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The purpose of this study is to develop an intelligent tutoring system (ITS) for programming learning based on information tutoring feedback (ITF) to provide real-time guidance and feedback to self-directed learners during programming problem-solving and to improve learners’ computational thinking.

By analyzing the mechanism of action of ITF on the development of computational thinking, an ITF strategy and corresponding ITS acting on the whole process of programming problem-solving were developed to realize the evaluation of programming problem-solving ideas based on program logic. On the one hand, a lexical and syntactic analysis of the programming problem solutions input by the learners is performed and presented with a tree-like structure. On the other hand, by comparing multiple algorithms, it is implemented to compare the programming problem solutions entered by the learners with the answers and analyze the gaps to give them back to the learners to promote the improvement of their computational thinking.

This study clarifies the mechanism of the role of ITF-based ITS in the computational thinking development process. Results indicated that the ITS designed in this study is effective in promoting students’ computational thinking, especially for low-level learners. It also helped to improve students’ learning motivation, and reducing cognitive load, while there’s no significant difference among learners of different levels.

This study developed an ITS based on ITF to address the problem of learners’ difficulty in obtaining real-time guidance in the current programming problem-solving-based computational thinking development, providing a good aid for college students’ independent programming learning.

The study integrated understanding by design-Internet of Things (UbD-IoT) education with design thinking and computational thinking to plan and design an IoT course. Cross-domain application examples were employed to train students in problem-understanding, deep thinking and logical design for IoT applications.

In this study, the UbD model was integrated with design thinking and computational thinking in the planning and design of an IoT course. The examples of cross-domain applications were used to train students to understand a problem by engaging themselves in deep thinking and helping them think and design logically for an IoT application.

The UbD-IoT learning design greatly decreased students' overall cognitive load. UbD-IoT learning has a significant impact on the performance of computational thinking in problem-solving and problem-understanding. The impact of UbD-IoT learning on logical thinking and program learning cognition in students needs to be verified.

The results of this study have shown that the UbD model is effective in reducing the cognitive load of a learning course and also strengthens T-competencies in the lateral skills of computational thinking, critical problem-solving, logical thinking and creative thinking.

This pragmatic research paper aims to unravel the smart vision-based method (SVBM), an AI program to correlate the computer vision (recorded and live videos using mobile and embedded cameras) that aids in manual lifting human pose deduction, analysis and training in the construction sector.

Using a pragmatic approach combined with the literature review, this study discusses the SVBM. The research method includes a literature review followed by a pragmatic approach and lab validation of the acquired data. Adopting the practical approach, the authors of this article developed an SVBM, an AI program to correlate computer vision (recorded and live videos using mobile and embedded cameras).

Results show that SVBM observes the relevant events without additional attachments to the human body and compares them with the standard axis to identify abnormal postures using mobile and other cameras. Angles of critical nodal points are projected through human pose detection and calculating body part movement angles using a novel software program and mobile application. The SVBM demonstrates its ability to data capture and analysis in real-time and offline using videos recorded earlier and is validated for program coding and results repeatability.

Literature review methodology limitations include not keeping in phase with the most updated field knowledge. This limitation is offset by choosing the range for literature review within the last two decades. This literature review may not have captured all published articles because the restriction of database access and search was based only on English. Also, the authors may have omitted fruitful articles hiding in a less popular journal. These limitations are acknowledged. The critical limitation is that the trust, privacy and psychological issues are not addressed in SVBM, which is recognised. However, the benefits of SVBM naturally offset this limitation to being adopted practically.

The theoretical and practical implications include customised and individualistic prediction and preventing most posture-related hazardous behaviours before a critical injury happens. The theoretical implications include mimicking the human pose and lab-based analysis without attaching sensors that naturally alter the working poses. SVBM would help researchers develop more accurate data and theoretical models close to actuals.

By using SVBM, the possibility of early deduction and prevention of musculoskeletal disorders is high; the social implications include the benefits of being a healthier society and health concerned construction sector.

Human pose detection, especially joint angle calculation in a work environment, is crucial to early deduction of muscoloskeletal disorders. Conventional digital technology-based methods to detect pose flaws focus on location information from wearables and laboratory-controlled motion sensors. For the first time, this paper presents novel computer vision (recorded and live videos using mobile and embedded cameras) and digital image-related deep learning methods without attachment to the human body for manual handling pose deduction and analysis of angles, neckline and torso line in an actual construction work environment.

