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This study aims to investigate Sri Lankan Government university students’ acceptance of Chat Generative Pretrained Transformer (ChatGPT) for educational purposes. Using the unified theory of acceptance and use of technology 2 (UTAUT2) model as the primary theoretical lens, this study incorporated personal innovativeness as both a dependent and moderating variable to understand students’ ChatGPT use behaviour.

This quantitative study used a questionnaire survey to collect data. A total of 500 legitimate undergraduates from 17 government universities in Sri Lanka were selected for this study. Items for the variables were adopted from previously validated instruments. Partial least squares structural equation modelling (PLS-SEM) using SmartPLS 4 was used to investigate latent constructs’ relationships. Furthermore, the variables’ relative relevance was ranked using a two-stage artificial neural network analysis with the SPSS 27 application.

The results of the analysis revealed that eight of the nine proposed hypotheses were confirmed. The most significant determinants of behavioural intention were habit and performance expectancy, closely followed by hedonic motivation and perceived ease of use. Use behaviour was highly influenced by both behavioural intention and personal inventiveness. Though personal innovativeness (PI) was suggested as a moderator, the relationship was not significant.

The research highlights the impact of habit, performance expectancy and perceived ease of use on students’ acceptance of AI applications such as ChatGPT, emphasising the need for efficient implementation techniques, individual variations in technology adoption and continuous support and training to improve students’ proficiency.

This study enhances the comprehension of how undergraduate students adopt ChatGPT in an educational setting. The study emphasises the significance of certain variables in the UTAUT2 model and the importance of PI in influencing the adoption of ChatGPT in educational environments.

This study aims to investigate the influence of ChatGPT, an AI-based chatbot, on the digital learning experience of students at Mzumbe University.

This study adopted a qualitative research design to gather in-depth insights from participants. Semi-structured interviews and an analysis of previous chat content were used as primary sources of data. Thematic analysis was used to analyze the qualitative data, allowing for the exploration of participants’ perspectives, experiences and opinions regarding the integration of ChatGPT into the learning process.

The results of the study demonstrated that ChatGPT is widely used in educational contexts and has a positive influence on students’ study habits, academic performance, and understanding of course material. Students appreciated the system’s simplicity, tailored instructions, and the promptness and accuracy of the responses. Despite the possibility of isolated mistakes.

It is important to recognize the limitations of this study. First, the sample size was small, limiting the broad application of the results. Second, this study’s narrow emphasis on students at Mzumbe University limits its applicability in other situations. Furthermore, depending on self-reported experiences, biases, such as individual interpretation or recollection bias, can occur.

Educators can maximize ChatGPT in the classroom by using study insights. Its advantages, such as effectiveness and enhanced performance, highlight the possibility for student-centered learning. Practitioners are guided by their awareness of problems, such as probable errors. Constant updates guarantee ChatGPT’s applicability and provide educators with useful advice.

Peer impact is highlighted in this study concerning social factors on the adoption of AI in education. Resolving issues preserves public confidence. Views influence public opinion and direct policymakers in discussions about safe AI use. It influences public attitudes while navigating the ethical integration of AI.

This study offers insightful information about the impact of ChatGPT on digital learning in Tanzania’s higher education. It makes innovative research contributions that enhance educational practices and emphasizes the advantages, difficulties and demands of responsible usage in the context of AI-based chatbots.

This study investigates the user’s perspective regarding opportunities, challenges and implications related to artificial intelligence technologies such as ChatGPT in several contexts, such as education and healthcare. In detail, the aim was to understand user’ perceptions of the relationship between ChatGPT and sustainability and their views about how companies can contribute to achieving well-being.

To reach the research aim, the study adopts an explorative inductive design, carried out through a qualitative approach and grounded in 34 in-depth consumer interviews.

The interviewees perceive ChatGPT as a helpful tool that simplifies activities and reduces time and human errors. However, risks associated with using this tool have been identified, such as the reliability of the information provided, the need for more privacy for the requested personal data and the lack of empathy and human interaction. AI tools can support businesses and governments in creating and promoting sustainable strategies and reducing waste. Furthermore, the study shows how these tools can improve the community's well-being by disseminating knowledge and information, supporting workers and generating less stress.

The paper describes the users' perspective on the opportunities of ChatGPT in promoting sustainable development and well-being, providing suggestions for future research.

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.

This study aims to review the results of relevant studies to shed light on social trust-building in different contexts and the factors that affect it in disaster risk management.

This systematic review was conducted using the Preferred Reporting Items for Systematic reviews and Meta-Analyses model. The study keywords were searched for in PubMed, Scopus and Web of Science databases on August 2021. The inclusion criteria were English-written articles published in social trust and disaster relief efforts. Exclusion criteria were lack of access to the full text and article types such as nonoriginal articles.

Out of 1,359 articles found, 17 articles were included in the final analysis using four general categories: six articles on the role of local government in trust-building (local governments), five articles on the role of social media in trust-building (social media), four articles on the role of social capital in trust-building (social capital) and two articles on the importance of community participation in trust-building (community participation).

Understanding the role of social trust and the factors which influence it will help the development of community-based disaster risk management. Therefore, disaster management organizations and other relief agencies should take the findings of this study into account, as they can help guide policymaking and the adoption of strategies to improve public trust and participation in comprehensive disaster risk management. Further studies recommended understanding people’s experiences and perceptions of social trust, relief and disaster preparedness.

There has been an unprecedented increase in online learning worldwide, including in teacher education. However, student lurking can be a common issue, leading to a non-interactive learning environment.

