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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

Open Access
Article
Publication date: 9 February 2024

Mohsen Rafiei and Hans Van Dijk

Early research on overqualification suggested that overqualification is primarily associated with negative attitudes and behavior. As a consequence, hiring practitioners were…

Abstract

Purpose

Early research on overqualification suggested that overqualification is primarily associated with negative attitudes and behavior. As a consequence, hiring practitioners were advised against hiring overqualified job applicants. However, recent studies have revealed that there are several potential positive consequences of overqualification. Given this change in perspective on overqualification, we examine how hiring practitioners nowadays look at overqualified job applicants, and what their considerations are for hiring an overqualified job applicant or not.

Design/methodology/approach

We have interviewed 33 hiring practitioners to examine their attitudes and considerations toward hiring overqualified job applicants.

Findings

Results show that hiring practitioners are aware of potential positive as well as negative consequences of overqualification and consider a variety of factors to assess how beneficial hiring an overqualified candidate will be. These factors fall under three categories: Individual considerations, interpersonal considerations and contextual considerations.

Originality/value

We show that overqualification is not a stigma anymore and that the decision to hire an overqualified job applicant or not depends on a mixture of factors that are carefully considered. Two of these three considerations transcend the individual level (i.e. the overqualified person), whereas most research and theories on the consequences of overqualification do not go beyond the individual level. As such, our findings call for more theory and research on interpersonal and contextual factors shaping the consequences of overqualification.

Details

Personnel Review, vol. ahead-of-print no. ahead-of-print
Type: Research Article
ISSN: 0048-3486

Keywords

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