The utilization of Generative AI (GenAI) in higher education classrooms has significantly increased in recent years. Studies show that GenAI holds promise in impacting the learning experiences of both students and teachers, offering personalized learning and assessment opportunities. This study conducts a systematic review of the responses, attitudes, and behaviors related to the application of GenAI within higher education classrooms. To this end, we synthesized 99 papers published between 2020 and August 2024, focusing on the utilization of GenAI in higher education settings. The analysis addresses three key inquiries: responses, attitudes, and behaviors. This systematic review provides an updated understanding from psychological perspectives of GenAI’s role in the teaching and learning processes of higher education, with a particular emphasis on GenAI technologies.

Large language model (LLM) tools, such as ChatGPT, are rapidly transforming engineering education by enhancing tasks like information retrieval, coding, and writing refinement, which are critical to the problem-solving and technical focus of engineering disciplines. This study investigates how engineering students use LLM tools and the challenges they face, offering insights into the adoption of AI technologies in academic settings. A survey of 539 engineering students from 12 leading Chinese universities, using the UTAUT framework, examines factors such as technological expectations, environmental support, and personal characteristics. The key findings include the following: (1) Over 40% of engineering students use LLM tools, with 18.8% regarding them as indispensable. (2) Trust in AI-generated content remains a central challenge, as students must critically evaluate its accuracy and reliability. (3) Environmental support significantly affects usage, with notable regional disparities, particularly between eastern and other regions in China. (4) A persistent digital divide, influenced by gender, academic level, and socioeconomic background, affects the depth and effectiveness of tool use. These results underscore the need for targeted support to address regional and demographic disparities and optimize LLM tool integration in engineering education.

Intercultural Communicative Competence (ICC) is a crucial issue for the internationalization of higher education. In recent years, intercultural online learning has gradually become a new form of internationalization in higher education, which presents both challenges and opportunities for enhancing students' ICC. This study constructs an analytical framework for ICC within the context of online learning, drawing from existing research on ICC and classroom environment theory. Leveraging the "Global Integrated Classroom Learning Survey" by Tsinghua University's Global Integrated Classroom Teaching Research Group, 141 valid samples were collected and analyzed through descriptive statistics and regression analysis. Two main conclusions were drawn: (1) Students generally exhibit a relatively high level of ICC in online learning contexts, yet differences in ICC exist among different student groups; (2) Within online teaching, three contextual features—information literacy, instructional scaffolding, and technological support—significantly influence university students' ICC.

Student modeling, the task of inferring a student's learning characteristics through their interactions with coursework, is a fundamental issue in intelligent education. Although the recent attempts from knowledge tracing and cognitive diagnosis propose several promising directions for improving the usability and effectiveness of current models, the existing public datasets are still insufficient to meet the need for these potential solutions due to their ignorance of complete exercising contexts, fine-grained concepts, and cognitive labels. In this paper, we present MoocRadar, a fine-grained, multi-aspect knowledge repository consisting of 2,513 exercise questions, 5,600 knowledge concepts, and over 12 million behavioral records. Specifically, we propose a framework to guarantee a high-quality and comprehensive annotation of fine-grained concepts and cognitive labels. The statistical and experimental results indicate that our dataset provides the basis for the future improvements of existing methods. Moreover, to support the convenient usage for researchers, we release a set of tools for data querying, model adaption, and even the extension of our repository, which are now available at https://github.com/THU-KEG/MOOC-Radar.

Learners participating in Massive Open Online Courses (MOOC) have a wide range of backgrounds and motivations. Many MOOC learners enroll in the courses to take a brief look; only a few go through the entire content, and even fewer are able to eventually obtain a certificate. We discovered this phenomenon after having examined 92 courses on both xuetangX and edX platforms. More specifically, we found that the learning coverage in many courses—one of the metrics used to estimate the learners’ active engagement with the online courses—observes a Zipf distribution. We apply the maximum likelihood estimation method to fit the Zipf’s law and test our hypothesis using a chi-square test. In the xuetangX dataset, the learning coverage in 53 of 76 courses fits Zipf’s law, but in all of 16 courses on the edX platform, the learning coverage rejects the Zipf’s law. The result from our study is expected to bring insight to the unique learning behavior on MOOC.