Fighter jets are a critical national asset. Because of the high cost of their manufacture and that of their related equipment, both pilots and maintenance personnel must complete intensive training before coming into contact with a jet. Due to gradual military downsizing, one-on-one training is often impracticable, and the level of familiarization with procedures among personnel is difficult to measure. The US military introduced a chatbot as part of its digital training material to enhance training effectiveness and avoid equipment damage. In this study, the contribution of an artificial intelligence (AI) chatbot in training is explored. To evaluate the necessity of an AI chatbot, research samples were divided into two groups, namely an experimental and a control group, with 20 people in each group. A paired t test was employed for differentiation analysis of pretest and posttest average scores, revealing that the two groups exhibited a statistically significant improvement in their learning performance. In addition, the blend of analysis of variance results indicated significant differences between the two groups. The chatbot training was more effective than traditional instructor teaching in terms of trainee performance improvement.

The current study explores students’ collaboration and problem solving (CPS) abilities using a human-to-agent (H-A) computer-based collaborative problem solving assessment. Five CPS assessment units with 76 conversation-based items were constructed using the PISA 2015 CPS framework. In the experiment, 53,855 ninth and tenth graders in Taiwan were recruited, and a multidimensional item response analysis was used to develop CPS scales and represent the students’ collaboration and problem solving performance. The results show that the developed H-A approach is feasible for measuring students’ CPS skills, and the CPS scales are also shown to be reliable. In addition, the students’ CPS performance scores are further explored and discussed under the PISA CPS framework.

Feature extraction plays an essential role in Hyperspectral image classification. Linear discriminant analysis (LDA) is a commonly used feature extraction (FE) method to resolve the Hughes phenomenon for classification. The Hughes phenomenon (also called the curse of dimensionality) is often encountered in classification when the dimensionality of the space grows and the size of the training set is fixed, especially in the small sampling size problem. Recent studies show that the spatial information can greatly improve the classification performance. Hence, for hyperspectral image classification, it is not only necessary to use the available spectral information but also to exploit the spatial information. In this paper, spatial information is acquired by the concept of the Markov random field (MRF), and this spatial information is used to form the membership values of every pixel in the hyperspectral image. The experimental results on two hyperspectral images, the Washington DC Mall and the Indian Pine Site, show that the proposed method can yield a better classification performance than LDA in the small sampling size problem.

Collaborative problem solving (CPS) is a crucial skill for students and people entering the workforce. Hence, an online CPS assessment system was developed for the Programme for International Student Assessment to test 12 CPS skills. This study compared the CPS skills of students on the basis of...

The purpose of this study is to explore the validity of the assessment tool. The purposive sampling method is applied in this research on a total of 551 preschool children between 4 and 6 years old. Their ages range from 46 to 81 months, with an average age of 63.9 months (SD = 7.58). The assessment tool used in this research is the “Computerized Visual Motor Integration Assessment Tool Using Chinese Basic Strokes”. The multivariate logistic regression analysis was used to automatically determine children’s writing classification. The findings suggest that the assessment tool developed has good validity in terms of sensitivity, specificity and the accuracy. This tool is recommended for use in the diagnosis of Chinese visual motor integration issues for preschoolers, so that their potential writing difficulties may be detected early, and the goal of early intervention may be achieved.