Higher education has witnessed a massive transformation due to the advent of generative artificial intelligence (AI) technologies such as ChatGPT. In essence, AI has transformed various aspects of society as a whole. Despite the growing interest in applying AI tools such as ChatGPT in higher education, there is limited understanding of lecturers’ perceptions regarding their use in teaching and learning contexts. Studies reported in the literature have not comprehensively explored lecturers’ attitudes towards AI adoption, particularly in terms of its impact on classroom activities, assessment, and feedback. This study aims to fill this gap by qualitatively studying lecturers’ perspectives. The findings reveal that the advent of AI was met with mixed feelings among lecturers. Some lecturers embraced AI technologies and developed mechanisms for utilizing them in the classroom, while others resisted the change. This research is significant, as it can inform best practices and guide future implementation strategies of technologies in education.

Educational data mining (EDM) is instrumental in the early detection of students at risk of academic underperformance, enabling timely and targeted interventions. Given that many undergraduate students face challenges leading to high failure and dropout rates, utilizing EDM to analyze student data becomes crucial. By predicting academic success and identifying at-risk individuals, EDM provides a data-driven approach to enhance student performance. However, accurately predicting student performance is challenging, as it depends on multiple factors, including academic history, behavioral patterns, and health-related metrics. This study aims to bridge this gap by proposing a deep learning model to predict student academic performance with greater accuracy. The approach combines a convolutional neural network (CNN) and a bidirectional gated recurrent unit (BiGRU) network to enhance predictive capabilities. To improve the model’s performance, we address key data preprocessing challenges, including handling missing data, addressing class imbalance, and selecting relevant features. Additionally, we incorporate optimization techniques to fine-tune hyperparameters to determine the best model architecture. Using key performance metrics such as accuracy, precision, recall, and F-score, our experimental results show that our proposed model achieves improved prediction accuracy of 97.48%, 90.90%, and 95.97% across the three datasets.

TikTok has rapidly become a leading platform for youth self-expression and digital participation, yet little is known about how gender influences motivations for content creation. This study explores gender-based differences among South African university students, addressing a key gap in the literature. Using a mixed-methods design, 100 students (50 male, 50 female) were surveyed and focus groups with 25 participants were conducted to examine content types and motivational drivers, over a three-month period. Findings reveal clear distinctions: female students are primarily motivated by self-expression, social connection, and stress relief, favoring fashion, lifestyle, and dance content. Male students, by contrast, focus on entertainment, follower growth, and raising awareness, often creating comedy and educational videos. These patterns reflect broader social norms and platform dynamics, emphasizing different gratifications by gender. The study is guided by the Uses and Gratifications Theory in understanding these gendered motivations is essential for fostering more inclusive and responsive social media environments. It offers practical insights for educators designing digital literacy interventions, as well as for platform developers aiming to enhance inclusivity and user engagement.