To measure the feasibility of AI chatbots in K-12 education, the perspective of both teachers and students regarding the matter should be considered. There are a number of surveys capturing academic perspective of AI chatbots but none of them are focused on K-12 education. Further, they only cover student perspective. We summarized the perspective of K-12 teachers and students via two questionnaire surveys with 75 teachers (44 primary school and 36 high school) and 433 students (242 primary school and 191 high school). K-12 teachers were moderately aware about AI chatbots and somewhat encouraged the use. High school students had similar level of awareness but primary school students were less aware about it. Policies for dealing with AI misuses need to be clearly defined and employed.

When checking student programs for plagiarism and collusion, many similarity detectors aim to capture semantic similarity. However, they are not particularly effective for strongly directed assessments, in which the student programs are expected to be semantically similar. A detector focusing on syntactic similarity might be useful, and this paper reports its effectiveness on programming assessment tasks collected from algorithms and data structures courses in one academic semester. Our study shows that syntactic similarity detection is more effective than its semantic counterpart in strongly directed assessments, with some irregular similarity patterns being useful for raising suspicion. We also tested whether take-home assessments have higher similarity than in-class assessments, and confirmed that hypothesis. Consistency of the findings will be further validated on other courses with strongly directed assessments, and a syntactic similarity detector specifically tailored for strongly directed assessments will be proposed.

Covid-19 pandemic greatly affects student daily life. Instead of physically attend classes, they need to meet the lecturer and learn the course material via online meeting platform. The transition somehow introduces some issues like the difficulty of maintaining their focus. This becomes worse for computing students given that the assessments are not limited to standard essays. They include programming and hardware-based assessments which are more difficult to complete at home as students might not have the required software or hardware. This paper reports any issues experienced by 112 computing students in terms of transitioning from offline to online learning. Our study shows that online learning forces the students to allocate more time to study and complete the assessments. Online learning also introduces other issues like higher stress level but still has a few of positive traits like spending less money to physically attend the classes. Many students argue that programming is the most difficult subject to learn in online environment. In response to the issues, some suggestions are provided for computing lecturers.