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While AI has become more prevalent in our society than ever, many young learners are found holding various naive, erroneous conceptions of AI due to the influence of their technology and media environments. To address this issue, this study seeks to propose a novel pedagogical solution to improve upper‐elementary school students' scientific understanding of AI. Following a theory‐informed design convention, we propose an embodied, analogical and disruptive (EAD) approach which is centred on a human–AI comparison through analogical teaching and embodied interaction. To evaluate the impact of this approach, a matched‐group experimental study with pre‐ and posttest interviews was conducted among 77 Grade 6 elementary students in China. The statistical analysis showed that the experimental group, learning via the EAD approach, significantly outperformed their counterparts receiving direct instruction in terms of student growth in understanding. Qualitative analyses revealed the strengths of the EAD approach in supporting student engagement, abstract thinking and system thinking, and its limitations in cognitive overload and communication issues. Patterns of student learning were identified, including their mental schemas and strategies. The EAD approach, as an evidence‐based, age‐appropriate pedagogical solution, demonstrates the value of embodied cognition and human–AI analogy in AI education in elementary education. Practitioner notesWhat is already known about this topic Many young students hold naive, erroneous conceptions about AI.Pedagogical design has long been a critical challenge in teaching AI in K‐12 schools.Human–AI analogies are often used to teach AI, but with obvious pitfalls to be fixed.What this paper adds An embodied, analogical and disruptive (EAD) approach is proposed to improve students' understandings of AI.An experimental study shows statistically significant greater gains in students' understanding with the EAD approach than with the conventional AI‐only direct instruction approach.Qualitative analysis shows the strength of the EAD approach in supporting student engagement, abstract thinking and systems thinking, and its limitations in cognitive overload and communication issues.Implications for practice and/or policy The EAD approach offers a novel pedagogical solution to promote inclusive and quality AI education for young learners.The EAD approach highlights the pedagogical value of embodied cognition and human–AI analogy in AI education.The EAD approach can complement programming and other plugged activities in AI, and more broadly technology and engineering education. [ABSTRACT FROM AUTHOR]

Landslides with step‐like deformation features are widely distributed in the Three Gorges Reservoir area (TGR) of China, posing a severe hazard to the inhabitants of this region. This paper proposes a multi‐input and multi‐output intelligent integrated displacement prediction model for landslides with step‐like displacement patterns. In this new model, three interconnected and information‐transmitted functional sub‐models are integrated. Unsupervised learning is used to identify different landslide deformation states automatically, and the imbalance classification and explainable artificial intelligence techniques are introduced for qualitative prediction and information filtering. Probability theory and deep machine learning are adopted to provide deterministically predicted values and quantify their uncertainty. The case study of the Baijiabao landslide in the TGR region proves that the proposed model performs satisfactorily in both point and interval predictions. The intelligent integrated model can also provide the forecast of landslide deformation states, visual input information filtering and back analysis of influencing factors, which are valuable to landslide early warning and risk management. [ABSTRACT FROM AUTHOR]

With the continuous advancement of urbanization, the contradiction between urban development and environmental resources has become increasingly prominent, and environmental pollution has become increasingly serious. To fundamentally solve the problems of environment, energy, and low carbon, we must rely on the intelligence of energy. This paper aims to study the sustainable development of China's intelligent energy industry based on artificial intelligence and low‐carbon economy. In view of the problems existing in the optimization of power generation industry, this paper uses the annual load, electricity price, weather, and climate data of a southern power grid, uses the statistical variation particle swarm optimization algorithm, uses the historical runoff and rainfall data to optimize it, and studies the analysis methods, characteristics and laws of short‐term load, electricity price and runoff, as well as the uncertain factors affecting their changes. The experimental results show that the predicted price is close to the actual price, and the median error of each period is <1% in statistical analysis, so the forecast value can be used to replace the actual value for scheduling. This method makes full use of the adaptive mutation in the late stage of particle optimization, and introduces the mechanism of particle size selection, which fully ensures the diversity of particles and improves the search ability of particles. [ABSTRACT FROM AUTHOR]

Background: Video lectures which include the instructor's presence are becoming increasingly popular. Presenting a real human does, however, entail higher financial and time costs in making videos, and one innovative approach to reduce costs has been to generate a virtual speaking instructor. Objectives: The current study examined whether the use of a virtual instructor in video lectures would facilitate learning as well as a human instructor, and whether manipulating the virtual instructor's characteristics (i.e., voice and appearance) might optimize the effectiveness of the virtual instructor. Methods: Our study set four conditions. In the control condition, students watched a human instructor. In the experiment conditions, students watched one of (a) a virtual instructor which used the human instructor's voice and an AI image, (b) a virtual instructor which spoke in an AI voice with an AI image made to speak using text‐to‐speech and lip synthesis techniques, or (c) a virtual instructor with used an AI voice and an AI likable‐image of an instructor. Results and Conclusions: The AI likable instructor condition had a significant positive effect on students' learning performance and motivation, without decreasing the attention students paid to the learning materials. Implications: Our findings suggest that instructional video designers can make use of AI voices and AI images of likable humans as instructors to motivate students and enhance their learning performance. Lay Description: What is already known about this topic: Video lectures which include the instructor's presence are becoming increasingly popular.Presenting a virtual instructor (i.e., a pedagogical agent) in video lectures has been shown to reduce costs of video production, both financially and in terms of time. What this paper adds: An AI‐generated voice and an AI‐generated image facilitated more students' transfer performance than a human instructor.The AI likable instructor condition had a significant positive effect on students' learning performance and motivation, without decreasing the attention students paid to the learning materials. Implications for practice and/or policy: We encourage instructional designers to use AI‐generated voices and AI‐generated images connected to a virtual instructor instead of a human instructor in video lectures.We encourage instructional designers to use likable human images (as defined by the students themselves) as a way to motivate students more and enhance their learning performance. [ABSTRACT FROM AUTHOR]

In recent years, research on intelligent systems is becoming more and more popular in academe. Intelligent systems research mainly involves the field of artificial intelligence. There are many research groups engaged in the research of intelligent systems in the world, and a variety of achievements have been made. In this paper, the main research institutions in China as well as their main research topics are introduced. [ABSTRACT FROM AUTHOR]