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This study aims to develop an associative text analyzer (ATA) to support users in quickly grasping and interpreting the content of large amounts of text through text association recommendations, facilitating the identification of the contextual relationships between people, events, organization and locations for digital humanities. Additionally, by providing text summaries, the tool allows users to link between distant and close readings, thereby enabling more efficient exploration of related texts.

To verify the effectiveness of this tool in supporting exploration of historical texts, this study uses a counterbalanced design to compare the use of the digital humanities platform for Mr. Lo Chia-Lun’s Writings (DHP-LCLW) with and without the ATA to assist in exploring different aspects of text. The study investigated whether there were significant differences in effectiveness for exploring textual contexts and technological acceptance as well as used semi-structured in-depth interviews to understand the research participants’ viewpoints and experiences with the ATA.

The results of the experiment revealed that the effectiveness of text exploration using the DHP-LCLW with and without the ATA varied significantly depending on the topic of the text being explored. The DHP-LCLW with the ATA was found to be more suitable for exploring historical texts, while the DHP-LCLW without the ATA was more suitable for exploring educational texts. The DHP-LCLW with the DHP-LCLW was found to be significantly more useful in terms of perceived usefulness than the DHP-LCLW without the ATA, indicating that the research participants believed the ATA was more effective in helping them efficiently grasp the related texts and topics during text exploration.

The study’s practical implications lie in the development of an ATA for digital humanities, offering a valuable tool for efficiently exploring historical texts. The ATA enhances users’ ability to grasp and interpret large volumes of text, facilitating contextual relationship identification. Its practical utility is evident in the improved effectiveness of text exploration, particularly for historical content, as indicated by users’ perceived usefulness.

This study proposes an ATA for digital humanities, enhancing text exploration by offering association recommendations and efficient linking between distant and close readings. The study contributes by providing a specialized tool and demonstrating its perceived usefulness in facilitating efficient exploration of related texts in digital humanities.

The purpose of this study is to gain insight into how citations are distributed and concentrated in the introduction, methods, discussion, results and other sections of journal articles to determine which section has received the most citations and whether the citation concentration score affects how articles rank.

The present study uses scite.ai and the Dimensions database to emphasize the significance of including multiple in-text citations in evaluating the impact and quality of journal publications. The study has two approaches: paper-based and author-based.

The study provides empirical insights into how variations in ranking are observed when citation concentration is considered in the evaluation process. It also suggests that in-text citations be used as an evaluation criterion or aspect for assessing the impact and quality of journals, publications and authors.

This study underscores the importance of considering citation concentration when evaluating journal articles. To assess highly cited articles, it suggests using the CC-index method, which is based on scite.ai.

Safety management plays an important part in coal mine construction. Due to complex data, the implementation of the construction safety knowledge scattered in standards poses a challenge. This paper aims to develop a knowledge extraction model to automatically and efficiently extract domain knowledge from unstructured texts.

Bidirectional encoder representations from transformers (BERT)-bidirectional long short-term memory (BiLSTM)-conditional random field (CRF) method based on a pre-training language model was applied to carry out knowledge entity recognition in the field of coal mine construction safety in this paper. Firstly, 80 safety standards for coal mine construction were collected, sorted out and marked as a descriptive corpus. Then, the BERT pre-training language model was used to obtain dynamic word vectors. Finally, the BiLSTM-CRF model concluded the entity’s optimal tag sequence.

Accordingly, 11,933 entities and 2,051 relationships in the standard specifications texts of this paper were identified and a language model suitable for coal mine construction safety management was proposed. The experiments showed that F1 values were all above 60% in nine types of entities such as security management. F1 value of this model was more than 60% for entity extraction. The model identified and extracted entities more accurately than conventional methods.

This work completed the domain knowledge query and built a Q&A platform via entities and relationships identified by the standard specifications suitable for coal mines. This paper proposed a systematic framework for texts in coal mine construction safety to improve efficiency and accuracy of domain-specific entity extraction. In addition, the pretraining language model was also introduced into the coal mine construction safety to realize dynamic entity recognition, which provides technical support and theoretical reference for the optimization of safety management platforms.

The use of generative artificial intelligence (genAi) language models such as ChatGPT to write assignment text is well established. This paper aims to assess to what extent genAi can be used to obtain guidance on how to avoid detection when commissioning and submitting contract-written assignments and how workable the offered solutions are.

Although ChatGPT is programmed not to provide answers that are unethical or that may cause harm to people, ChatGPT’s can be prompted to answer with inverted moral valence, thereby supplying unethical answers. The authors tasked ChatGPT to generate 30 essays that discussed the benefits of submitting contract-written undergraduate assignments and outline the best ways of avoiding detection. The authors scored the likelihood that ChatGPT’s suggestions would be successful in avoiding detection by markers when submitting contract-written work.

