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The purpose of this paper is to share findings from empirically driven conceptual research into the implications for English teachers of understanding generative AI as a “tool” for writing.

The paper reports early findings from an Australian National Survey of English teachers and interrogates the notion of the AI writer as “tool” through intersectional feminist discursive-material analysis of the metaphorical entailments of the term.

Through this work, the authors have developed the concept of “coloniser tool-thinking” and juxtaposed it with First Nations and feminist understandings of “tools” and “objects” to demonstrate risks to the pursuit of social and planetary justice through understanding generative AI as a tool for English teachers and students.

Bringing together white and First Nations English researchers in dialogue, the paper contributes a unique perspective to challenge widespread and common-sense use of “tool” for generative AI services.

The purpose of this paper is to understand motivation of young consumers to use artificial intelligence (AI) tools such as chatbots, voice assistants and augmented reality in shopping by generating Vroom’s expectancy theory of motivation using grounded theory approach.

Grounded theory approach has been used to develop the Vroom’s expectancy theory. Initially data were collected through participant interviews using theoretical sampling. These data were analyzed and coded using the three step process, i.e. open coding, axial coding and selective coding. The categories created during coding were integrated to generate Vroom’s expectancy theory of motivation.

The findings indicate that Vroom’s expectancy theory of motivation can be used to explain motivation of young consumers to use AI tools as an aid in taking shopping decisions. The motivation may be intrinsic motivation, extrinsic motivation or force choice motivation. Expectancy represents the ease of using the tools, instrumentality represents competence of tools in performing desired tasks while valence represents satisfaction, rewarding experience and trust in using of tools.

The findings of the study are based on grounded theory approach which is an inductive approach. Alternate research methodologies, both inductive and deductive, need to be employed to strengthen the external validity and generalize the results. The study is limited to shopping motives of young consumers in India. A comparison with other consumer motivational studies has not been done. Hence no claim is made regarding the advantage of Vroom’s theory over other motivational theories.

The study has strong implications for retailers in developing countries which are seen as an emerging market for retail and have introduced AI tools in recent years. The Vroom’s expectancy theory will help retailers to understand consumer motivation in using AI tools or shopping.

Vroom’s expectancy theory to understand consumer motivation to use AI tools in shopping was generated using the grounded theory approach.

This paper aims to develop an artificial intelligence (AI) audit tool for auditing text-based evidence and determine its efficiency and effectiveness.

A manual audit checklist and an AI audit tool are developed with fuzzy front-end (FFE) from Innovation Management System Standard (IMSS) as the audit scope, First, a manual audit of five organisations is conducted to determine their compliance scores. The transcripts of the audit are recorded which are used by the AI audit tool to assign compliance scores for the same organisations. The effectiveness and efficiency of the AI audit tool are determined by comparing their results with the manual audit.

This paper demonstrates the development of the FFE AI audit tool which led to 92% improved efficiency while being 95% effective compared to a human auditor.

The publication of new financial and non-financial standards (such as ISO56002: IMSS) have implications for internal auditing (IA). The scope of IA must expand to include new standards while remaining efficient. Emerging technologies, such as AI help achieve this. Even though the use of AI in financial auditing is widely studied, it has not received similar attention in non-financial auditing. This paper develops a non-financial AI audit tool to audit an essential component of the IMSS, the FFE of innovation and determine its efficiency and effectiveness.

The study develops an FFE AI audit tool for the first time. The methodology used has practical and academic implications for the use of AI in non-financial auditing.

This article reviews the quantitative marketing literature on artificial intelligence (AI) through an economics lens. We apply the framework in Prediction Machines: The Simple Economics of Artificial Intelligence to systematically categorize 96 research papers on AI in marketing academia into five levels of impact, which are prediction, decision, tool, strategy, and society. For each paper, we further identify each individual component of a task, the research question, the AI model used, and the broad decision type. Overall, we find there are fewer marketing papers focusing on strategy and society, and accordingly, we discuss future research opportunities in those areas.

The purpose of this article is to analyze the existing studies on the application of artificial intelligence (AI) in lean construction management (LCM). Further, this study offers a classification scheme that specifies different categories of AI tools, as applied to the field of LCM to support various principles of LCM.

This research adopts the systematic literature review (SLR) process, which consists of five consecutive steps: planning, searching, screening, extraction and synthesis and reporting. As a supplement to SLR, a bibliometric analysis is performed to examine the quantity and citation impact of the reviewed papers.

In this paper, seven key areas related to the principles of LCM for which AI tools have been used are identified. The findings of this research clarify how AI can assist in bolstering the practice of LCM. Further, this article presents directions for the future evolution of AI tools in LCM based on the current emerging trends.

