality (indexed by grades and feedback) showed no discernible shifts. Exploratory analysis asked GPT models to rewrite pre-ChatGPT reports, and these GPT-rewritten texts resembled post-ChatGPT reports more in style and sentiment. Finally, no students disclosed AI use despite clear guidelines, highlighting the ineffectiveness of voluntary disclosure.

While there has been extensive consideration of whether generative artificial intelligence (GAI) developers infringe copyright when scraping online works to train their systems, there has been limited analysis of the legal status and treatment of the underlying shadow libraries, which facilitate such infringement. ‘Shadow libraries’ are vast online repositories of textual content, the majority of which are copyrighted works that have been compiled without permission. The objective of this paper is to develop effective copyright datasets laws that calibrate the protection of author rights with the support of GAI innovation. The paper begins by developing a three-step test (author incentives, access to works and public interest) for determining what constitutes ‘effective’ copyright law in the AI era. Applying this three-step test to the selected case of Australian copyright law, the paper advocates for the implementation of a modified version of the European Union’s copyright exception for text and data mining to provide GAI developers with broader legitimate access to works, with opt-out provisions preserving author rights. Additionally, a more clearly defined requirement that copied works be ‘lawfully accessible’ would operate to exclude pirated works being scraped from shadow libraries. The paper further recommends accompanying mandatory disclosure obligations to assist rightsholders to identify and prosecute extractions of their works from shadow libraries for AI training. It is hoped that this multifaceted and nuanced suite of recommended reforms will provide insights to law and policy makers globally who are seeking to design copyright laws for AI datasets.

Artificial intelligence (AI) transforms how we work, think, and lead. In the Philippines, almost half of the population uses generative artificial intelligence (GenAI) tools at least once a month for work and personal purposes. Despite growing adoption of GenAI, there isn't much information about how it is used in government offices, which still rely heavily on traditional, paper-based methods. This study examined how much GenAI is being used by the Department of Public Works and Highways (DPWH) employees, focusing on areas of application, perceived impacts, and barriers to integration. Data were collected from 50 respondents using Google Forms through a non-probability convenience sampling. Generalist tools like ChatGPT were widely adopted across functions, while specialized tools like Canva, Grammarly, and Quillbot were only used by a few. On the other hand, tools like Firefly, Midjourney, Claude, and Perplexity remained underutilized. Statistical analysis confirmed task-specific patterns of GenAI use, where the chi-square test showed a significant association between task type and tool usage, χ²(48, N = 232) = 91.17, p &lt; .001. The overall index (μ = 2.07, SD = 0.78) reflects a low-to-moderate level of adoption, implying that while GenAI is already introduced in the workplace, its use continues to emerge and has not yet reached full integration into daily operations. Current applications were concentrated in correspondence, documentation, and research. The study recommends policy development, training, and ethical guidelines tailored to government operations to enable strategic and sustainable integration.

This study examines how higher education students use generative artificial intelligence (GenAI) for academic tasks and identifies the types of tasks in which these tools are employed. Our research was conducted in a large multidisciplinary university in Israel. Data from 825 eligible responses to an open-ended item underwent a conventional content analysis using a bottom-up coding approach; data were coded inductively in an iterative process, achieving substantial inter-rater reliability. The findings produced the SOI-MARSMeLLAW framework, which maps eight themes of GenAI use (by frequency, from higher to lowest)—Writing, Learning, Reading, Searching, Meta-learning, Multimedia, Analysis, and Learning Aids—across three information channels: Self, Output, and Input. Overall, we find that students rely on GenAI primarily to support rather than replace their learning, suggesting a flexible and strategic approach that balances efficiency with agency. Two follow-up analyses using the broad data looked at domain-specificity and Task-Centered GenAI Literacy. We found that STEM students emphasizing Learning tasks and non-STEM students highlighting Reading, Meta-learning, and Searching; and that literacy was higher for Writing-related tasks than for Learning-related tasks. These findings have implications for GenAI policy in higher education institutions, and for the redesign of pedagogy and assessment in higher education.

Successful integration of Generative Artificial Intelligence (AI) into healthcare requires understanding of health professionals' perspectives, ideally through data-driven approaches. In this study, we use a semi-structured survey and mixed methods analyses to explore clinicians' perceptions on the utility of generative AI for all types of clinical tasks, familiarity and competency with generative AI tools, and sentiments regarding the potential impact of generative AI on healthcare. Analysis of 116 clinician responses found differing perceptions regarding the usefulness of generative AI across clinical workflows, with information gathering from external sources rated highest and communication rated lowest. Clinician-generated prompt suggestions focused most often on clinician decision making and were of mixed quality, with participants more familiar with generative AI suggesting more high-quality prompts. Sentiments regarding the impact of generative AI varied, particularly regarding trustworthiness and impact on bias. Thematic analysis of open-ended comments highlighted concerns about patient care and the role of clinicians.