The authors employed a qualitative case study with thematic analysis to examine a novel “self-directed” pre-service teacher online degree module that engaged students in regular peer-feedback, which intended to promote student engagement and interactivity. The research questions were as follows: To what extent did the seminar series represent the principles of self-directed learning and were learning outcomes effective from the process? And, how effective was the use of peer feedback?

The thematic analysis revealed that student progression and course completion was successful, and it represented some principles of self-directed learning; but (a) it cannot be presumed that pre-service teachers are competent in giving (peer) feedback and (b) pre-service teachers may need specific guidance and training for providing competent feedback.

This paper is highly original in respect of its combination of the self-directed learning framework with use of peer feedback, to engage students in an interactive learning environment. The present paper identifies that peer feedback is a powerful tool in online learning; peer feedback can supplement self- and teacher-assessment; but it should not be assumed that pre-service teachers are competent in providing (peer) feedback – pre-service teachers may need specific training in providing feedback.

This paper aims to examine the impact of the COVID-19 pandemic on the private label food products, specifically assessing the stability and changes in factors influencing purchasing decisions, and comparing pre-pandemic and post-pandemic datasets.

The study employs the Extra Tree Classifier method, a robust quantitative approach, to analyse data collected from questionnaires distributed among two distinct consumer samples. This methodological choice is explicitly adopted to provide a clear classification of factors influencing consumer preferences for private label products, surpassing conventional qualitative methods.

Despite the profound disruptions caused by the COVID-19 pandemic, this research underscores the persistent hierarchy of factors shaping consumer choices in the private label food market, showing an overall stability in consumer behaviour. At the same time, the analysis of individual variables highlights the positive increase in those related to product quality, health, taste, and communication.

The use of online surveys for data collection may introduce a self-selection bias, and the non-probabilistic sampling method could limit the generalizability of the results.

Practical implications suggest that managers in the private label industry should prioritize enhancing quality control, ensuring effective communication, and dynamically adapting strategies to meet evolving consumer preferences, with a particular emphasis on quality and health attributes.

This study contributes to the existing body of literature by providing insights into the profound transformations induced by the COVID-19 pandemic on consumer behaviour, specifically in relation to their preferences for private label food products.

This paper examines the evolution of massive open online courses (MOOCs) literature over the past 15 years and identifies its significant developments.

Utilizing main path analysis (MPA) on a dataset of 1,613 articles from the Web of Science (WoS) databases, the authors construct the main pathway in MOOC literature through a citation analysis. Pajek software is used to visualize the 34 influential articles identified in the field.

Three phases emerge in MOOC research: connectivism as a learning theory, facilitating education reform and breaking barriers to MOOCs adoption. Multiple-Global MPA highlights sub-themes including self-regulated learning (SRL), motivation, engagement, dropouts, student performance and the impact of COVID-19.

First, data limitations from the WoS core collection might not cover all research, but using reputable sources enhances data validity. Second, despite careful algorithm selection to enhance accuracy, there remains a limitation inherent in the nature of citations. Such biased citations may result in findings that do not fully align with scholars' perspectives.

The authors' findings contribute to the understanding of MOOCs literature development, enabling educators and researchers to grasp key trends and focus areas in the field. It can inform the design and implementation of MOOCs for more effective educational outcomes.

This study presents novel methodologies and important findings for advancing research and practice in MOOCs.

Due to the growing complexity involved in leveraging the endless possibilities of ICT on all levels, the technical competence of faculties of higher education institutions (HEI) and effective methods for fostering e-readiness has become questionable.

This research has developed and validated an empirically supported e-readiness scale, which can be used by HEIs to assess faculty members’ preparedness toward online teaching. The measurement model and the structural model were developed as the results of exploratory factor analysis and confirmatory factor analysis (n = 245). The previously identified components and their indicators were validated using the structural models and the final scale was developed with five dimensions (“online technological readiness, pedagogical readiness, institutional readiness, learning and delivery readiness and content readiness”).

The faculties’ e-readiness assessment tool, as a useful tool, could aid institutions in identifying problems that affect the implementation of e-learning or digitalization in the institutions and developing strategies in response.

Like any research this research also has some limitations and can be considered as future research probability like the responses for this research were collected from HEI in India; however, a cross-cultural study can be conducted to understand the parameters across the globe. Although the psychometric qualities of the e-readiness scale are acceptable, additional research in various higher educational environments, both nationally and internationally, is required to further establish the scale’s relevance, validation and generalizability.

Although many scales have been developed to assess the readiness level in the education sector, a scale, that holistically measures, the readiness level of faculties from an overall perspective was required. This scale can be used to recognize the e-readiness level of teachers in HEIs. This scale can also help the institutions assess the readiness level of their faculty members and address any improvements required in their teaching and learning pedagogy, further acknowledging training needs.

This study’s main objective is to determine the extent to which social capital and psychological capital can explain differences in career adaptability among higher education students.

The study employed a quantitative approach, utilizing a survey research design. Data were gathered using an online questionnaire completed by 384 fourth-year undergraduate agricultural students in Iran. The inverse square root and multistage sampling methods were used to determine the sample size. The partial least squares-structural equation modelling (PLS-SEM) method examined the associations between latent variables.

The results suggest that social and psychological capital significantly influence the career adaptability of agricultural students, highlighting their significance in enhancing career adaptability. Moreover, psychological capital positively mediates the relationship between social capital and career adaptability.

Agricultural higher education institutions must focus on developing students' social and psychological capital to cultivate career adaptability in agricultural students. Agricultural higher education institutions, for example, should help students develop soft skills.

This study offers novel insights into the significance of individual resources, such as social and psychological capital, in enhancing the career adaptability of students. In addition, the key contribution of this study is the researchers' empirical evidence that multiple career resources are interconnected (social capital, career adaptability, and psychological capital).