This qualitative study investigates the impact of generative artificial intelligence (GenAI) on the emotional engagement, motivation and well-being of first-year university students in Hong Kong. We conducted semi-structured interviews with 21 students and three instructors to explore their perceptions of how GenAI influences the affective dimensions of language learning. The data were analyzed using manual coding and inductive thematic analysis to identify key themes. The findings revealed that GenAI generally enhances students’ motivation, reduces anxiety and stress, and fosters an emotionally supportive learning environment. However, challenges related to cultural context and technical issues were also identified. The study highlights the pivotal role of instructors in shaping students’ experiences with GenAI and underscores the need for ongoing support and professional development. It also demonstrates the importance of cultural sensitivity, technological infrastructure and balance. The study is valuable for those who aim to harness GenAI while preserving the irreplaceable human elements of teaching. It contributes to the growing body of knowledge on integrating AI in language learning. Copyright © 2025 The Authors. Published by Elsevier Ltd.

Computational thinking (CT) practices is a critical cognitive tool for students to overcome challenges in this technological era. Few studies investigated CT practices from a development perspective. A two-year intervention was implemented in 30 intervention and 22 control schools in Hong Kong to promote CT education (T1baseline: N = 13,056; T2: N = 16,367; and T3: N = 15,269). Senior primary students (grades 4 to 6) around 8–12 years old were targeted. Among them, 34% were girls at T1baseline. Their learning progression of cognitive development was assessed with a CT practices test. Data were analyzed using item response analysis. Learning trajectories of CT practices were modeled to understand how students’ cognitive development differed between schools. Insights of findings were discussed in bringing CT practices to life. Copyright © 2023 Elsevier Ltd. All rights reserved.

Computational thinking (CT) practices have gained increasing importance in primary school education. However, there are few tools to measure primary school students' CT practices that are based on rigorous psychometric approaches. This study developed an operational definition for CT practices by exploring the key dimensions of their composition and, more importantly, developed and validated a generic test tool that can effectively assess students’ CT practices. Items to measure CT practices were developed using the evidence-centered design (ECD) approach. These items prompted students to exhibit behaviors that demonstrated their skills in the four dimensions of CT practice, namely testing and debugging, algorithmic thinking, reusing and remixing, and abstracting and modularizing. A test consisting of these items was administered to 13,956 Grade 4–6 primary school students from 56 public schools in Hong Kong. The data were analyzed based on the item response theory (IRT) approach, together with other psychometric tests. A bi-factor three-parameter logistic model was ultimately adopted for test validation, as it fitted the data better than the corresponding two-parameter and one-parameter logistic models. The results validated the four-dimensional structure of the CT practices test. The reliability, test item characteristics, and CT practice abilities of students are also discussed. Copyright © 2021 Elsevier Ltd. All rights reserved.