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Calligraphy is the treasure of traditional Chinese culture and it is ubiquitous in China. Because of the unique aesthetic characteristics of calligraphy, the difficulty of calligraphy teaching lies in the assessment of calligraphy works. The major problems concerning calligraphy assessment are the low time efficiency of calligraphy assessment and low engagement of students in the assessing process. As an active learning method, peer assessment (PA) has been widely applied to education. This research aims to explore the application of PA to the calligraphy class. With reasonable training, elementary school students can act rationally as an assessing group. On the basis of students’ scores, this study further takes the most reasonable scores based on clustering analysis as the final score. Through the comparative analysis of the students’ scores and the teachers’ scores, it shows that the students’ scores of calligraphy writing can obtain results that are close to the teachers’ scores. The significance of the study lies in enhancing the time-efficiency of calligraphy assessment and maintaining the learners’ high levels of engagement in the assessing process. The results show that the use of PA in the assessment of calligraphy writing is an effective and accurate method of calligraphy assessment, which contributes to students’ command of the key points of calligraphy marking and plays an integral role in guiding students’ future calligraphy practice.

Artificial intelligence is the unification of philosophy, cognitive science, mathematics, neurophysiology, psychology, computer science, information theory, cybernetics, and uncertainty theory. Therefore, it is feasible and necessary to utilize STEAM (Science, Technology, Engineering, Liberal Arts, and Mathematics) education to learn artificial intelligence courses. Computational thinking skills are of vital importance to high school students. This paper integrates artificial intelligence education with STEAM model with the aim of enhancing students' computational thinking skills. First, we investigate the feasibility of this model and set teaching objectives about artificial intelligence curricula. Second, artificial intelligence curricula with STEAM model is proposed to carry out interdisciplinary artificial intelligence knowledge acquisition. Finally, the effects of this model on students' computational thinking skills, learning motivation, and self-efficacy are evaluated. One hundred thirty-six participants are recruited from a high school in Beijing. The results reveal that the integration of artificial intelligence education with STEAM is able to promote computational thinking skills, learning motivation, and self-efficacy of the students in the experimental group. The main implication of this study is that artificial intelligence education in light of STEAM model can be used as a teaching guide for the combination of artificial intelligence curricula with multi-disciplinary knowledge at the primary and secondary levels.