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Knowledge, Volume 5, Issue 4 (December 2025) – 3 articles

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27 pages, 2572 KB  
Article
Automating Lexical Graph Construction with Large Language Models: A Scalable Approach to Japanese Multi-Relation Lexical Networks
by Benedikt Perak and Dragana Špica
Knowledge 2025, 5(4), 24; https://doi.org/10.3390/knowledge5040024 - 27 Oct 2025
Abstract
In recent advancements within natural language processing (NLP), lexical networks play a crucial role in representing semantic relationships between words, enhancing applications from word sense disambiguation to educational tools. Traditional methods for constructing lexical networks, however, are resource-intensive, relying heavily on expert lexicographers. [...] Read more.
In recent advancements within natural language processing (NLP), lexical networks play a crucial role in representing semantic relationships between words, enhancing applications from word sense disambiguation to educational tools. Traditional methods for constructing lexical networks, however, are resource-intensive, relying heavily on expert lexicographers. Leveraging GPT-4o, a large language model (LLM), our study presents an automated, scalable approach to creating multi-relational Japanese lexical networks for the general Japanese language. This study builds on previous methods of integrating synonyms but extends to other relations such as hyponymy, hypernymy, meronymy, and holonomy. Using a combination of structured prompts and graph-based data storage, the model extracts detailed lexical relationships, which are then systematically validated and encoded. Results reveal a substantial expansion in network size, with over 155,000 nodes and 700,000 edges, enriching Japanese lexical associations with nuanced hierarchical and associative layers. Comparisons with WordNet show substantial alignment in relation types, particularly with soft matching, underscoring the model’s efficacy in reflecting the multifaceted nature of lexical semantics. This work contributes a versatile framework for constructing expansive lexical resources that hold promises for enhancing NLP tasks and educational applications across various languages and domains. Full article
(This article belongs to the Special Issue Knowledge Management in Learning and Education)
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32 pages, 781 KB  
Article
Navigating Emotional Barriers and Cognitive Drivers in Mobile Learning Adoption Among Greek University Students
by Stefanos Balaskas, Vassilios Tsiantos, Sevaste Chatzifotiou, Dionysia Filiopoulou, Kyriakos Komis and George Androulakis
Knowledge 2025, 5(4), 23; https://doi.org/10.3390/knowledge5040023 - 11 Oct 2025
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Abstract
Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as [...] Read more.
Mobile learning (m-learning) technologies are gaining popularity in universities but not uniformly across institutions because of cognitive, affective, and behavior obstacles. This research tested and applied an expansion of the Technology Acceptance Model (TAM) with technostress (TECH) and resistance to change (RTC) as affective obstacles, as well as the core predictors of perceived usefulness (PU), perceived ease of use (PE), and perceived risk (PR). By employing a cross-sectional survey of Greek university students (N = 608) and partial least squares structural equation modeling (PLS-SEM), we tested direct and indirect impacts on behavioral intention (BI) to apply m-learning applications. The results affirm that PU and PE are direct predictors of BI, while PR has no direct impact on BI but acts indirectly through TECH and RTC. Mediation is partial in terms of PE and PU and indirect-only (complete) in terms of PR with respect to the impact of affective states on adoption. Multi-group comparisons found differences in terms of gender, age, confidence, and years of use but not frequency of use, implying that psychological and experiential characteristics have a greater impact on intention than habitual patterns. These results offer theory-driven and segment-specific guidelines for psychologically aware, user-focused m-learning adoption in higher education. Full article
(This article belongs to the Special Issue Knowledge Management in Learning and Education)
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15 pages, 656 KB  
Article
The New Normal and the Era of Misknowledge—Understanding Generative AI and Its Impacts on Knowledge Work
by Zhiguo Yang, Xiang Guo and Peng Zhang
Knowledge 2025, 5(4), 22; https://doi.org/10.3390/knowledge5040022 - 9 Oct 2025
Viewed by 446
Abstract
The revealed capability of generative AI tools can significantly transform the way knowledge work is conducted. With more tools being built and implemented, generative AI-aided knowledge work starts to emerge as a new normal, where knowledge workers shift a significant portion of their [...] Read more.
The revealed capability of generative AI tools can significantly transform the way knowledge work is conducted. With more tools being built and implemented, generative AI-aided knowledge work starts to emerge as a new normal, where knowledge workers shift a significant portion of their workloads to the tools. This new normal can lead to many concerns and issues including workers’ mental health, employees’ confusion in production, and potential spreading misknowledge. Considering the substantial portion of knowledge work in the US economy, this paper calls for more research to be conducted on this important area. This paper synthesizes relevant economic and behavioral research findings in the AI automation field and opinions of field experts, and presents a comprehensive framework, “generative AI-aided knowledge work”. This framework theoretically addresses concerns such as job replacement and organizational and behavioral factors in using generative AI and provides directions for future research and guidelines for practitioners in incorporating generative AI tools. This is one of the early attempts to provide a comprehensive overview of generative AI’s impacts on knowledge workers and production. It has the potential to seed future research in many areas such as countering misknowledge and employees’ mental health. Full article
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