his research-in-progress paper investigates the effects of repeated generative AI use on cognitive laziness and learning performance in digital literacy education. While AI tools such as ChatGPT can support learning by providing structured guidance and immediate feedback, frequent reliance may reduce mental effort and hinder long-term knowledge retention. Grounded in Cognitive Load Theory and Bloom's Taxonomy, this study adopts a quasi-experimental longitudinal design with pretest, learning phase, posttest, and delayed retention assessment. Students complete weekly tasks with or without AI assistance, allowing comparisons of performance, engagement, and reliance patterns over time. By analyzing ChatGPT interaction logs alongside learning outcomes, the study examines whether generative AI promotes deep learning or fosters cognitive offloading. The findings aim to inform instructional strategies that integrate AI tools without displacing cognitive effort.

Wearable devices provide reliable biometric measurements in different contexts, and AI algorithms are increasingly being used to analyze this data. The objective of this review is to examine the use of wearable devices to collect biometric data combined with AI algorithms in an educational setting. A systematic review was conducted through the PRISMA methodology, by searching the Scopus database for works that included wearables, biometrics, and AI algorithms. A total of 43 studies were included and examined. The objectives, the type of collected data, and the methodologies of the included studies were investigated. Most articles utilized machine learning and deep learning algorithms for classification tasks, such as detecting stress or attention. Other applications included human activity recognition (HAR) for classroom orchestration and emotional or cognitive state detection. Many of the studies applied knowledge from previous works to the educational context, resembling exploratory research. Conversely, some authors developed tasks and methodologies tailored to the educational context. The strengths and weaknesses of the presented studies were discussed to propose future research directions. The main findings of this review highlight the advantages of the combination of multimodal sensing and predictive modeling in education with the eventual prospect of personalization. The absence of standardized acquisition and reporting remains the main barrier to replication, benchmarking, and synthesis across studies.