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crossrefSustainability2025-09-18Cited by 5

Human–AI Collaboration: Students’ Changing Perceptions of Generative Artificial Intelligence and Active Learning Strategies

Hyunju Woo, Yoon Y. Cho

This paper explores ways to use AI for active learning strategies so that students in higher education may perceive generative artificial intelligence (generative AI) as a collaborative partner in their learning experience. This study proposes AI can help advance educational sustainability when students read texts on critical posthumanism, reflect on the philosophical and ontological paradigms through which the human has been understood, and discuss the collaborative relationship between humans and AI using literary texts. By analyzing AI-collaborated writing assignments, student questionnaires, and peer evaluations, this study concludes there are three learning types based on the different levels of students’ perceived difficulties: a cognitive learner, who focuses on AI’s functional aspects such as information retrieval; a metacognitive learner, who engages with generative AI in a two-way communication; and an affective learner, who strictly differentiates the human from the nonhuman and claims reciprocity in human–AI communication to be impossible. This study utilizes a mixed-methods approach by integrating quantitative analysis of the student questionnaires and qualitative analysis of the writing assignments. The findings of the study will serve as a valuable resource for researchers and educators committed to fostering future-oriented citizenship through collaboration between humans and generative AI in higher education.

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crossrefSustainability2025-07-19Cited by 22

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The resilience of the pharmaceutical supply chain (PSC) is crucial to ensuring the availability of medical products. However, increasing complexity and logistical bottlenecks have exposed weaknesses within PSC frameworks. These challenges underscore the urgent need for more resil…

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crossrefSustainability2026-01-08Cited by 3

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crossrefSustainability2023-07-19Cited by 29

A New Insight for Daily Solar Radiation Prediction by Meteorological Data Using an Advanced Artificial Intelligence Algorithm: Deep Extreme Learning Machine Integrated with Variational Mode Decomposition Technique

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Reliable and precise estimation of solar energy as one of the green, clean, renewable and inexhaustible types of energies can play a vital role in energy management, especially in developing countries. Also, solar energy has less impact on the earth’s atmosphere and environment a…

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openalexSustainability2026-07-23

Artificial Intelligence Utilization and Perceived Firm Performance in Chinese Logistics Firms: The Roles of Innovation Capability and Logistics Efficiency

Chenghao Shang, Chang One Kim

Artificial intelligence (AI) is used in logistics, but the mechanisms linking AI utilization to firm performance remain insufficiently differentiated. Drawing on the information technology business value perspective and dynamic capabilities theory, this study examines whether man…

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crossrefSustainability2024-07-10Cited by 26

The Application of Machine Learning and Deep Learning in Intelligent Transportation: A Scientometric Analysis and Qualitative Review of Research Trends

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Machine learning (ML) and deep learning (DL) have become very popular in the research community for addressing complex issues in intelligent transportation. This has resulted in many scientific papers being published across various transportation topics over the past decade. This…

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crossrefSustainability2024-12-17Cited by 9

Prediction of Potential Evapotranspiration via Machine Learning and Deep Learning for Sustainable Water Management in the Murat River Basin

Ibrahim A. Hasan, Mehmet Ishak Yuce

Potential evapotranspiration (PET) is a significant factor contributing to water loss in hydrological systems, making it a critical area of research. However, accurately calculating and measuring PET remains challenging due to the limited availability of comprehensive data. This…

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