This paper advances the LCM systems by offering a lens through which construction managers can better understand key concepts in the linkage of AI to LCM.

This research offers a new classification scheme that allows researchers to properly recall, identify and group various applications of AI categories in the construction industry based on various principles of LCM. In addition, this study provides a source of references for researchers in the LCM discipline, which advances knowledge and facilitates theory development in the field.

Recent United Nations Climate Change Conferences recognise extreme climate change of heatwaves, floods and droughts as threatening risks to the resilience and success of public–private partnership (PPP) infrastructure projects. Such conferences together with available project reports and empirical studies recommend project managers and practitioners to adopt smart technologies and develop robust measures to tackle climate risk exposure. Comparatively, artificial intelligence (AI) risk management tools are better to mitigate climate risk, but it has been inadequately explored in the PPP sector. Thus, this study aims to explore the tools and roles of AI in climate risk management of PPP infrastructure projects.

Systematically, this study compiles and analyses 36 peer-reviewed journal articles sourced from Scopus, Web of Science, Google Scholar and PubMed.

The results demonstrate deep learning, building information modelling, robotic automations, remote sensors and fuzzy logic as major key AI-based risk models (tools) for PPP infrastructures. The roles of AI in climate risk management of PPPs include risk detection, analysis, controls and prediction.

For researchers, the findings provide relevant guide for further investigations into AI and climate risks within the PPP research domain.

This article highlights the AI tools in mitigating climate crisis in PPP infrastructure management.

This article provides strong arguments for the utilisation of AI in understanding and managing numerous challenges related to climate change in PPP infrastructure projects.

This chapter provides preliminary evidence to show that financial accounting students would use generative artificial intelligence (AI) tools to improve their learning if given the opportunity to do so by their instructors. Most students who completed the exercises we used in the study did so diligently and modified their answers after using a generative AI tool in a manner that suggests beneficial effects. It appears that the more prior knowledge a student had about the subject matter, the more beneficial was the experience. Pitfalls still exist, however. For example, students without knowledge of the subject matter struggled with crafting queries and judging the efficacy of their answers. Moreover, although a minority, some students tended to duplicate their original answers without utilizing the responses generated by the generative AI tool. Additionally, certain students merely copied the answers generated by the AI tool without providing any additional critique or analysis. Implications for teaching and learning and opportunities for future research are discussed.

The purpose of this study is to identify the relationship between artificial intelligence (AI) and fake news detection. It also intended to explore the negative effects of fake news on society and to find out trending techniques for fake news detection.

“Preferred Reporting Items for the Systematic Review and Meta-Analysis” were applied as a research methodology for conducting the study. Twenty-five peer-reviewed, most relevant core studies were included to carry out a systematic literature review.

Findings illustrated that AI has a strong positive relationship with the detection of fake news. The study displayed that fake news caused emotional problems, threats to important institutions of the state and a bad impact on culture. Results of the study also revealed that big data analytics, fact-checking websites, automatic detection tools and digital literacy proved fruitful in identifying fake news.

The study offers theoretical implications for the researchers to further explore the area of AI in relation to fake news detection. It also provides managerial implications for educationists, IT experts and policymakers. This study is an important benchmark to control the generation and dissemination of fake news on social media platforms.

The purpose of this essay is to illustrate how project management “pull” and AI or analytics technology “push” are likely to result in incremental and disruptive evolution of project management capabilities and practices.

This paper is written as a critical essay reflecting the experience and reflections of the author with many ideas drawn from and extending selected items from project management, artificial intelligence (AI) and analytics literatures.

Neither AI nor sophisticated analytics is likely to elicit hands on attention from project managers, other than those producing AI or analytics-based artifacts or using these tools to create their products and services. However, through the conduit of packaged software support for project management, new tools and approaches can be expected to more effectively support current activities, to streamline or eliminate activities that can be automated, to extend current capabilities with the availability of increased data, computing capacity and mathematically based algorithms and to suggest ways to reconceive how projects are done and whether they are needed.

This essay includes projections of possible, some likely and some unlikely, events and states that have not yet occurred. Although the hope and purpose are to alert readers to the possibilities of what may occur as logical extensions of current states, it is improbable that all such projections will come to pass at all or in the way described. Nonetheless, consideration of the future ranging from current trends, the interplay among intersecting trends and scenarios of future states can sharpen awareness of the effects of current choices regarding actions, decisions and plans improving the probability that the authors can move toward desired rather than undesired future states.

Project managers not involved personally with creating AI or analytics products can avoid mastering detailed skill sets in AI and analytics, but should scan for new software features and affordances that they can use enable new levels of productivity, net benefit creation and ability to sleep well at night.

This essay brings together AI, analytics and project management to imagine and anticipate possible directions for the evolution of the project management domain.