Human Behavior and Emerging Technologies最新文献

{"title":"Smart Archives: A Machine Learning Approach","authors":"Anurag Deb,&nbsp;Anubhab Ray","doi":"10.1155/hbe2/1148949","DOIUrl":"https://doi.org/10.1155/hbe2/1148949","url":null,"abstract":"<p>Digital archival data have an increasing exponential capacity, thereby revealing major weaknesses of conventional archival systems such as sluggish retrieval speed, erroneous categorization, and poor adaptation to heterogeneous formats. While the avenue that machine learning (ML) presents is highly promising for transformation, its exploration within the domain of archival science remains limited, with specific emphasis on multimodal data. This study outlines a hybrid ML framework to bring together CNN-based image document classification with transformer-based natural language processing for metadata extraction. With three datasets—historical manuscripts, government records, and multimedia archives—advanced data augmentation, optical character recognition (OCR) correction, and multimodal fusion were applied. Evaluation was based on precision, recall, <i>F</i>1-score, and latency metrics, along with user surveys. The framework achieved a mean <i>F</i>1-score of 0.92, outperforming rule-based systems that scored an <i>F</i>1 of 0.76. Image classification results of 94% on degraded manuscripts and 40% on lessening errors in metadata by NLP. User testing timed 60% faster retrievals, and 82% of archivists rated the system “intuitive” or “highly efficient.” ML enhances archival efficiency, scalability, and accuracy in a significant way. The study offers a validated roadmap for implementing ML in a range of archival setups, addressing key challenges in digital preservation. We did all experiments on simulated generated datasets due to hardware limitations and thus provide a proof of concept for the development of scalable ML archival frameworks.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/1148949","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784151","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Incorporating AI-Assisted Language Learning Application to Enhance Speaking Skills in the English Classroom","authors":"Satyanarayana Karedla,&nbsp;Kranthi Priya Oruganti","doi":"10.1155/hbe2/5550193","DOIUrl":"https://doi.org/10.1155/hbe2/5550193","url":null,"abstract":"<p>Artificialintelligence (AI) plays a paramount role in the field of education. By integrating AI-powered language learning applications into language classrooms, educators can support learners to enhance their speaking skills. The integration of AI creates new opportunities to improve the quality of language education. To substantiate the above argument, the paper discusses a study conducted in which an AI language learning application, “Duolingo” was used to improve the speaking skills of 122 undergraduate students that were selected to be part of the research work. This study has adopted a mixed method of research to know how technology could be incorporated with language learning to enhance the speaking skills. This paper tries to emphasize the impact of AI-driven language learning tools in developing speaking skills by utilizing AI platforms where learners can use many diversified resources, personalized opportunities for learning, enhanced language practice, immediate feedback, and flexibility in learning. This study draws its theoretical underpinnings to Krashen′s input hypothesis where learners need comprehensible input in context of second language learning. This study employed a mixed methods approach, collected quantitative data via a structured 21-item questionnaire administered to 122 students, and qualitative data collected through classroom observations and informal interviews. To arrive at the findings, data were interpreted through descriptive analysis. The results of the study revealed that incorporation of technology in language classes to enhance speaking skills yields better results compared with traditional classroom activities.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/5550193","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784150","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Building Successful Career Pathways: AI-Driven English Communication Skills for Engineering Students","authors":"Prasanna Lakshmi Narra","doi":"10.1155/hbe2/1727267","DOIUrl":"https://doi.org/10.1155/hbe2/1727267","url":null,"abstract":"<p>This study assesses the introduction of artificial intelligence (AI) into English-language training and its direct impact on engineering graduates′ job market preparedness. In the global, technology-driven job market, good English communication skills are essential in professional engineering education. This study looks at how interactive, AI-powered educational applications, such as automated writing feedback systems, adaptive learning software, and speech evaluation platforms, can help students develop essential linguistic abilities by focusing on specific skills like grammar, vocabulary, and pronunciation. Using empirical performance data from an <i>N</i> = 80 student cohort and institutional measures, this study examined how automated systems provide personalized learning paths tailored to individual competencies. The empirical findings demonstrate that tailored AI assistance significantly improves language-learning outcomes, directly enhancing professional readiness and recruitment prospects. Based on these results, the paper offers practical recommendations for integrating AI tools into engineering education to maximize their benefits and address limitations, aiming to inform educators and policymakers about responsible AI use.