Occupation-specific job tasks (OSTs) refer to the duties, responsibilities, and activities associated with a particular occupation, which define the core functions and performance expectations for those engaged in that profession. Efficient recognition and extraction of OSTs from large-scale job description data are essential for establishing a continually updated occupational information system (OIS), such as O*NET, which serves as critical tools for advancing research in work and labor markets. However, this task presents substantial challenges due to its heavy reliance on domain experts for the labor-intensive annotation of job postings, rendering the process time-consuming and difficult to scale for large-scale implementation. To this end, in this article, we present COTR , a novel data-driven framework designed for the efficient recognition of OSTs from job postings, capable of continually identifying new tasks through class-incremental learning. Specifically, we first employ large language models (LLMs) and prompt learning to develop a three-phase process–“expansion, translation, and generation”–that addresses the critical challenge of the absence of predefined OSTs in non-English labor market data, leveraging O*NET as a foundational reference. Subsequently, we introduce a BERT-based model for OST recognition, incorporating a uniquely designed pair-wise loss function that distills valuable insights from ChatGPT or other LLMs, thereby substantially enhancing recognition performance. In addition, to achieve cost-effective training data annotation, we develop an LLM-based coarse-to-fine candidate OSTs generation algorithm, integrating contrastive active learning to optimize the annotation process through human-machine collaboration. Notably, we design a supervised fine-tuning strategy with a novel encoding technique to optimize LLMs, improving the recall rate of the generated candidate OSTs and achieving up to a 343-fold increase in annotation efficiency compared to traditional manual expert annotation in our experiments. Afterward, we propose an efficient class-incremental learning method that incorporates an out-of-distribution (OOD) detection module for identifying potential novel OSTs and a fine-tuning module to extend the model’s recognition capabilities to include newly discovered tasks. Finally, we construct two real-world datasets using job posting data collected from the labor markets of China and the United States, respectively. Extensive experiments on the real-world datasets, along with two publicly available datasets, have demonstrated the effectiveness of the proposed COTR. Furthermore, several case studies showcase the significant benefits of COTR for various downstream applications in labor market analysis, including analyzing the evolving demand for OSTs, assessing the value of OSTs, and recognizing the relationships between OSTs and associated skills.

Artificial Intelligence (AI) is being increasingly implemented within road transport systems worldwide. Next generation of AI, Artificial General Intelligence (AGI) is imminent, and is anticipated to be more powerful than current AI. AGI systems will have a broad range of abilities and be able to perform multiple cognitive tasks akin to humans that will likely produce many expected benefits, but also potential risks. This study applied the EAST Broken Links approach to forecast the functioning of an AGI system tasked with managing a road transport system and identify potential risks. In total, 363 risks were identified that could have adverse impacts on the stated goals of safety, efficiency, environmental sustainability, and economic performance of the road system. Further, risks beyond the stated goals were identified; removal from human control, mismanaging public relations, and self-preservation. A diverse set of systemic controls will be required when designing, implementing, and operating future advanced technologies. Practitioner summary: This study demonstrated the utility of HFE methods for formally considering risks associated with the design, implementation, and operation of future technologies. This study has implications for AGI research, design, and development to ensure safe and ethical AGI implementation.