This study investigates the level of artificial intelligence (AI) awareness among library leaders, practitioners and scientists of Indonesian academic libraries to elucidate the benefits of AI implementation and its necessary infrastructure and challenges.

The study adopted a purposive sampling technique to select the 38 participants and thematic analysis to analyze the data, identifying eight themes: understanding of AI, AI adoption, benefits of AI, competencies needed to support AI, facilities to support AI, factors supporting AI adoption, AI-inhibiting factors and expectations of AI.

Different viewpoints provided full awareness among library stakeholders and sufficient information to begin AI initiatives in Indonesian libraries as leaders, practitioners and scientists had a favorable, open and encouraging outlook on AI.

The study does not investigate variations in perspectives between the participants, but it examines their understanding of AI and elaborates the results into the concept of an intelligent library. Moreover, this study only uses samples from academic libraries.

Libraries can take these results into consideration before implementing AI, especially in technology and facilities, librarian competency with regard to AI and leadership roles in AI projects.

Library boards and library associations can use this research as a source to create guidelines about AI implementation in academic libraries.

The study addresses the gap in the research on university libraries' readiness and awareness to implement AI, especially in developing countries.

With the aid of bibliometric analysis, this study aims to show the state-of-the-art of research on the digital divide and identifies new areas for further investigation.

Performance analysis and science mapping were used in the study to analyse a sample of 3,571 studies that were published between 2018 and 2022. The “Title-Keyword-Abstract” search option was used to collect the anticipated publications data from the Scopus database. The gathered data were analysed using the common bibliometric indices to evaluate the research landscape. The science mapping tactics made use of the VOSviewer and Biblioshiny software.

The performance and science mapping analysis shows that recent research on the digital divide has not been sufficiently exposed and examined. The analysis discovered emerging topics, prolific authors and nations, affiliations, a network of collaboration among authors, countries and institutions, bibliographic coupling and keyword co-occurrence.

This work presents a state-of-the-art that has significant theoretical and practical ramifications for the existing digital divide literature. The methodologies and database used in the current study are more extensive.

This study explored the different artificial intelligence (AI) applications used in academic libraries and the key factors and impediments related to their implementation.

The author applied quantitative research methods in the form of a questionnaire, using both open and closed questions. A total of 472 valid questionnaires were received from academic librarians.

The author sought responses from librarians who had implemented AI applications and those who had not, identifying the types of AI applications implemented, key factors relating to their implementation, and impediments to promoting AI. Gaps were identified between the level of support for AI applications and the negative effect of the impediments. Furthermore, the more extensive the individual and organizational knowledge activities performed by the librarians and libraries held, the more positive the attitude was librarians' attitude toward AI applications in their libraries. However, librarians recognized that AI applications are inevitable, but indicated that the difficulties of in execution have hampered the adoption of AI.

The sample data were collected in Taiwan; therefore, the data may only represent the views of Taiwanese academic librarians on AI applications. The results of this study may not apply to librarians worldwide; however, they may provide a useful reference.

The results revealed the top four AI applications that libraries would most likely implement in the near future. Therefore, AI application developers and suppliers can prioritize the promotion of these products for to academic libraries. This study revealed that funding and costs related to AI implementation were discovered to be key factors relating to implementing AI applications. Some impediments to the implementation of AI applications relate to technological problems. Several librarians suggested that managers should invest more resources at an early stage rather than reducing cutting back on human resources initially. Although worries regarding privacy and ethics were mentioned expressed by some respondents, most academic librarians did not regard these to be major concerns.

This study provides the perspectives of librarians who have implemented AI applications and of those who have not. In addition, it explores the advantages and disadvantages of AI applications, and the level of support for and impact of AI applications and promotions. This study also included a gap analysis. Moreover, individual and organizational knowledge activity scales were adopted to examine AI awareness and the perceptions of academic librarians.

This study aims to conduct an exploratory research to find out the evolving constructs and variables of digital literacy, as seen by researchers since its inception. This research also includes an empirical study to identify and further analyze the digital literacy dimensions of university students studying fashion design program in Kolkata, India.

The exploratory study is based on a review of extant literature, whereas the empirical study is carried out through a self-assessment survey based on UNESCOs Digital Literacy Global Framework competences after validating their relevance with respect to the fashion and apparel industry. A total of 120 university students studying four years Bachelor of Design (Fashion Design) program were asked to rate their digital literacy competences on a five-point Likert scale, with a self-reported truth response against each statement. The results were analyzed using multivariate statistical tools.