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/1727267","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"An Explainable Machine Learning Approach to Predict Suicidal Thoughts in University Students: The SMS-ME Project","authors":"Francesco Prinzi,&nbsp;Giada Tripoli,&nbsp;Nicole Bonaccorso,&nbsp;Eleonora Bongiorno,&nbsp;Marco Daino,&nbsp;Roman Byrtus,&nbsp;Caterina La Cascia,&nbsp;Tiziana Currieri,&nbsp;Giuseppe Maniaci,&nbsp;Ilaria Vella,&nbsp;Elisa Galatà,&nbsp;Giulia Ferrara,&nbsp;Fabio Seminerio,&nbsp;Crocettarachele Sartorio,&nbsp;Alessandra Scaglione,&nbsp;Alessandra Giammanco,&nbsp;Rosa Lo Baido,&nbsp;Maria Rosaria Anna Muscatello,&nbsp;Carmela Mento,&nbsp;Clara Lombardo,&nbsp;Maria Salvina Signorelli,&nbsp;Maria Stella Epifanio,&nbsp;Gaia Albano,&nbsp;Francesco Vitale,&nbsp;Daniele La Barbera,&nbsp;Walter Mazzucco,&nbsp;Claudio Costantino,&nbsp;Diego Quattrone,&nbsp;Salvatore Vitabile,&nbsp;Laura Ferraro","doi":"10.1155/hbe2/4663853","DOIUrl":"https://doi.org/10.1155/hbe2/4663853","url":null,"abstract":"<p>The increasing prevalence of suicidal ideation among university students, especially those enrolled in medical and healthcare courses, necessitates innovative approaches for early detection and prevention. The southern part of Italy presents a distinct cultural, social, and economic context and is under-represented in research. This study leverages the capabilities of machine learning methods and explainable artificial intelligence (XAI) to address this challenge, developing a predictive model for suicidal thoughts within a large sample of university students in Sicily, the southernmost and largest Italian region. The XGBoost algorithm was implemented for model training on survey data from 3278 students, including sociodemographic factors, academic performance metrics, and mental health indicators such as the Depression, Anxiety, and Stress Scale short version (DASS-21) and the Eating Attitude Test short version (EAT-26). The optimized XGBoost achieved promising performance, with AUROC ranging from 0.719 to 0.808 and balanced accuracy between 0.666 and 0.780 across internal and external test sets. Probability calibration was generally reasonable (Brier 0.151–0.185; ECE 0.035–0.120), supporting the reliability of predicted risk estimates. Performance in general varied by degree program, suggesting cohort-specific characteristics. Our findings underscore the potential of machine learning models to identify at-risk students and XAI for understating model prediction, emphasizing the importance of early intervention and targeted mental health support in university settings. The model′s performance and explainability highlight the potential in developing a student-centric and self-assessment framework while helping to mitigate potential ethical concerns associated with predictive mental health tools.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/4663853","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148784352","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Intervention of AI in Interpreting the Novels of Shobha De","authors":"Gaddam Swarna Latha,&nbsp;V. B. Chithra","doi":"10.1155/hbe2/4251977","DOIUrl":"https://doi.org/10.1155/hbe2/4251977","url":null,"abstract":"<p>In the present scenario, artificial intelligence (AI) empowerment is becoming more and more popular, and it is spreading its wide uses in various disciplines. Likewise, intervention of AI in English language and literature also cannot be isolated and deviated. AI has totally transformed different areas of English with its sophisticated machine learning algorithms in understanding, analyzing, creating new works, finding out research gaps, and so on. AI algorithms can also reveal hidden themes, patterns, and styles that are impossible for traditional methods and lead to new compositions in literary works. AI helps in sharpening the brain of human which creates new trends, complex themes, problem solving, reasoning, and perception. AI tools convert text into speech, translate, summarize, make images, and even code and decode depending on necessity. AI enhances the proficiency of academicians and guides the researchers on their journey. This article explores the role of AI in interpreting and analyzing the novels of Shobha De, who delves into the minds of people especially women and characterizes the characters in her novels taking from the real life. AI helps in understanding her novels in her point of view to the readers which enables them to enhance their knowledge and avoid misconceptions which leads to social issues.