While the majority of suggested strategies had a low chance of escaping detection, recommendations related to obscuring plagiarism and content blending as well as techniques related to distraction have a higher probability of remaining undetected. The authors conclude that ChatGPT can be used with success as a brainstorming tool to provide cheating advice, but that its success depends on the vigilance of the assignment markers and the cheating student’s ability to distinguish between genuinely viable options and those that appear to be workable but are not.

This paper is a novel application of making ChatGPT answer with inverted moral valence, simulating queries by students who may be intent on escaping detection when committing academic misconduct.

The narrow purpose of this article is to review de Fremery’s (2024) book about the bibliographic foundations of information science. The broader purpose is to consider the actual as well as the potential relevance of the field(s) of bibliography for information science besides the book under review.

This review essay examines the arguments put forward by de Fremery (2024), introduces concepts and traditional lore from the study of bibliography and presents internal conflicts or paradigms in the field of bibliography. It relates this information to foundational issues in information science.

De Fremery’s basic ambition of basing information science in the field of bibliography is important, and so is the attempt to consider bibliography in relation to contemporary information technologies such as machine learning and data science. The book under review fails, however, to describe the relations between different positions in bibliography, such as enumerative, analytical, descriptive, critical and historical bibliography in relation to information science. It rather tends to make problematic claims, for example, that scientific experiments are based on bibliographical methods, and to describe the relation of bibliography to information science on the basis of such interpretations. Nonetheless, the book is a serious attempt to consider the field of bibliography and thereby support the focus on documents in information science.

Information science often suffers because of ambiguities in the concept of information. When information science is understood as the study of literature-based answering, much else falls into place. The field of bibliography is a core concept for this understanding and re-orientation of information science, for example, by establishing the core relation between bibliography, information searching and knowledge organization.

This research aims to explore the integration of Bing Chat, an artificial intelligence (AI)-powered conversational assistant, into academic libraries and its potential impact on reference services.

This study analyzes existing literature to investigate the integration of Bing Chat in academic libraries. This research focuses on its features, capabilities and implications for enhancing user experiences and addressing information needs.

The findings highlight that Bing Chat’s conversational interface facilitates more dynamic and context-aware reference interactions, fostering user empowerment and personalized learning pathways. The integration of Bing Chat with existing library resources enhances resource discovery, navigational assistance and engagement with visual content. However, challenges related to accuracy, privacy and algorithmic bias require careful consideration.

This research contributes to the literature by providing an in-depth exploration of Bing Chat’s integration into academic libraries, emphasizing its transformative potential for reference services. This study addresses ethical and social dimensions associated with AI-driven tools in library environments, offering insights into ensuring fairness, privacy and user trust.

Nurses work in a wide range of roles and settings, each with inherent stressors that can impact mental well-being. High levels of stress, burnout and clinical errors have been linked to the levels of support nurses have in their workplaces. Both consumers and health organisations benefit when nurses can engage in their work with support. Digital applications offer a promising option for supporting the mental well-being of nurses. This paper aims to provide analysis and synthesis of studies from the emerging field of research investigating the potential of digital applications to support the mental well-being of nurses.

An integrative literature review was conducted focused on studies published within the last ten years that employed digital applications specifically included to support nurses’ mental well-being. As part of the review inductive thematic analysis was then undertaken identifying four main themes diversity of digital applications; wide applicability across nursing; differing dimensions of well-being; and challenges to integration.

The study of digital applications used among nurses is a modern emerging field of research. Digital applications have considerable design variability and are being rolled out across multiple nursing specialities. They target differing aspects of mental well-being, offering promise despite the challenges to integration.

Digital applications are an emergent and promising option for supporting nurses' mental well-being. They have the potential to be personalised, cost-efficient and engaging platforms. The fast-paced technological advancement of digital applications will lead to the adoption of novel means of supporting nurses.

Career preparedness is critical to successfully transitioning from college to a full-time work environment. Being prepared means students have the technical and non-technical skills to help an organization meet its strategic goals. In many instances, students lack the requisite career competencies and collaborative skills. This study proposes designing an instructor-led, career-driven quasi-virtual internship to address this issue and improve students’ preparedness and teamwork.

Our research integrates and expands the four-step processes outlined in instructional and course redesign theories, including analysis, design and development, implementation and evaluation. In the evaluation phase, a survey is used to collect data, and natural language processing is applied to identify the emerging themes. The sample included 104 undergraduate students enrolled in an information systems degree program, which resulted in a digital corpus of 40,744 words for analysis.