Based on UNESCO competences, it came out that there are eight digital literacy dimensions. ANOVA further confirms that the dimensions requiring higher-order cognition, such as “software management competence” and “digital citizenship competence,” increase with progress in the graduate program. However, lower-order competence dimensions remained unchanged over time.

The research instrument used for this empirical study, its identified dimensions and the fact that higher-order competence dimensions are enhanced with progression in university education may be helpful for similar research in other fashion-related programs.

Following the recent rise in generative artificial intelligence (GenAI) tools, fundamental questions about their wider impacts have started to reverberate around various disciplines. This study aims to track the unfolding landscape of general issues surrounding GenAI tools and to elucidate the specific opportunities and limitations of these tools as part of the technology-assisted enhancement of mechanical engineering education and professional practices.

As part of the investigation, the authors conduct and present a brief scientometric analysis of recently published studies to unravel the emerging trend on the subject matter. Furthermore, experimentation was done with selected GenAI tools (Bard, ChatGPT, DALL.E and 3DGPT) for mechanical engineering-related tasks.

The study identified several pedagogical and professional opportunities and guidelines for deploying GenAI tools in mechanical engineering. Besides, the study highlights some pitfalls of GenAI tools for analytical reasoning tasks (e.g., subtle errors in computation involving unit conversions) and sketching/image generation tasks (e.g., poor demonstration of symmetry).

To the best of the authors’ knowledge, this study presents the first thorough assessment of the potential of GenAI from the lens of the mechanical engineering field. Combining scientometric analysis, experimentation and pedagogical insights, the study provides a unique focus on the implications of GenAI tools for material selection/discovery in product design, manufacturing troubleshooting, technical documentation and product positioning, among others.

The use of improvement tools in the construction sector has shown to be an important determinant of quality. Companies endeavoring to enhance their daily practices require assistance, evidence, standards, frameworks and quantitative models from existing experts to help them set out for the road. This paper is aimed to assist construction managers in the selection of tools to increase customer satisfaction.

This piece of research is based on the results of a previous empirical study on the use, within a sample of Mexican firms, of a set of more than 30 tools. It then proposes a Bayesian network (BN) to select them. By analyzing the variables under study, it is possible to establish their interaction and dependencies. The resultant BN comprises 24 nodes, and it is useful for choosing some tools that help to increase customer satisfaction.

Customers and their needs now have become more complicated and harder to meet than in the past. Then, the use of improvement tools that put quality at the heart of the management strategies is crucial for achieving customer satisfaction. In order to reduce prices, keep product quality and meet delivery times, these tools should be used on a daily basis. Along this line of thought, the overall results from the hypothetical scenarios explored in this were positive, reflecting the relevance of the proposed model. In particular, the use of tools for gathering customer needs, the utilization of technology and the implementation of a quality department are relevant for increasing customer satisfaction in the sector.

The sample size could be further expanded. The customer satisfaction dimensions could be enhanced.

While the sample in which the investigation is based could be expanded along with the number of variables and their states, the BN can help practitioners in the global construction industry to improve their quality practices, to foster loyalty and to grow revenues.

Most of the research reported in the area of continuous improvement in construction focuses on qualitative considerations, and it is still scarce in terms of developing mathematical models for selecting existing tools and, ultimately, satisfying customer’s requirements. This investigation is aimed to bridge this gap in the literature.

This study aims to analyze the university environment’s role in the intention–action gap (IAG)of highly successful startup founders in an emerging market.

Using multiple regression analysis, this study analyzed data collected from 314 founders representing 99 successful startups (289 valid observations), renowned for their high funding and value operating in an emerging market, Brazil.

The results demonstrate that extracurricular activities and exchange programs lead to a reduced IAG while living in a significant economic center extends it. Computer science and industrial engineering students show reduced IAGs. Studying together with future co-founders also leads to reduced gaps.

The study contributes to the microfoundations theory by presenting new interactions between students and the university environment that influence entrepreneurial action. Limitations are related to the sample, limited to Brazilian founders and selected only through venture capital firms’ filters.

This study also provides practical insights to the universities’ leaders on how they can create programs that improve the rate of startup creation, potentially leading to successful companies.

This study investigates the association between the university role and the entrepreneur’s IAG in emerging markets. The entrepreneur’s IAG is still a relatively new phenomenon explored in entrepreneurship. Even less understanding and limited empirical data exist on successful startups from emerging markets. This study drew on the microfoundations literature to answer how universities in emerging markets could address specific resources and entrepreneurship programs to reduce the IAG among students and alumni.