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/4251977","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753802","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Correction to “Integrative Review of Recruitment Literature: Conceptual Evolution, Technological Developments, and Scope for Future Research”","authors":"","doi":"10.1155/hbe2/9787948","DOIUrl":"https://doi.org/10.1155/hbe2/9787948","url":null,"abstract":"<p>P. Sharma and M. Padhi, “Integrative Review of Recruitment Literature: Conceptual Evolution, Technological Developments, and Scope for Future Research,” <i>Human Behavior and Emerging Technologies</i>, 2026, 3210624, https://doi.org/10.1155/hbe2/3210624.</p><p>The affiliation of both the authors (Preeti Sharma and Mousumi Padhi) should be updated to: XIM University, Nijigada, Kurki, Harirajpur, Odisha, India.</p><p>We apologize for this error.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-12","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/9787948","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Facilitators and Fears: What Influences Students′ Continued Use of AI in Learning Systems?","authors":"Rana Saeed Al-Maroof,&nbsp;Ragad M. Tawafak,&nbsp;Waleed Mugahed Al-Rahmi,&nbsp;Ibrahim Yaussef Alyoussef","doi":"10.1155/hbe2/8512492","DOIUrl":"https://doi.org/10.1155/hbe2/8512492","url":null,"abstract":"<p>The impact of artificial intelligence technologies is rapid and continuous, leading to a huge transformation in learning technology systems. This allows students to perform various challenging tasks simultaneously, leading to more complex, creative, and innovative learning technologies and systems that can positively affect productivity. Based on this, this study makes two fundamental contributions to the literature. First, our research expands the literature on the impact of AI application technologies in learning technology systems environments by focusing on the factors that facilitate the continuous intention to use AI application technologies. Second, it deepens the theoretical understanding of the integrated fear of using AI and its effect on students′ future perspectives by focusing on AI learning anxiety and AI job security anxiety. Thus, this study intends to investigate both the facilitators and fears behind using AI technologies by developing a research framework that embraces six factors. A pilot study was conducted using the data collected from 78 participants from a university (Al Buraimi University College) in Oman to test the proposed model. A PLS-SEM program was used to test and validate the model, which consisted of six hypotheses used to describe the model instruments. The results of the current study show that the four hypotheses have a significant impact on the continuous intention to use AI application technology. H1 and H6 show not accepted results after applying the test. This study reveals the validity of the study and its impact on developing factors influencing the continued intention to use AI application technologies.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/8512492","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148753173","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Understanding Generative AI Adoption: An Integrated Model of Emotional, Cognitive, and Motivational Drivers","authors":"Van Kien Pham,&nbsp;Ai Van Huynh,&nbsp;Linh Le Phuong Giao,&nbsp;Oanh Hoang","doi":"10.1155/hbe2/4286265","DOIUrl":"https://doi.org/10.1155/hbe2/4286265","url":null,"abstract":"<p>This study develops and empirically tests an associational model of generative artificial intelligence (GenAI) adoption by focusing on emotional resistance, social-cognitive appraisal, and motivational agency. Rather than claiming that the breadth of constructs alone is novel, the study specifies how perceived AI competence functions as a GenAI-specific capability cue that may be interpreted through anthropomorphic and social-categorization processes, and how this competence cue is translated into adoption through trust, perceived control, and attitude. Drawing on survey data from 215 respondents with experience or awareness of GenAI in academic and research-related contexts, the study examines perceived anxiety, perceived risk, perceived AI competence, perceived trust, perceived control, attitude toward GenAI use, and behavioral intention. The model was assessed using a nonexperimental cross-sectional design and PLS-SEM-oriented reporting procedures; therefore, the results are interpreted as theoretically ordered associations rather than causal effects. The findings show that attitude is the strongest predictor of behavioral intention, whereas perceived control and perceived AI competence also have positive direct associations