As a cutting-edge field of artificial intelligence in education (AIEd) that depends on advanced computing technologies, AI performance prediction model is widely used to identify at-risk students that tend to fail, establish student-centered learning pathways, and optimize instructional design and development. A majority of the existing AI prediction models focus on the development and optimization of the accuracy of AI algorithms rather than applying AI models to provide student with in-time and continuous feedback and improve the students’ learning quality. To fill this gap, this research integrated an AI performance prediction model with learning analytics approaches with a goal to improve student learning effects in a collaborative learning context. Quasi-experimental research was conducted in an online engineering course to examine the differences of students’ collaborative learning effect with and without the support of the integrated approach. Results showed that the integrated approach increased student engagement, improved collaborative learning performances, and strengthen student satisfactions about learning. This research made contributions to proposing an integrated approach of AI models and learning analytics (LA) feedback and providing paradigmatic implications for future development of AI-driven learning analytics.

Recently, there has been a growing interest in deploying smart materials as sensing components of structural health monitoring systems. In this arena, piezoelectric materials offer great promise for researchers to rapidly expand their many potential applications. The main goal of this study is to review the state-of-the-art piezoelectric-based sensing techniques that are currently used in the structural health monitoring area. These techniques range from piezoelectric electromechanical impedance and ultrasonic Lamb wave methods to a class of cutting-edge self-powered sensing systems. We present the principle of the piezoelectric effect and the underlying mechanisms used by the piezoelectric sensing methods to detect the structural response. Furthermore, the pros and cons of the current methodologies are discussed. In the end, we envision a role of the piezoelectric-based techniques in developing the next-generation self-monitoring and self-powering health monitoring systems.

Online education has been facing difficulty in predicting the academic performance of students due to the lack of usage of learning process, summative data and a precise prediction of quantitative relations between variables and achievements. To address these two obstacles, this study develops an artificial intelligence-enabled prediction model for student academic performance based on students’ learning process and summative data. The prediction criteria are first predefined to characterize and convert the learning data in an online engineering course. An evolutionary computation technique is then used to explore the best prediction model for the student academic performance. The model is validated using another online course that applies the same pedagogy and technology. Satisfactory agreements are obtained between the course outputs and model prediction results. The main findings indicate that the dominant variables in academic performance are the knowledge acquisition, the participation in class and the summative performance. The prerequisite knowledge tends not to play a key role in academic performance. Based on the results, pedagogical and analytical implications are provided. The proposed evolutionary computation-enabled prediction method is found to be a viable tool to evaluate the learning performance of students in online courses. Furthermore, the reported genetic programming model provides an acceptable prediction performance compared to other powerful artificial intelligence methods.

Mechanical metamaterials have opened an exciting venue for control and manipulation of architected structures in recent years. Research in the area of mechanical metamaterials has covered many of their fabrication, mechanism characterisation and application aspects. More recently, however, a paradigm shift has emerged to an exciting research direction towards designing, optimising and characterising mechanical metamaterials using artificial intelligence (AI) techniques. This new line of research aims at addressing the difficulties in mechanical metamaterials (i.e. design, analysis, fabrication and industrial application). This review article discusses the advent and development of mechanical metamaterials, and the future trends of applying AI to obtain smart mechanical metamaterials with programmable mechanical response. We explain why architected materials and structures have prominent advantages, what are the main challenges in the mechanical metamaterial research domain, and how to surpass the limit of mechanical metamaterials via the AI techniques. We finally envision the potential research avenues and emerging trends for using the AI-enabled mechanical metamaterials for future innovations.