Artificial Intelligence in Education (AIED) is becoming increasingly influential in the educational sphere, offering significant benefits and presenting ethical risks. This study fills a crucial gap by systematically classifying and analyzing these risks. Using a combined approach of systematic review and grounded theory coding, ethical risks were categorized into three dimensions: technology, education, and society. In the technology dimension, risks include privacy invasion, data leakage, algorithmic bias, the black box algorithm, and algorithmic error. The education dimension risks involve student homogenized development, homogeneous teaching, teaching profession crisis, deviation from educational goals, alienation of the teacher-student relationship, emotional disruption, and academic misconduct. Risks in the society dimension consist of exacerbating the digital divide, the absence of accountability, and a conflict of interest. Based on an analysis of the types, potential triggers, and hazards associated with these risks, we propose strategies spanning three critical dimensions—technology, education, and society, from the perspectives of stakeholders, to address these ethical risks. This study contributes to a concise and precise analysis of the ethical risks associated with AIED, offering practical solutions for the responsible implementation of AIED.

Game-based learning (GBL) can allow learners to acquire and construct knowledge in a fun and focused learning atmosphere. A systematic literature review of 42 papers from 2010 to 2020 in this study showed that the current difficulties in implementing GBL in classrooms could be classified into the following categories: infrastructure, resources, theoretical guidance, teacher’s capabilities and acceptance of GBL. In order to solve the above problems, the study constructs a technology enhanced GBL model, from the four parts of learning objective, learning process, learning evaluation, and smart classroom. In addition, this study adopted the Delphi method, inviting a total of 29 scholars, experts, teachers and school managers to explore how to implement GBL in smart classrooms. Finally, the technology enhanced GBL model was validated and the utilization approaches were provided at the conclusion part.

Smart classroom was regarded as one of the essential methods to transform the traditional instructional mode. The research and practice of smart classroom became popular since 2012. However, there were few research on the large scale survey of smart classroom from the both technological and pedagogical perspective to provide baselines for smart classroom building. In this study, a large-scale survey of smart classroom was conducted in China. A total of 13, 495 students from 135 primary and middle schools participated in the survey. Data were analyzed in SPSS 23. Results indicated that participants generally did not perceive much smart learning in classrooms, especially in the dimension of resource and enhancement. The results also confirmed that digital device and Internet were the basis for equipping smart classroom. Technology equipment plus advanced technology could lead to the success of learning. Future research could be conducted to investigate how to match technologies, pedagogies and smart classroom to improve student’s achievements.

A smart learning environment, featuring personalization, real-time feedback, and intelligent interaction, provides the primary conditions for actively participating in online education. Identifying the factors that influence active online learning in a smart learning environment is critical for proposing targeted improvement strategies and enhancing their active online learning effectiveness. This study constructs the research framework of active online learning with theories of learning satisfaction, the Technology Acceptance Model (TAM), and a smart learning environment. We hypothesize that the following factors will influence active online learning: Typical characteristics of a smart learning environment, perceived usefulness and ease of use, social isolation, learning expectations, and complaints. A total of 528 valid questionnaires were collected through online platforms. The partial least squares structural equation modeling (PLS-SEM) analysis using SmartPLS 3 found that: (1) The personalization, intelligent interaction, and real-time feedback of the smart learning environment all have a positive impact on active online learning; (2) the perceived ease of use and perceived usefulness in the technology acceptance model (TAM) positively affect active online learning; (3) innovatively discovered some new variables that affect active online learning: Learning expectations positively impact active online learning, while learning complaints and social isolation negatively affect active online learning. Based on the results, this study proposes the online smart teaching model and discusses how to promote active online learning in a smart environment.