with intention. Perceived trust mainly operates through attitude rather than directly predicting intention. Perceived anxiety has a small negative direct association with behavioral intention, whereas perceived risk shows a capability-trust dissociation; it reduces trust but does not significantly reduce perceived AI competence. Reverse-outcome robustness checks further support treating the model as an associational explanation rather than as evidence of temporal mediation. The model explains 71.9% of the variance in behavioral intention. The study contributes to GenAI adoption research by clarifying how perceived competence, trust calibration, perceived agency, anxiety, and ethical risk appraisal jointly shape adoption among informed users.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/4286265","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752511","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"RETRACTION: Multimodal Data Fusion Framework for Early Prediction of Autism Spectrum Disorder","authors":"Human Behavior and Emerging Technologies","doi":"10.1155/hbe2/9896173","DOIUrl":"https://doi.org/10.1155/hbe2/9896173","url":null,"abstract":"<p>RETRACTION: M. Sha, H. Al-Dossary, and M. P. Rahamathulla, “Multimodal Data Fusion Framework for Early Prediction of Autism Spectrum Disorder,” <i>Human Behavior and Emerging Technologies</i> (2025): 1496105, https://doi.org/10.1155/hbe2/1496105.</p><p>The above article, published online on 19 February 2025 in Wiley Online Library (https://www.wileyonlinelibrary.com), has been retracted by agreement between <i>Human Behavior and Emerging Technologies</i>; and John Wiley &amp; Sons Ltd.</p><p>The retraction was agreed upon due to concerns regarding both the reliability of the study and significant privacy issues. The <i>dataset</i> used in the study contains images of children purported to have, or not have autism spectrum disorder (ASD), sourced from the internet without any documented clinical history or independent verification of an ASD diagnosis, raising concerns about the scientific validity of the dataset. In addition, the images of minors have been used and presented in the article without consent from the individuals or their parents/legal guardians. For these reasons, the article has been retracted and the corresponding images of children have been redacted. The authors have been informed of this decision but were not available for final confirmation.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-08-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/9896173","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148752500","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"The Double-Edged Sword: AI in Education as a Challenge to Management Theory and Human Capital Development","authors":"Hasham Khaleel Al Musawi","doi":"10.1155/hbe2/8223597","DOIUrl":"https://doi.org/10.1155/hbe2/8223597","url":null,"abstract":"<p>The integration of generative artificial intelligence (AI) into higher education exposes a critical gap in management theory concerning the governance of technologies that disrupt core institutional processes. Moving beyond pedagogical debates, we argue that AI′s challenges, eroding academic integrity, decoupling credentials from skills, and exacerbating inequity, represent a fundamental crisis of institutional legitimacy and strategic governance for knowledge-intensive organizations. This paper makes three core contributions. First, we provide a multitheoretic diagnosis through institutional theory, human capital theory, and the resource-based view, revealing AI as a compound threat to normative, credentialing, and resource-governance systems. Second, in response to the limitations of reactive models, we propose a formal theoretical framework: Proactive Institutional Entrepreneurship for Technology Governance (PIET-Gov). PIET-Gov specifies three interdependent processes, <i>anticipatory governance</i>, <i>ethical scaffolding</i>, and <i>adaptive institutional work</i>, and articulates testable propositions about how they interact to preserve institutional legitimacy, maintain human capital integrity, and ensure equitable resource access. Third, we translate this framework into actionable implications for leaders in credentialing institutions and outline a research agenda to advance theories of institutional change, technology governance, and human capital signalling. Through reframing technological disruption as a governance imperative, this paper advances a midrange theory for how organizations can proactively shape, rather than be passively shaped by, transformative cognitive technologies.</p>","PeriodicalId":36408,"journal":{"name":"Human Behavior and Emerging Technologies","volume":"2026 1","pages":""},"PeriodicalIF":4.9,"publicationDate":"2026-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1155/hbe2/8223597","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"148616781","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}