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This paper aims to report the findings of a study into the automated text analysis of student feedback comments to assist in investigating a high volume of qualitative information at various levels in an Australian university. It includes the drawbacks and advantages of using selected applications and established lexicons. There has been an emphasis on the analysis of the statistical data collected using student surveys of learning and teaching, while the qualitative comments provided by students are often not systematically scrutinised. Student comments are important, as they provide a level of detail and insight that are imperative to quality assurance practices.

The paper outlines the process by which the institution researched, developed and implemented the automated analysis of student qualitative comments in surveys of units and teaching.

The findings indicated that there are great benefits in implementing this automated process, particularly in the analysis of evaluation data for units with large enrolments. The analysis improved efficiency in the interpretation of student comments. However, a degree of human intervention is still required in creating reports that are meaningful and relevant to the context.

This paper is unique in its examination of one institution’s journey in developing a process to support academics staff in interpreting and understanding student comments provided in surveys of units and teaching.

The paper aims to disseminate solutions to common problems in student evaluation processes. It proposes that student evaluation can be applied to quality assurance and improving learning and teaching. The paper presents solutions in the areas of: presenting outcomes as performance indicators, constructing appropriate surveys, improving response rates, reporting student feedback to students and student engagement as a feature of university quality assurance.

The research approach of this paper is comparative case study, allowing in-depth exploration of multiple perspectives and practices at seven Australian universities. Process and outcome data were rigorously collected, analysed, compared and contrasted.

The paper provides empirical evidence for student evaluation as an instrument of learning and teaching data analysis for quality improvement. It suggests that collecting data about student engagement and the student experience will yield more useful data about student learning. Furthermore, findings indicate that students benefit from more authentic inclusion in the evaluation process and outcomes.

Because of the chosen research approach, the research results may lack generalisability. Therefore, researchers are encouraged to test the proposed propositions further and apply to their own university contexts.

The paper includes recommendations at the institution- and sector-wide levels to effectively use student evaluation as a university performance indicator and as a tool of change.

This paper fulfils an identified need to examine student evaluation processes across institutions and focuses on the role of student evaluation in quality assurance.

The primary purpose of the study was to explore the impact of health workers’ awareness of artificial intelligence (AI) on their workplace well-being, addressing a critical gap in the literature. By examining this relationship through the lens of the Job demands-resources (JD–R) model, the study aimed to provide insights into how health workers’ perceptions of AI integration in their jobs and careers could influence their informal learning behaviour and, consequently, their overall well-being in the workplace. The study’s findings could inform strategies for supporting healthcare workers during technological transformations.

The study employed a quantitative research design using a survey methodology to collect data from 420 health workers across 10 hospitals in Ghana that have adopted AI technologies. The study was analysed using OLS and structural equation modelling.

The study findings revealed that health workers’ AI awareness positively impacts their informal learning behaviour at the workplace. Again, informal learning behaviour positively impacts health workers’ workplace well-being. Moreover, informal learning behaviour mediates the relationship between health workers’ AI awareness and workplace wellbeing. Furthermore, employee learning orientation was found to strengthen the effect of AI awareness on informal learning behaviour.

While the study provides valuable insights, it is important to acknowledge its limitations. The study was conducted in a specific context (Ghanaian hospitals adopting AI), which may limit the generalizability of the findings to other healthcare settings or industries. Self-reported data from the questionnaires may be subject to response biases, and the study did not account for potential confounding factors that could influence the relationships between the variables.

The study offers practical implications for healthcare organizations navigating the digital transformation era. By understanding the positive impact of health workers’ AI awareness on their informal learning behaviour and well-being, organizations can prioritize initiatives that foster a learning-oriented culture and provide opportunities for informal learning. This could include implementing mentorship programs, encouraging knowledge-sharing among employees and offering training and development resources to help workers adapt to AI-driven changes. Additionally, the findings highlight the importance of promoting employee learning orientation, which can enhance the effectiveness of such initiatives.

The study contributes to the existing literature by addressing a relatively unexplored area – the impact of AI awareness on healthcare workers’ well-being. While previous research has focused on the potential job displacement effects of AI, this study takes a unique perspective by examining how health workers’ perceptions of AI integration can shape their informal learning behaviour and, subsequently, their workplace well-being. By drawing on the JD–R model and incorporating employee learning orientation as a moderator, the study offers a novel theoretical framework for understanding the implications of AI adoption in healthcare organizations.

This study aims to examine the association between workplace spirituality (WPS) and individual’s work engagement (WE) and also the mediating effect of individual’s psychological conditions [psychological meaningfulness (PSYM), psychological safety (PSYS) and psychological availability (PSYA)] on this relationship.

Grounded on Kahn’s personal engagement theory, a model has been developed with WPS as an independent variable, individual’s psychological conditions (PSYM, PSYS and PSYA) as the mediators and individual’s WE as the dependent variable. Based on the online responses from 510 millennial employees working in Fortune 500 manufacturing and service industries operating in India, analysis has been undertaken using confirmatory factor analysis, Pearson correlation and PROCESS macro of Hayes (2017).

WPS has been noted to influence individual’s WE positively and there is a partial mediation of PSYM, PSYS and PSYA on this relationship.

The present study is able to extend the scope of Kahn’s personal engagement theory.

Leaders and HR administrators can use the framework to ensure positive engagement levels for the millennial workforce. It will also help to reduce job dissatisfaction and burnouts at the workplace.

The present study contributes to understanding WE through the lens of WPS. It adds to the existing knowledge by explaining the mediation of the psychological conditions between spirituality and WE among millennials working in India. To the best of the authors’ knowledge, this study can be considered one of the first studies that has attempted to understand the role of WPS and psychological conditions on WE levels of millennials.

This systematic literature review aims to investigate the application and integration of Industry 4.0 (I4.0) technologies in transportation infrastructure construction projects focusing on sustainability pillars.

The study employs a systematic literature review approach, combining qualitative review and quantitative analysis of 142 academic articles published between 2011 and March 2023.

The findings reveal the dominance of Building Information Modelling (BIM) as a central tool for sustainability assessment, while other technologies such as blockchain and autonomous robotics have received limited attention. The adoption of I4.0 technologies, including Internet of Things (IoT) sensors, Augmented Reality (AR), and Big Data, has been prevalent for data-driven analyses, while Unmanned Aerial Vehicle (UAVs) and 3D printing are mainly being integrated either with BIM or in synergy with Artificial Intelligence (AI). We pinpoint critical challenges including high adoption costs, technical barriers, lack of interoperability, and the absence of standardized sustainability benchmarks.

This research distinguishes itself by not only mapping the current integration of I4.0 technologies but also by advocating for standardization and a synergistic human-technology collaborative approach. It offers tailored strategic pathways for diverse types of transportation infrastructure and different project phases, aiming to significantly enhance operational efficiency and sustainability. The study sets a new agenda for leveraging cutting-edge technologies to meet ambitious future sustainability and efficiency goals, making a compelling case for rethinking how these technologies are applied in the construction sector.