Rethinking Education in the Era of Artificial Intelligence (AI): Towards Future Workforce Competitiveness and Business Success

Purpose This study aims to consolidate existing research on digital technology in business education, focusing on adoption determinants, theoretical frameworks and implementation challenges. Design/methodology/approach A hybrid literature review was conducted. The first phase involved a keyword co-occurrence analysis of 254 Association of Business Schools-ranked journal articles from the Web of Science database to identify key research themes. To deepen the analysis, the second phase included a content analysis of the 24 most globally cited articles, each with a minimum of 65 total global citations. Findings The review reveals that studies primarily focus on factors influencing digital technology adoption, including institutional support, perceived usefulness and ease of use. Researchers frequently cite theoretical models such as the technology acceptance model and the unified theory of acceptance and use of technology. Identified challenges include concerns over technology effectiveness, resistance to change and the influence of cultural and generational differences on technology use. Practical implications The findings guide educators and policymakers in formulating strategies for effective technology adoption, emphasizing alignment with educational goals and addressing cultural barriers. Social implications Understanding generational and cultural impacts enables the creation of inclusive digital education strategies that cater to diverse student needs. Originality/value This study synthesizes fragmented literature, offering new insights into the integration of digital technologies in business education.

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Generative AI in Higher Education: Uncertain Students, Ambiguous Use Cases, and Mercenary Perspectives

Stone

2024

Teaching of Psychology

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Background: Students in higher education are using generative artificial intelligence (AI) despite mixed messages and contradictory policies. Objective: This study helps answer outstanding questions about many aspects of AI in higher education: familiarity, usage, perceptions of peers, ethical/social views, and AI grading. Method: I surveyed 733 undergraduates. Results: Students reported mixed levels of experience with AI and tended toward nervousness over excitement. Most reported professors addressing AI but not integrating it. While 41% of students had used AI in ways explicitly banned, many more students (59%) reported ambiguous use cases. Students overestimated peer cheating, and this predicted their own cheating, as did general experience with and excitement about AI. Meanwhile, 11% of students reported false accusations, with first-generation students possibly at a higher rate. Pragmatic views about career and inequality may be affecting behaviors. Men consistently reported more involvement with AI than women. Conclusion: Future research should focus on the hybrid collaboration of humans and AI and how AI might be leveraged to support and scaffold genuine learning. Teaching Implications: AI will be relevant to many future careers, and students increasingly want it to be part of their education. Academic integrity will be a continuing challenge, and students need transparency.

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Rethinking Education in the Era of Artificial Intelligence (AI): Towards Future Workforce Competitiveness and Business Success

Cited by 5 publications

References 14 publications

Emerging Technologies: Opportunities and Challenges from User and Business Perspectives

Emerging Technologies: Opportunities and Challenges from User and Business Perspectives

Digital technologies in business education: a hybrid literature review from the Web of Science database

Generative AI in Higher Education: Uncertain Students, Ambiguous Use Cases, and Mercenary Perspectives

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