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Papers
61,005 resultsShowing papers similar to Integrating Environmental Education into English Language Teaching: An AI-Supported Approach
ClearContextual Teaching and Learning in Learning Environmental Pollution: the Effect on Student Learning Outcomes
This study evaluated contextual teaching and learning approaches for environmental pollution education, finding that connecting plastic pollution and waste concepts to real-world situations significantly improved student comprehension and motivation compared to conventional instruction.
Programme basé sur l’étude collective d’une leçon (Lesson Study) et les applications d'intelligence artificielle pour développer les pratiques enseignantes et les compétences de la pensée réflexive auprès des enseignants de français
This French-language paper evaluates an AI-enhanced lesson study program to develop reflective teaching practices among French language teachers. This is an education research paper with no direct connection to microplastics or environmental health.
Artificial Intelligence as an Aid: Regulating Plastic and Microplastic Pollution
Researchers reviewed how India is tackling plastic and microplastic pollution through legislation and cleanup campaigns, while also examining how artificial intelligence tools could improve monitoring, detection, and regulation of plastic waste. The article argues that AI integration into environmental policy could significantly accelerate progress against this global health and ecological crisis.
The supporting role of Artificial Intelligence and Machine/Deep Learning in monitoring the marine environment: a bibliometric analysis
This review examines the supporting role of artificial intelligence and machine learning in monitoring and managing plastic pollution, covering applications in remote sensing, image-based plastic detection, and predictive modeling of plastic fate. The authors identify deep learning for image classification and satellite-based detection as the most rapidly advancing AI applications in plastic pollution science.
The Use of Artificial Intelligence and Machine Learning in Creating a Roadmap Towards a Circular Economy for Plastics
This paper examines how artificial intelligence and machine learning can help transition the plastics industry toward a circular economy. AI tools can optimize recycling processes, predict material degradation, and identify opportunities to reduce plastic waste before it enters the environment.
Advancing environmental sustainability through emerging AI-based monitoring and mitigation strategies for microplastic pollution in aquatic ecosystems
This review explores how artificial intelligence technologies, including machine learning, computer vision, and remote sensing, can improve the detection, tracking, and removal of microplastic pollution in waterways. Researchers found that AI-based approaches offer significant advantages over traditional monitoring methods for identifying microplastic distribution patterns. The study highlights the potential of AI-driven robotic systems to support more efficient and scalable environmental cleanup efforts.
Conversational AI Tools for Environmental Topics: A Comparative Analysis of Different Tools and Languages for Microplastics, Tire Wear Particles, Engineered Nanoparticles and Advanced Materials
Researchers tested ChatGPT, Microsoft Bing, and Google Bard on environmental science questions about microplastics, tire wear particles, nanoparticles, and advanced materials across six languages. The AI tools provided generally satisfactory answers but still require expert review, as some statements were found to be debatable or inaccurate.
What are the valuable lessons from global research on environmental literacy in the last two decades? A systematic literature review
This paper is not about microplastics; it is a systematic literature review of global research on environmental literacy in education over the past two decades, analyzing publication trends and teaching approaches.
Artificial intelligence and IoT driven technologies for environmental pollution monitoring and management
This review explores how artificial intelligence and Internet of Things sensors can be used to detect and monitor environmental pollutants, including microplastics, in air, water, and soil. Machine learning methods show promise for improving pollution tracking and prediction, but challenges remain around data sharing and model reliability. Advanced monitoring technology could play a key role in identifying and managing microplastic contamination in the environment.
Harnessing Artificial Intelligence to Increase the Efficiency of Education Management in the Future
This paper is not about microplastics; it examines the use of artificial intelligence to improve educational management and teaching effectiveness.
A Critical Review on Artificial Intelligence—Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges
Researchers reviewed the use of artificial intelligence and machine learning techniques for detecting and identifying microplastics in environmental samples. The study found that AI-based imaging tools can significantly speed up analysis and improve accuracy compared to traditional manual methods. However, challenges remain around standardizing datasets and making these tools accessible for routine environmental monitoring.
Global Plastic Waste Management: Analyzing Trends, Economic and Social Implications, and Predictive Modeling Using Artificial Intelligence
This study analyzed global plastic waste management practices and used artificial intelligence models to predict future waste trends. The researchers found that current waste management systems are struggling to keep up with rising plastic production, posing threats to ecosystems, human health, and the economy. The AI models help forecast where waste generation is headed, which could inform better policy decisions.
