Background: In the midst of digital transformation, schools are transforming their classrooms as they prepare students for a world increasingly automated by new technologies, including artificial intelligence (AI). During curricular implementation, it has not made sense to teachers to teach AI as a stand-alone subject as it is not a traditional discipline in schools. As such, subject matter teachers may need to take on the responsibility of integrating AI content into discipline-based lessons to help students make connections and see its relevance rather than present AI as separate content. This paper reports on a study that piloted a new lesson package in science classrooms to introduce students to the idea of AI. Specifically, the AI-integrated science lesson package, designed by the research team, provided an extended activity that used the same context as an existing lesson activity. Three science teachers from different schools piloted the lesson package with small groups of students and provided feedback on the materials and implementation. Findings: The findings revealed the teachers’ perceptions of integrating AI into science lessons in terms of the connection between AI and science, challenges when implementing the AI lesson package and recommendations on improvements. First, the teachers perceived that AI and science have similarities in developing accurate models with quality data and using simplified reasoning, while they thought that AI and science play complementary roles when solving scientific problems. Second, the teachers thought that the biggest challenge in implementing the lesson package was a lack of confidence in content mastery, while the package would be challenging to get buy-in from teachers regarding curriculum adaptation and targeting the appropriate audience. Considering these challenges, they recommended that comprehensive AI resources be provided to teachers, while this package can be employed for science enrichment programs after-school. Conclusions: The study has implications for curriculum writers who design lesson packages that introduce AI in science classrooms and for science teachers who wish to contribute to the development of AI literacy for teachers and the extension of the range of school science and STEM to students.

Affective computing is an emerging area of education research and has the potential to enhance educational outcomes. Despite the growing number of literature studies, there are still deficiencies and gaps in the domain of affective computing in education. In this study, we systematically review affective computing in the education domain. Methods: We queried four well-known research databases, namely the Web of Science Core Collection, IEEE Xplore, ACM Digital Library, and PubMed, using specific keywords for papers published between January 2010 and July 2023. Various relevant data items are extracted and classified based on a set of 15 extensive research questions. Following the PRISMA 2020 guidelines, a total of 175 studies were selected and reviewed in this work from among 3102 articles screened. The data show an increasing trend in publications within this domain. The most common research purpose involves designing emotion recognition/expression systems. Conventional textual questionnaires remain the most popular channels for affective measurement. Classrooms are identified as the primary research environments; the largest research sample group is university students. Learning domains are mainly associated with science, technology, engineering, and mathematics (STEM) courses. The bibliometric analysis reveals that most publications are affiliated with the USA. The studies are primarily published in journals, with the majority appearing in the Frontiers in Psychology journal. Research gaps, challenges, and potential directions for future research are explored. This review synthesizes current knowledge regarding the application of affective computing in the education sector. This knowledge is useful for future directions to help educational researchers, policymakers, and practitioners deploy affective computing technology to broaden educational practices.

Increasing access to affordable physiological sensors and computing devices has bolstered the development of emotionaware systems that recognize human emotions. However, to date, there has been little research testing machine-driven approaches to recognizing boredom, particularly in learning contexts. To address that, this study tests the automatic recognition of boredom induced during a video lecture using a multimodal recognition system relying on physiological signals. Electroencephalogram (EEG), electrocardiogram (ECG), galvanic skin response (GSR), and eye gaze data were recorded from 84 healthy adults (mean age = 26.90 ± 5.29) as they watched both non-boring and boring educational videos to control and induce boredom. Signal features were extracted from the physiological data and validated using Wilcoxon signed-rank tests prior to evaluating their utility for boredom recognition with three separate machine-learning classification techniques, namely Extreme Gradient Boosting (XGB), Random Forest (RF), and Gradient Boosting (GB), with leave-one-out cross-validation. Both unimodal and multimodal recognition systems were assessed by evaluating model performance using each physiological signal separately or in combination (using decision fusion). Multimodal recognition was found to enhance model performance when compared to any single modality, with the highest average boredom recognition accuracy of 88.56% ± 0.82% recorded for EEG + eye gaze modal fusion with RF.

In pursuing innovation scaling-up in educational contexts, school leaders tend to adopt top-down approaches grounded in the Newtonian paradigm. This paper argues that the scaling-up of pedagogical innovations can be studied using the complexity paradigm regarding innovation scaling-up as a complex adaptive process that is non-linear, adaptive and emerging. Current research methods, while informative, have limitations in dealing with complex adaptive processes that evolve dynamically in temporal and spatial dimensions. To this end, this paper proposes the use of agent-based modelling (ABM) as a complementary method, which is particularly helpful for school leaders in exploring innovation scaling-up patterns in 'what if' scenarios. This paper also cautions that ABM approach can be used to inform school leaders in scaling-up strategy-making, but the simulation outcomes should not be treated at face value.