Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development

Alkhulaifat, Dana; Rafful, Patricia; Khalkhali, Vahid; Welsh, Michael; Sotardi, Susan

doi:10.1016/j.jacr.2023.04.013

Cited by 9 publications

(4 citation statements)

References 28 publications

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“…Artificial intelligence (AI) in entrepreneurship has a transformational impact on entrepreneurial expertise. AI technologies, through diverse methods, improve the acquisition and enrichment of entrepreneurial knowledge (Alkhulaifat et al, 2023). For starters, AI enables businesses to quickly and efficiently evaluate massive amounts of data, discovering new insights and patterns that lead to knowledge creation.…”

Section: Artificial Intelligence Utilization and Entrepreneurial Know...mentioning

confidence: 99%

The Influence of Entrepreneurial Bricolage on Opportunity Recognition for New Ventures Based on Artificial Intelligence

Kang,

Chaivirutnukul,

Zeng

2023

J INFORM SYSTEMS ENG

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This study delves into the subtle relationships between entrepreneurial bricolage, the use of artificial intelligence (AI), entrepreneurial knowledge, and the identification of opportunities in the field of entrepreneurship. It fills information gaps about the impact of these elements on opportunity recognition while examining the mediating role of entrepreneurial expertise. The major goal of this study was to investigate how entrepreneurial bricolage and AI adoption influence opportunity recognition, with a particular emphasis on the mediating role of entrepreneurial knowledge. Furthermore, the study hoped to contribute empirical data that could improve our knowledge of how these aspects interact. A structured questionnaire was provided to 420 entrepreneurs as part of a quantitative study methodology to collect data. For data analysis, SPSS was used, which included descriptive statistics, correlation analysis, regression analysis, and mediation analysis. According to the findings, entrepreneurial knowledge serves as a bridge between entrepreneurial bricolage and opportunity recognition, as well as between AI utilization and opportunity recognition. As a result, the study presents empirical evidence demonstrating the importance of entrepreneurial bricolage, AI adoption, and entrepreneurial knowledge in improving opportunity recognition. These findings highlight the importance of these aspects and their interdependence in the context of entrepreneurship. This work has practical consequences for businesses, politicians, and researchers alike by shining light on the optimal allocation of resources, the adoption of AI, and the cultivation of knowledge to better opportunity recognition. Furthermore, it adds empirical data on the role of entrepreneurial knowledge as a mediator in the theoretical understanding of entrepreneurship and innovation.

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Section: Artificial Intelligence Utilization and Entrepreneurial Know...mentioning

confidence: 99%

The Influence of Entrepreneurial Bricolage on Opportunity Recognition for New Ventures Based on Artificial Intelligence

Kang,

Chaivirutnukul,

Zeng

2023

J INFORM SYSTEMS ENG

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“…The integration of artificial intelligence assisted learning (AIAL) in universities worldwide is rapidly increasing to enhance the learning experience for students. AIAL utilizes artificial intelligence to identify valuable patterns and insights, thereby improving education (Alkhulaifat et al, 2023;Tam et al, 2023;X. Wang et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Understanding the factors influencing undergraduate students' readiness to embrace AI technologies is crucial as it prepares them for an AI-integrated future (Alkhulaifat et al, 2023;Chiu et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

“…Through a survey at prominent universities in Hangzhou, this study aims to uncover factors influencing the acceptance of AI technology among undergraduate students. AI technologies offer tailored learning experiences and data-driven insights, potentially enhancing pedagogical strategies (Alkhulaifat et al, 2023;Lakshmi et al, 2023). However, their impact depends on various adoption and utilization factors among students.…”

Section: Introductionmentioning

confidence: 99%

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Navigating the Future: Exploring AI Adoption in Chinese Higher Education Through the Lens of Diffusion Theory

