By analysing large datasets, AI can identify areas for improvement, predict student outcomes, and inform educators on strategies to enhance teaching methodologies and student outcomes.
Artificial Intelligence (AI) has the capacity to expedite achievement of sustainable development goals (SDGs) in most sectors of the economy. One of the papers contributing to the World Development Report 2024: Economic Growth in Middle-Income Countries noted this in its findings.
The research, AI, the New Wingman of Development, noted that AI could be leveraged for poverty reduction efforts, highlighting efficiency and effectiveness of using it to estimate poverty statistics.
Among the significant findings in agriculture was AI's ability to assist small farmers in increasing crop productivity in low-income countries through various applications and solutions. It also detects crop diseases and pests, optimises irrigation, and provides tailored crop management advice.
It can analyse soil moisture levels, weather patterns, and crop water requirements to conserve water and improve crop yields. AI-powered decision support systems help farmers make informed choices about crop selection, pest control, market trends, and resource allocation.
AI platforms can also provide real-time agricultural advice, weather forecasts, and market information to small farmers, empowering them to improve their farming practices.
In education, AI can facilitate the future of learning through personalised grading and tutoring in many ways, including personalisation of learning.
By tracking progress and adjusting the difficulty level of tasks, AI tutors can provide customised learning experiences that cater to the specific needs of each student, ensuring optimal comprehension and retention.
The study reveals that AI can offer students one-on-one tutoring and support, supplementing traditional classroom instruction. Through interactive lessons, practice exercises, and virtual simulations, AI tutors can engage students in personalized learning activities that enhance their understanding of complex concepts.
Further, AI can automate the grading process for assignments, tests, and assessments, saving teachers time and enabling faster student feedback. AI can use machine learning algorithms to evaluate student responses, provide scores, and generate detailed performance reports, streamlining the grading workflow.
More importantly, AI analytics can generate valuable insights into student performance trends, learning gaps, and instructional effectiveness.
By analysing large datasets, AI can identify areas for improvement, predict student outcomes, and inform educators on strategies to enhance teaching methodologies and student outcomes.
AI's role in the development sector is not limited to specific fields. It plays a crucial role in enhancing energy efficiency, traffic management, and infrastructure monitoring.
In terms of energy efficiency, AI algorithms can analyse energy consumption patterns and optimise usage in various settings such as buildings, industries, and transportation systems. Additionally, AI can predict equipment failures and recommend maintenance schedules, thereby reducing downtime and improving overall energy efficiency.
Moreover, AI systems can manage energy demand in real time by adjusting consumption based on supply availability and pricing, promoting efficient energy usage.
In traffic management, AI-powered systems are instrumental in analysing traffic patterns, optimising signal timings, and suggesting alternative routes to alleviate congestion and enhance traffic flow. Furthermore, AI algorithms can predict accident-prone areas by analysing historical data, enabling the implementation of preventive measures to bolster road safety. AI also offers innovative parking solutions.
Regarding infrastructure monitoring, AI technologies excel in remote sensing by analysing satellite imagery and sensor data to monitor the conditions of critical infrastructure such as bridges, roads, and pipelines.
By detecting potential issues early on, AI helps prevent escalations and ensures timely maintenance. AI systems also excel in asset management by tracking infrastructure asset conditions, predicting maintenance requirements, and optimizing resource allocation for efficient infrastructure management.
In disaster response scenarios, AI technologies prove invaluable by analyzing real-time data to assess damage, prioritise recovery efforts, and allocate resources effectively post-disaster.
The technology offers huge opportunities for improving urban growth and contributing to the SDGs. However, these opportunities come with ethical and social challenges that need to be addressed. By ensuring responsible and ethical use of AI, we can build better and greener cities.
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