ChatGPT is an artificial intelligence chatbot that utilizes advanced natural language processing technologies, including large language models, to produce human‐like responses to user queries spanning a wide range of topics from programming to mathematics. As an emerging generative artificial intelligence (GAI) tool, it presents novel opportunities and challenges to the ongoing digital transformation of education. This article employs a systematic review approach to summarize the viewpoints of Chinese scholars and experts regarding the implementation of GAI in education. The research findings indicate that a majority of Chinese scholars support the cautious integration of GAI into education as it serves as a learning tool that offers personalized educational experiences for students. However, it also raises concerns related to academic integrity and the potential hindrance to students' critical thinking skills. Consequently, a framework called DATS, which outlines an optimization path for future GAI applications in schools, is proposed. The framework takes into account the perspectives of four key stakeholders: developers, administrators, teachers, and students.

ChatGPT has received considerable attention in education, particularly in programming education because of its capabilities in automated code generation and program repairing and scoring. However, few empirical studies have investigated the use of ChatGPT to customize a learning system for scaffolding students’ computational thinking. Therefore, proposed here is an intelligent programming scaffolding system using ChatGPT following the theoretical framework of computational thinking and scaffolding. A mixed-method study was conducted to investigate the affordance of the scaffolding system using ChatGPT, and the findings show that most students had positive attitudes about the proposed system, and it was effective in improving their computational thinking generally but not their problem-solving skills. Therefore, more scaffolding strategies are discussed with the aim of improving student computational thinking, especially regarding problem-solving skills. The findings of this study are expected to guide future designs of generative artificial intelligence tools embedded in intelligent learning systems to foster students’ computational thinking and programming learning.

Writing an essay is a very important skill for students to master, but a difficult task for them to overcome. It is particularly true for English as Second Language (ESL) students in China. It would be very useful if students could receive timely and effective feedback about their writing. Automatic essay feedback generation is a challenging task, which requires understanding the relationship between the text features of the essay and feedback. In this study, we first analyzed 1,290 teacher comments on their 327 Englishmajor students and annotated the feedback on seven aspects of writing, including the grammar, spelling, sentence diversity, structure, organization, supporting ideas, coherence, and conclusion, for each paper. Then, an automatic feedback classification experiment was conducted with the machine learning approach. Finally, we investigated the impact of the system generated-indirect corrective feedback (ICF) and human teachers' direct corrective feedback (DCF) in two English writing classes (N = 56 in ICF class; N = 54 in DCF class) at a key Chinese university through a web-based assignment management system. The study results indicated the feasibility of this approach that system generated ICF can be as useful as direct comments made by the teachers in terms of improving the quality of the content regarding to the structure, organization, supporting ideas, coherence, and conclusion, and encouraging students to spend more time on self-correction.

Automatic question generation can help teachers to save the time necessary for constructing examination papers. Several approaches were proposed to automatically generate multiple-choice questions for vocalbuary assessment or grammar exercises. However, most of these studies focused on generating questions in English with a certain similarity strategy. This paper presents a mixed similarity strategy which generates Chinese multiple choice distractors with a statistical regression model including orthographic, phonological and semantic features, i.e., features that were shown in previous psycholinguistics studies to contribute to character recognition. In a first experiment, we evaluated the predictive power of the proposed features in measuring Chinese character similarity. One of the significant experimental results showed that the combination of the four proposed categories of features (structure, semantic radical, stroke and meaning) accounts for 62.5 percent of the variance in the human judgments of character similarity. In the second experiment, a user study was conducted to evaluate the quality of system-generated questions using a test item analysis method. Two hundred ninety-six Chinese primary school students (10-11-year-old) participated in this study. We have compared the mixed strategy with another three common distractor generation strategies, orthographic strategy, semantic strategy, and phonological strategy. One of important findings suggested that the mixed strategy significantly outperformed other three strategies in terms of the distractor usefulness and has a highest discrimination power among four strategies.

Student question generation (SQG) is an effective strategy for improving reading comprehension. It helps students improve their understanding of reading materials, metacognitively monitor their comprehension, and self-correct comprehension gaps. Internet technologies have been used to facilitate student question generation process through intensive peer support. However, the availability, level of task commitment, and capabilities of student peers have emerged as significant concerns, particularly in light of the global pandemic and the subsequent post-pandemic era. Thus, this article presents a student-Artificial Intelligence (AI) co-creation tool called CoAsker for supporting question generation. Following recent Human Computer Interaction (HCI) research in human-AI collaborative writing, CoAsker first allows students to provide question clues and answers and then uses a state-of-the-art pre-trained language model, T5-PEGASUS, to generate questions. Finally, the student can use this AI question directly or perform reflection by comparing his or her questions with the AI question. An empirical study was conducted to examine the quality of AI questions and the effect of this tool on student engagement and reading comprehension. The results of the study shows that students using this tool (treatment) were more engaged in generating low-level cognitive questions and performed better in acquiring knowledge than those using a traditional online question generation tool (control). These results indicate that student-AI question co-creation is beneficial to SQG training and educational assessment for reading comprehension, such as repeated practices.