Huang,

J Kumarasinghe,

Sakunthala Jayarathna

2024

IJIKM

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Aim/Purpose: This paper aims to investigate and understand the intentions of management undergraduate students in Hangzhou, China, regarding the adoption of Artificial Intelligence (AI) technologies in their education. It addresses the need to explore the factors influencing AI adoption in the educational context and contribute to the ongoing discourse on technology integration in higher education. Background: The paper addresses the problem by conducting a comprehensive investigation into the perceptions of management undergraduate students in Hangzhou, China, regarding the adoption of AI in education. The study explores various factors, including Perceived Relative Advantage and Trialability, to shed light on the nuanced dynamics influencing AI technology adoption in the context of higher education. Methodology: The study employs a quantitative research approach, utilizing the Confirmatory Tetrad Analysis (CTA) and Partial Least Squares Structural Equation Modeling (PLS-SEM) methodologies. The research sample consists of management undergraduate students in Hangzhou, China, and the methods include data screening, principal component analysis, confirmatory tetrad analysis, and evaluation of the measurement and structural models. We used a random sampling method to distribute 420 online, self-administered questionnaires among management students aged 18 to 21 at universities in Hangzhou. Contribution: This paper explores how management students in Hangzhou, China, perceive the adoption of AI in education. It identifies factors that influence AI adoption intention. Furthermore, the study emphasizes the complex nature of technology adoption in the changing educational technology landscape. It offers a thorough comprehension of this process while challenging and expanding the existing literature by revealing the insignificant impacts of certain factors. This highlights the need for an approach to AI integration in education that is context-specific and culturally sensitive. Findings: The study highlights students’ positive attitudes toward integrating AI in educational settings. Perceived relative advantage and trialability were found to impact AI adoption intention significantly. AI adoption is influenced by social and cultural contexts rather than factors like compatibility, complexity, and observability. Peer influence, instructor guidance, and the university environment were identified as pivotal in shaping students’ attitudes toward AI technologies. Recommendations for Practitioners: To promote the use of AI among management students in Hangzhou, practitioners should highlight the benefits and the ease of testing these technologies. It is essential to create communication strategies tailored to the student’s needs, consider cultural differences, and utilize the influence of peers and instructors. Establishing a supportive environment within the university that encourages innovation through policies and regulations is vital. Additionally, it is recommended that students’ attitudes towards AI be monitored constantly, and strategies adjusted accordingly to keep up with the changing technological landscape. Recommendation for Researchers: Researchers should conduct cross-disciplinary and cross-cultural studies with qualitative and longitudinal research designs to understand factors affecting AI adoption in education. It is essential to investigate compatibility, complexity, observability, individual attitudes, prior experience, and the evolving role of peers and instructors. Impact on Society: The study’s insights into the positive attitudes of management students in Hangzhou, China, toward AI adoption in education have broader societal implications. It reflects a readiness for transformative educational experiences in a region known for technological advancements. However, the study also underscores the importance of cautious integration, considering associated risks like data privacy and biases to ensure equitable benefits and uphold educational values. Future Research: Future research should delve into AI adoption in various academic disciplines and regions, employing longitudinal designs and qualitative methods to understand cultural influences and the roles of peers and instructors. Investigating moderating factors influencing specific factors’ relationship with AI adoption intention is essential for a comprehensive understanding.

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Explainable Artificial Intelligence in Paediatric: Challenges for the Future

Salih,

Menegaz,

Pillay

et al. 2024

Health Science Reports

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BackgroundExplainable artificial intelligence (XAI) emerged to improve the transparency of machine learning models and increase understanding of how models make actions and decisions. It helps to present complex models in a more digestible form from a human perspective. However, XAI is still in the development stage and must be used carefully in sensitive domains including paediatrics, where misuse might have adverse consequences.ObjectiveThis commentary paper discusses concerns and challenges related to implementation and interpretation of XAI methods, with the aim of rising awareness of the main concerns regarding their adoption in paediatrics.MethodsA comprehensive literature review was undertaken to explore the challenges of adopting XAI in paediatrics.ResultsAlthough XAI has several favorable outcomes, its implementation in paediatrics is prone to challenges including generalizability, trustworthiness, causality and intervention, and XAI evaluation.ConclusionPaediatrics is a very sensitive domain where consequences of misinterpreting AI outcomes might be very significant. XAI should be adopted carefully with focus on evaluating the outcomes primarily by including paediatricians in the loop, enriching the pipeline by injecting domain knowledge promoting a cross‐fertilization perspective aiming at filling the gaps still preventing its adoption.

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Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development

Cited by 9 publications

References 28 publications

The Influence of Entrepreneurial Bricolage on Opportunity Recognition for New Ventures Based on Artificial Intelligence

The Influence of Entrepreneurial Bricolage on Opportunity Recognition for New Ventures Based on Artificial Intelligence

Navigating the Future: Exploring AI Adoption in Chinese Higher Education Through the Lens of Diffusion Theory

Explainable Artificial Intelligence in Paediatric: Challenges for the Future

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Implications of Pediatric Artificial Intelligence Challenges for Artificial Intelligence Education and Curriculum Development

Cited by 9 publications

References 28 publications

The Influence of Entrepreneurial Bricolage on Opportunity Recognition for New Ventures&nbsp;Based on Artificial Intelligence

The Influence of Entrepreneurial Bricolage on Opportunity Recognition for New Ventures&nbsp;Based on Artificial Intelligence

Navigating the Future: Exploring AI Adoption in Chinese Higher Education Through the Lens of Diffusion Theory

Explainable Artificial Intelligence in Paediatric: Challenges for the Future

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Product

Resources

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The Influence of Entrepreneurial Bricolage on Opportunity Recognition for New Ventures Based on Artificial Intelligence

The Influence of Entrepreneurial Bricolage on Opportunity Recognition for New Ventures Based on Artificial Intelligence