A conceptual analysis of artificial intelligence (AI) on academic opportunities and challenges: a case study based on higher educational institutions in Bangladesh

IF 1.5 Q2 EDUCATION & EDUCATIONAL RESEARCH
Marzia Tamanna, Bijaya Sinha
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引用次数: 0

Abstract

Purpose

The purpose of this paper is to provide an in-depth analysis of the challenges associated with using artificial intelligence (AI) in academic research and suggest various preventive measures that can be taken to address these issues and transform them into opportunities.

Design/methodology/approach

To develop measurement items and constructs, the authors collected 248 responses through an online survey. These responses were then used to establish the structural model and determine discriminant validity through the use of structural equation modeling with SmartPLS 4.0.9.9. Additionally, the authors used SPSS (Version 29) to create graphs and visual representations of the challenges faced and the most commonly used AI tools. These techniques allowed them to explore data and draw meaningful conclusions for future research.

Findings

This research shows that AI has a positive impact on higher education, improving learning outcomes and data security. However, issues such as plagiarism and academic integrity can destroy students. The study highlights AI’s potential in education while emphasizing the need to address challenges.

Practical implications

This paper emphasizes the preventive measures to tackle academic challenges and suggests enhancing academic work.

Originality/value

This study examines how AI can be used to personalize learning and overcome challenges in this area. It emphasizes the importance of academic institutions in promoting academic integrity and transparency to prevent plagiarism. Additionally, the study stresses the need for technology advancement and exploration of new approaches to further improve personalized learning with AI.

人工智能(AI)对学术机遇和挑战的概念分析:基于孟加拉国高等教育机构的案例研究
目的本文旨在深入分析在学术研究中使用人工智能(AI)所面临的挑战,并提出各种预防措施,以解决这些问题并将其转化为机遇。设计/方法/途径为了开发测量项目和构造,作者通过在线调查收集了 248 份回复。然后,通过使用 SmartPLS 4.0.9.9 进行结构方程建模,利用这些回复建立结构模型并确定判别效度。此外,作者还使用 SPSS(29 版)创建了图表,直观地展示了所面临的挑战和最常用的人工智能工具。这些技术使他们能够探索数据,并为未来的研究得出有意义的结论。研究结果这项研究表明,人工智能对高等教育有着积极的影响,可以改善学习成果和数据安全。然而,剽窃和学术诚信等问题可能会毁掉学生。本研究强调了人工智能在教育领域的潜力,同时强调了应对挑战的必要性。本文强调了应对学术挑战的预防措施,并建议加强学术工作。它强调了学术机构在促进学术诚信和透明度以防止剽窃方面的重要性。此外,研究还强调了技术进步和探索新方法的必要性,以进一步提高人工智能个性化学习的水平。
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来源期刊
QUALITY ASSURANCE IN EDUCATION
QUALITY ASSURANCE IN EDUCATION EDUCATION & EDUCATIONAL RESEARCH-
CiteScore
3.10
自引率
20.00%
发文量
47
期刊介绍: QAE publishes original empirical or theoretical articles on Quality Assurance issues, including dimensions and indicators of Quality and Quality Improvement, as applicable to education at all levels, including pre-primary, primary, secondary, higher and professional education. Periodically, QAE also publishes systematic reviews, research syntheses and assessment policy articles on topics of current significance. As an international journal, QAE seeks submissions on topics that have global relevance. Article submissions could pertain to the following areas integral to QAE''s mission: -organizational or program development, change and improvement -educational testing or assessment programs -evaluation of educational innovations, programs and projects -school efficiency assessments -standards, reforms, accountability, accreditation, and audits in education -tools, criteria and methods for examining or assuring quality
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