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Article
Publication date: 27 March 2024

Jyoti Mudkanna Gavhane and Reena Pagare

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

Abstract

Purpose

The purpose of this study was to analyze importance of artificial intelligence (AI) in education and its emphasis on assessment and adversity quotient (AQ).

Design/methodology/approach

The study utilizes a systematic literature review of over 141 journal papers and psychometric tests to evaluate AQ. Thematic analysis of quantitative and qualitative studies explores domains of AI in education.

Findings

Results suggest that assessing the AQ of students with the help of AI techniques is necessary. Education is a vital tool to develop and improve natural intelligence, and this survey presents the discourse use of AI techniques and behavioral strategies in the education sector of the recent era. The study proposes a conceptual framework of AQ with the help of assessment style for higher education undergraduates.

Originality/value

Research on AQ evaluation in the Indian context is still emerging, presenting a potential avenue for future research. Investigating the relationship between AQ and academic performance among Indian students is a crucial area of research. This can provide insights into the role of AQ in academic motivation, persistence and success in different academic disciplines and levels of education. AQ evaluation offers valuable insights into how individuals deal with and overcome challenges. The findings of this study have implications for higher education institutions to prepare for future challenges and better equip students with necessary skills for success. The papers reviewed related to AI for education opens research opportunities in the field of psychometrics, educational assessment and the evaluation of AQ.

Details

Education + Training, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0040-0912

Keywords

Article
Publication date: 5 March 2024

William Joseph Fassbender

This study builds on previous theoretical work that considered artificial intelligence (AI) and its potential for creating “teacher-centaurs” whose labor could be accelerated…

Abstract

Purpose

This study builds on previous theoretical work that considered artificial intelligence (AI) and its potential for creating “teacher-centaurs” whose labor could be accelerated through the use of generative AI (Fassbender, in review). The purpose of this paper is to use empirical methods to study centaur teachers and the division of labor (Durkheim, 1893/2013) that arise from outsourcing teaching tasks to AI.

Design/methodology/approach

Multiple case study (Stake, 2006) was used to collect data on two secondary English teachers who were early adopters of generative AI. Data included semi-structured interviews as well as ChatGPT chat logs, which helped in describing how teaching approaches evolved using AI technology.

Findings

Results showed that teachers used AI for planning, instruction and assessment. AI-augmented teaching practices allowed teachers to complete tasks with greater speed, which in turn increased stamina and short-term work–life balance. Given the novelty of AI, concerns about data privacy and academic integrity raised ethical questions.

Originality/value

ChatGPT’s rise to popularity in 2023 brought with it significant discussions about education, specifically how students would use AI primarily as a tool for plagiarism. This study takes a different focus, considering how early adoption of AI has begun changing teacher labor, offering implications for the future of the teaching profession.

Article
Publication date: 8 March 2024

Agostino Marengo, Alessandro Pagano, Jenny Pange and Kamal Ahmed Soomro

This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published…

Abstract

Purpose

This paper aims to consolidate empirical studies between 2013 and 2022 to investigate the impact of artificial intelligence (AI) in higher education. It aims to examine published research characteristics and provide insights into the promises and challenges of AI integration in academia.

Design/methodology/approach

A systematic literature review was conducted, encompassing 44 empirical studies published as peer-reviewed journal papers. The review focused on identifying trends, categorizing research types and analysing the evidence-based applications of AI in higher education.

Findings

The review indicates a recent surge in publications concerning AI in higher education. However, a significant proportion of these publications primarily propose theoretical and conceptual AI interventions. Areas with empirical evidence supporting AI applications in academia are delineated.

Research limitations/implications

The prevalence of theoretical proposals may limit generalizability. Further research is encouraged to validate and expand upon the identified empirical applications of AI in higher education.

Practical implications

This review outlines imperative implications for future research and the implementation of evidence-based AI interventions in higher education, facilitating informed decision-making for academia and stakeholders.

Originality/value

This paper contributes a comprehensive synthesis of empirical studies, highlighting the evolving landscape of AI integration in higher education and emphasizing the need for evidence-based approaches.