Syntactic and Semantic Ambiguity Processing in Neural MT from English to French, Spanish and Italian&#x0D; Despite recent advances in artificial intelligence, human translators outperform MT for at least three types of tasks: identifying referents in anaphora (especially of the interphrastic kind), resolving semantic ambiguity (which is mainly due to polysemy or homonymy), and resolving syntactic ambiguity (especially with poorly inflected source languages such as English). Using the results obtained by two freely available online machine translation programs, Google Translate and DeepL, we examine how these two types of ambiguity are processed in translation from English into French, Spanish and Italian. Our results show that the two programs perform well overall in resolving the simplest cases of syntactic ambiguity, with difficulties arising more frequently for noun phrases featuring atypical syntactic divisions and rarely used collocations. MT output for ambiguous structures involving verb roots followed by the –ING morpheme (flying planes, growing pains) is studied, as well as syntactic structures in which two or more nouns are preceded by one or more adjectives. MT handles relatively well the longest of those structures (ADJ ADJ N N N N), probably because their subsets are part of the bilingual or target language monolingual corpora that underlie MT systems. Structures involving head modification and coordination (ADJ N AND N) are also known to pose problems for MT and human translators alike. But since many of the most frequent N AND N structures involve cohyponyms (men and women, brothers and sisters), antonyms (rights and duties, costs and benefits) or near- synonyms (aid and advice), their translation as a whole unit generally triggers the choice of correct syntact dependencies in translation. Structures in which the adjective only modifies the first noun (fresh air and exercise, social sciences and humanities) are much less frequent and are also probably translated as a whole unit. Structures involving premodification, coordination and post-modification may give rise to four distinct types of structures depending on whether long-range dependencies apply (detailed [knowledge and understanding] of the IT industry, [ethnic group] and [place of birth], invaluable [context and [source of information], [close friend] and confidant] of Mr Jones. Structures in which both long-range dependencies apply (integrated prevention and control of pollution) are the ones which most frequently cause errors for MT. Semantic ambiguity cases have been processed with increasing success by MT, especially when collocates vary widely for the main two meanings of homonyms (a well-known example is the word pen). Processing polysemy (for instance the medical use of conditions in pre-existing conditions) is a bit more of a challenge for MT. Other cases involving concentration of several polysemic terms in the same sentence (Changing the placement of beams relative to the staff involves changing the direction of the stems in the beam) also create difficulties for MT when the polysemic terms are used without any of their usual collocates (here in the specialised field of musical edition). Homonymy cases involving grammatical category changes (N-to-V or V-to-N conversion) seem to continue to pose the most difficulties to neural MT, despite increasing consideration of intra- and extraphrastic context. Potentially ambiguous word sequences (treatment increase in as the daily dose and duration of treatment increase), which were processed incorrectly before neuronal MT, are now correctly translated. But word sequences in which one word belongs to a part of speech which is not the most commonly used one (e.g. the noun remains in what remains can be considered) may cause occasional errors. Several examples that involve the verb founder are studied, and they frequently trigger translation of the noun in all three Romance languages (or translations of the verbs find or found due to incorrect segmentation).

The rapid development of emerging technologies, such as generative artificial intelligence (GenAI), has led to a profound transformation in societal demands for talent. In addition to the increasing emphasis on technical training, core competencies, such as problem-solving, are recognized as crucial for the cultivation of high-quality talent. At present, learning environments supported by new technologies are widely used to promote educational reform, with particular focus on the impact of intelligent environments on metacognitive processes. However, research indicates that environments driven by a single technology often suffer from limitations, such as a lack of interactivity and insufficient generativity, which are significant but short-lived. This study focuses on an intelligent learning environment built upon the fusion of “GenAI+Metaverse” as the core variable, exploring its effect on the metacognitive abilities of college students. Using a four-dimensional metacognitive frameworkcomprising “awareness, planning, cognitive strategy, and monitoring”-this study compares the effects of traditional teaching, GenAI-supported teaching, and “GenAI + Metaverse” integrated teaching on students' metacognitive development. The results show that the integrated intelligent environment leads to positive changes across all metacognitive dimensions, with particularly significant improvements in task understanding and strategy execution. Furthermore, metacognitive ability is found to depend on the coordinated support of the technological environment, structured instructional design, strategic guidance, and reflection mechanisms. The study deepens the understanding of the interaction between intelligent technologies and metacognition, providing empirical evidence for the optimization of intelligent teaching environments in higher education and for the cultivation of interdisciplinary talent.

Analyzing the impact of low-carbon city pilot policy on enterprises' labor demand: Evidence from China

ChatGPT is changing our working lives, conjuring visions of revolutionary shifts ahead. ChatGPT is a chatbot based on generative artificial intelligence, which can, e.g., write computer code. This article explores how it might shape the future labour market situation of software engineers. A Delphi study with 14 experts was conducted in Finland. The first round identified possible futures, and the second round assessed their probabilities. Five scenarios prevailed: the unlikely scenario that the status quo persists; the ambivalent scenario that ChatGPT can replace software engineers to a large extent; the likely scenario that computer departments in startups embrace ChatGPT; the likely scenario that ChatGPT use proliferates among software engineers to increase productivity; and the highly likely scenario that ChatGPT makes computer programming accessible to the masses. Findings contradict previous discussions that technological advancements might take over especially routine tasks. ChatGPT can also take over non-routine tasks. Moreover, findings underline that digitalisation does not only bring about a choice between upskilling and employability loss, but also a democratisation of knowledge and expertise. Software engineers and companies might use the findings as an impetus for upskilling, while universities might feel nudged to incorporate ChatGPT more strongly into their curricula.