Search results

This paper aims to examine the complex balance between enthusiasm and skepticism regarding artificial intelligence (AI) integration in educational practices. It advocates for a cautious, evidence-based approach while addressing both opportunities and challenges, aligning with the United Nations Sustainable Development Goal 4 (SDG4) for Quality Education.

Through critical analysis of current discourse surrounding AI in education, this paper synthesizes existing literature on both supportive and skeptical perspectives. The methodology involves systematic examination of past educational technology trends, current AI developments and their implications for teaching and learning. The paper develops its research agenda through careful consideration of existing empirical studies, theoretical frameworks and identifying gaps in current understanding.

The analysis reveals that while AI offers promising potential for enhancing learning outcomes and educational accessibility, its integration presents significant challenges that require careful consideration. The paper identifies critical tensions between technological innovation and pedagogical values, highlighting areas where enthusiasm for AI adoption must be tempered with empirical evidence and critical evaluation. Current evidence suggests that successful AI integration requires balanced consideration of both opportunities and limitations, with particular attention to maintaining human-centered educational practices.

This viewpoint provides a comprehensive framework for understanding the dialectic between AI’s educational potential and its limitations. By synthesizing both supportive and critical perspectives, it offers a nuanced approach to AI integration that acknowledges both opportunities and challenges. The article’s value lies in its systematic identification of key research priorities and its emphasis on evidence-based implementation strategies that serve educational goals while mitigating potential risks.

Based on the sociotechnical systems theory, we examined the human–technology interactions in the context of future works conditioned by machine learning (ML) and artificial intelligence (AI). Skills needed to support career sustainability and the future of the workforce, particularly for the middle-skilled workforce in the contemporary United States America (USA) context, were also studied.

We conducted a scenario analysis to demonstrate the potential roles that human resource professionals may perform to fill the skill gaps given their expertise in the shaping and skilling processes.

Assessing the success of the integration of AI and ML into the middle-skilled workforce requires a multi-faceted approach that considers performance metrics, cost-effectiveness, job satisfaction, environmental impact and innovation. Employees with AI skills can be more competitive in the workforce and forward to high-skilled positions.

Empirical research and related studies focusing on evaluations of reskilling and upskilling processes and outcomes would support career sustainability and the future development of middle-skilled workers.

Through a proactive strategic career development plan with AI integration, middle-skilled workers may enhance their career sustainability and be prepared for future higher-skilled work.

The economic downturn caused by technology-induced unemployment may be addressed by unleashing middle-skilled workforce potentials for future work created by AI and robotics and sustaining economic competitiveness.

This article offers important implications for human resource development theory-minded researchers and scholarly practitioners.

Adopting adaptive behavior has become a basic measure for farmers because the increasingly severe climate change is affecting agricultural production. Perception is a critical first step in adopting adaptive behaviors. Livelihood resilience represents a farmer's ability to adapt to climate change. Therefore, this article aims to explore the impact of livelihood resilience and climate change perception on the climate change adaptation behavior of farmers in the Qinling Mountains region of China.

In this study, 443 micro-survey data of farmers are obtained through one-on-one interviews with farmers. The Logit model and Poisson regression model are used to empirically examine the impact of farmers' livelihood resilience and climate change perception on their climate change adaptation behaviors.

It was found that 86.68% of farmers adopt adaptive behaviors to reduce the risks of facing climate change. Farmers' perception of extreme weather has a significant positive impact on their adaptive behavior under climate change. The resilience of farmers' livelihoods and their perception of rainfall have a significant positive impact on the intensity of their adaptive behavior under climate change. Climate change adaptation behaviors are also different for farmers with different levels of livelihood resilience.

Based on the results, policy recommendations are proposed to improve farmers' perception of climate change, enhance the sustainability of farmers' adaptive behavior to climate change, strengthen emergency management and infrastructure construction and adjust and upgrade farmers' livelihood models.