Details

Interactive Technology and Smart Education, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1741-5659

Keywords

Open Access
Article
Publication date: 18 December 2023

Francesca Ferrè

Value-based healthcare suggested using patient-reported information to complement the information available in the medical records and administrative healthcare data to provide…

Abstract

Purpose

Value-based healthcare suggested using patient-reported information to complement the information available in the medical records and administrative healthcare data to provide insights into patients' perceptions of satisfaction, experience and self-reported outcomes. However, little attention has been devoted to questions about factors fostering the use of patient-reported information to create value at the system level.

Design/methodology/approach

Action research design is carried out to elicit possible triggers using the case of patient-reported experience and outcome data for breast cancer women along their clinical pathway in the clinical breast network of Tuscany (Italy).

Findings

The case shows that communication and engagement of multi-stakeholder representation are needed for making information actionable in a multi-level, multispecialty care pathway organized in a clinical network; moreover, political and managerial support from higher level governance is a stimulus for legitimizing the use for quality improvement. At the organizational level, an external facilitator disclosing and discussing real-world uses of collected data is a trigger to link measures to action. Also, clinical champion(s) and clear goals are key success factors. Nonetheless, resource munificent and dedicated information support tools together with education and learning routines are enabling factors.

Originality/value

Current literature focuses on key factors that impact performance information use often considering unidimensional performance and internal sources of information. The use of patient/user-reported information is not yet well-studied especially in supporting quality improvement in multi-stakeholder governance. The work appears relevant for the implications it carries, especially for policymakers and public sector managers when confronting the gap in patient-reported measures for quality improvement.

Details

The TQM Journal, vol. 36 no. 9
Type: Research Article
ISSN: 1754-2731

Keywords

Article
Publication date: 2 February 2023

Lai-Wan Wong, Garry Wei-Han Tan, Keng-Boon Ooi and Yogesh Dwivedi

The deployment of artificial intelligence (AI) technologies in travel and tourism has received much attention in the wake of the pandemic. While societal adoption of AI has…

1144

Abstract

Purpose

The deployment of artificial intelligence (AI) technologies in travel and tourism has received much attention in the wake of the pandemic. While societal adoption of AI has accelerated, it also raises some trust challenges. Literature on trust in AI is scant, especially regarding the vulnerabilities faced by different stakeholders to inform policy and practice. This work proposes a framework to understand the use of AI technologies from the perspectives of institutional and the self to understand the formation of trust in the mandated use of AI-based technologies in travelers.

Design/methodology/approach

An empirical investigation using partial least squares-structural equation modeling was employed on responses from 209 users. This paper considered factors related to the self (perceptions of self-threat, privacy empowerment, trust propensity) and institution (regulatory protection, corporate privacy responsibility) to understand the formation of trust in AI use for travelers.

Findings

Results showed that self-threat, trust propensity and regulatory protection influence trust in users on AI use. Privacy empowerment and corporate responsibility do not.

Originality/value

Insights from the past studies on AI in travel and tourism are limited. This study advances current literature on affordance and reactance theories to provide a better understanding of what makes travelers trust the mandated use of AI technologies. This work also demonstrates the paradoxical effects of self and institution on technologies and their relationship to trust. For practice, this study offers insights for enhancing adoption via developing trust.

Details

Internet Research, vol. 34 no. 2
Type: Research Article
ISSN: 1066-2243

Keywords

Article
Publication date: 22 March 2024

Rongxin Chen and Tianxing Zhang

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation…

Abstract

Purpose

In the global context, artificial intelligence (AI) technology and environmental, social and governance (ESG) have emerged as central drivers facilitating corporate transformation and the business model revolution. This paper aims to investigate whether and how the application of AI enhances the ESG performance of enterprises.

Design/methodology/approach

This study uses panel data from Chinese A-share listed companies spanning the period from 2012 to 2022. Through a multivariate regression analysis, it examines the impact of AI on the ESG performance of enterprises.

Findings

The findings suggest that the application of AI in enterprises has a positive impact on ESG performance. Internal control systems within the organization and external information environments act as mediators in the relationship between AI and corporate ESG performance. Furthermore, corporate compliance plays a moderating role in the connection between AI and corporate ESG performance.

Originality/value

This paper underscores the pivotal role played by AI in enhancing corporate ESG performance. It explores the pathways to improving corporate ESG behavior from the perspectives of internal control and information environments. This discussion holds significant implications for advancing the application of AI in enterprises and enhancing their sustainable governance capabilities.

Details

Chinese Management Studies, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 1750-614X

Keywords

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