Results indicate that the implementation of instructor-led quasi-virtual internships offered a comprehensive career experience comparable to an onsite or virtual company-sponsored internship in five notable areas: (1) application of technical knowledge, (2) critical thinking, (3) time management, (4) application of IT project management and (5) collaborative skills.

Our study only evaluated participants in our treatment group. Future research should examine the differences between students who complete sponsored versus instructor-led quasi-virtual internships. Researchers can add a control group of students who receive a theoretically based capstone course. Future research can simulate randomized controlled trials (RCT) (Chalmers et al., 1981) to measure the effectiveness of quasi-virtual internships. A benefit of this research is that an open-ended survey allows for collecting rich, multifaceted primary data. The second limitation is that the study included only single-item questions. Future authors can create and validate a survey instrument based on the themes and concepts emanating from our investigation. For example, they can operationalize team dynamics and career preparedness using a reflective model in which the underlying construct drives the indicators, requiring multiple items to examine each construct (Coltman et al., 2008). Third, the sample size in the study was relatively small. We can increase the sample size using a time-series dataset with multiple measurement periods. The fourth limitation is context; this study was conducted at a public university; other institutions may have varied teaching approaches, student demographics and resources. By expanding our study using a multi-site approach (Ballantyne et al., 2012), we can increase cross-sectional sample sizes and improve the generalizability of the study’s results.

This study provides several practical insights for educators by examining quasi-virtual internships. Academic institutions that want to offer internships but struggle to establish industry partners can implement the quasi-internship model as an alternative approach. The study highlights that students gain critical insight into their future careers during these internships by being familiar with industry concepts and tools. We also provide insights into engaging students in “meaningful collaboration.” We suggest the instructor provide some time during the class sessions for group work to improve coordination and introduce industry-level tools that provide a more applied approach to IS education.

The National Survey of College Internships (NSCI) 2021 indicates that underrepresented and first-generation students were less likely to participate in internships. Our research may positively impact diverse communities since the quasi-virtual internship allows all students to participate once they are enrolled in a capstone class.

To our knowledge, this study is the first to utilize latent semantic analysis to analyze students’ feedback to improve course design, career preparedness and team dynamics.

In ubiquitous games, sensor data and the player’s dynamic profile personalize the experience, adapting scenarios and difficulty to the player’s context. However, manually creating custom virtual environments becomes impractical due to the scalability required in the real world, where gameplay depends on the player’s environment and various points of interest. Procedural content generation (PCG) naturally emerges as an automated solution. This study aims to review the state of the art among pervasive games that use procedural generation techniques.

The authors performed a systematic review of the literature on PCG techniques in ubiquitous games. The research methodology included initial database searches, forward snowballing and backward snowballing.

The authors selected 40 articles published from 2008 to 2022 after analyzing 1,017 PCG articles. Many of these paper use dynamic difficulty generation methods that affect game customization, balance and playability. The majority of titles are in the area of education and require rigorous evaluation. Some articles mention the use of PCG but provide limited details of algorithms or processes.

The low presence of recent articles stands out, which can lead to two conclusions: poor use of indexing of articles in the area or low amount of research over these years. One of the search bases did not enter due to the number of keywords used.

The paper is a guide for researchers in the area of pervasive games who are interested in using PCG techniques in their games.

This approach revealed articles combining PCG and ubiquitous games, with no previous systematic reviews at this intersection.

The purpose of this research is to analyse the level of knowledge about the Sustainable Development Goals (SDGs) of pre-service teachers in Early Childhood and Primary School Education at the University of Valencia (Spain) and the origin of this knowledge over several academic years, as well as to compare it with that of students of the Degree in Environmental Sciences at the same university.

A questionnaire was designed, validated and administrated to 1,040 students of Degree in Early Childhood Education, Primary School Education and Environmental Science of the University of Valencia to analyse their knowledge of the SDGs and their evolution over three academic years (2019–20, 2020–21, 2021–22).

Statistical analysis shows that most of the participants are either unaware of the SDGs and their importance or have insufficient knowledge of the SDGs and their linkages.

The need to continue promoting teaching resources to favour the implementation of education for the SDG in initial teacher training is highlighted.

Pre-service teachers can become agents of change if they understand the magnitude and complexity of the socio-environmental crisis and get involved through their educational action in the required measures to advance in the construction of more sustainable societies.

This study aims to emphasize the necessary training and involvement of students of the Degree in Early Childhood Education, Primary School Education and Environmental Science at the University of Valencia to advance in the transition to Sustainability.