Managing Marine Environmental Pollution using Artificial Intelligence
This review explores how artificial intelligence technologies are being developed to monitor and manage marine environmental pollution, including plastic contamination. The study suggests that AI-based approaches such as automated detection and predictive modeling offer promising opportunities for understanding ocean pollution and supporting sustainability goals.
Application of Machine learning techniques in environmental governance: A review
This paper is not relevant to microplastics research — it reviews the application of machine learning methods in environmental governance broadly, covering air and water quality monitoring and land use management.
Mapping the Rise in Machine Learning in Environmental Chemical Research: A Bibliometric Analysis
Researchers conducted a bibliometric analysis of over 3,100 articles to map how machine learning is being applied in environmental chemistry research, including areas like pollutant monitoring and toxicity prediction. They found an exponential surge in publications from 2015 onward, with deep learning and natural language processing emerging as key growth areas. The study identifies microplastics and PFAS among the environmental topics increasingly being studied with AI-driven approaches.
Marine plastic pollution in kindergarten as a means of engaging toddlers with STEM education and educational robotics
This paper explores using marine plastic pollution as a topic to engage preschool children with STEM education and robotics through experiential learning. Environmental topics like plastic pollution can serve as motivating contexts for early science and technology education.
Integrating Machine Learning and IoT Technologies for Smart Water Quality Monitoring: Methods, Challenges, and Future Directions
Machine learning and IoT sensor technologies were integrated into a smart environmental monitoring system designed for real-time detection of pollutants including microplastics. The platform demonstrates how digital technologies can improve the spatial and temporal resolution of environmental contamination surveillance.
Using artificial intelligence to rapidly identify microplastics pollution and predict microplastics environmental behaviors
This review summarizes how artificial intelligence and machine learning are being used to identify, track, and predict the environmental behavior of microplastics in soil and water. AI methods can analyze the chemical composition, shape, and distribution of microplastics faster and more accurately than traditional techniques. The technology could help scientists better understand where microplastics accumulate and what risks they pose to ecosystems and human health.
How AI methods enhance the design and performance of nanophotonic environmental sensors: a systematical review
This review examines how artificial intelligence methods including machine learning and deep learning are being integrated with nanophotonic sensor platforms to enhance environmental monitoring capabilities, with applications including microplastic and contaminant detection in portable, real-time systems.
¿La IA usada en biología de la conservación es una buena estrategia de justicia ambiental?
This paper critically examines whether artificial intelligence applications in conservation biology serve environmental justice goals. It raises concerns that AI tools may reinforce existing power imbalances and overlook local community knowledge in conservation decisions.
Learning Effects of Augmented Reality and Game-Based Learning for Science Teaching in Higher Education in the Context of Education for Sustainable Development
Researchers tested an augmented reality game-based learning environment called 'Beat the Beast' to teach university students about microplastics across biology, chemistry, and engineering disciplines. A study of 203 pre-service teachers compared settings with and without augmented reality and game elements to measure effects on motivation, knowledge, and sustainability outcomes. The findings contribute to understanding how emerging educational technologies can support interdisciplinary science teaching about environmental topics.
Deep Learning Approaches for Detection and Classification of Microplastics in Water for Clean Water Management
Researchers applied dual deep learning models (YOLOv8, YOLOv11, and several CNN architectures) to detect and classify microplastics in water, finding that these AI approaches could accurately identify plastic types across both aquatic and non-aquatic datasets.
Integrating artificial intelligence with microbial biotechnology for sustainable environmental remediation
This review examines how artificial intelligence is being combined with microbial biotechnology to improve the detection and breakdown of persistent environmental pollutants including microplastics. Researchers found that AI models achieve over 90 percent accuracy in classifying microplastics and have helped design enzymes that degrade PET plastic up to 46 times faster than conventional approaches. The integration of AI with biotechnology represents a significant advance in developing sustainable pollution remediation strategies.
Transformative STEAM Educators Developing Students’ Capabilities For Resolving Global Sustainability Crises
This paper argues that transformative STEAM education — integrating science, technology, engineering, arts, and mathematics — can develop students' capacity to address global sustainability crises including plastic pollution. The research advocates for educational approaches that go beyond conventional curricula to build environmental problem-solving skills.