This study aims to examine the impact of an innovative self-directed professional development (SDPD) model on fostering teachers’ professional development and improving their ability to manage this development independently. A quantitative research method was adopted, involving 60 participants from Almaty State Humanitarian and Pedagogical College No. 2, Almaty, Kazakhstan. Descriptive and inferential statistics were used to assess the SDPD model’s effectiveness, specifically in promoting teacher engagement, adoption of new pedagogical techniques, and improvement in reflective practices. The study findings reveal that teachers, particularly in developing regions, often face challenges in accessing formal professional development programs. The implementation of the SDPD model addresses these barriers by providing teachers with the tools and strategies required for self-improvement, regardless of geographic or economic constraints. The study participants in the pilot phase showed increased engagement with new pedagogical methods, improved reflective practices, and greater adaptability to emerging educational technologies. The algorithmic aspect of the model streamlined the professional development process, while the activity-based approach ensured that learning remained practical and relevant to teachers’ everyday needs. By offering a clear framework for continuous improvement, the model addresses the gaps in formal training access and cultivates a culture of lifelong learning. These findings suggest that the SDPD model can contribute to elevating teaching standards globally, particularly in regions with limited professional development resources.

Developing “New Quality Productive Forces” (NQPFs) has been accepted as a new theory to accelerate the high-quality development in China. In current, China’s high-quality development mainly relies on the traction of the digital economy. In view of this, developing NQPFs in China’s digital economy sector requires locate and remove some obstacles, such as the insufficient utilization of data, inadequate algorithm regulation, the mismatched supply and demand of regional computing power and the immature market environment. As a solution, it is necessary to allocating data property rights in a market-oriented way, establishing a user-centered algorithm governance system, accelerating the establishment of the national integrated computing network, and maintaining fair competition to optimize the market environment.

This study evaluated the performance of several machine learning classifiers—Decision Tree, Random Forest, Logistic Regression, Gradient Boosting, SVM, KNN, and Naive Bayes—for adaptability classification in online and onsite learning environments. Decision Tree and Random Forest models achieved the highest accuracy of 0.833, with balanced precision, recall, and F1-scores, indicating strong, overall performance. In contrast, Naive Bayes, while having the lowest accuracy (0.625), exhibited high recall, making it potentially useful for identifying adaptable students despite lower precision. SHAP (SHapley Additive exPlanations) analysis further identified the most influential features on adaptability classification. IT Resources at the University emerged as the primary factor affecting adaptability, followed by Digital Tools Exposure and Class Scheduling Flexibility. Additionally, Psychological Readiness for Change and Technical Support Availability were impactful, underscoring their importance in engaging students in online learning. These findings illustrate the significance of IT infrastructure and flexible scheduling in fostering adaptability, with implications for enhancing online learning experiences.

The economy, unemployment, and job creation of South Africa heavily depend on the growth of the agricultural sector. With a growing population of 60 million, there are approximately 4 million small-scale farmers (SSF) number, and about 36,000 commercial farmers which serve South Africa. The agricultural sector in South Africa faces challenges such as climate change, lack of access to infrastructure and training, high labour costs, limited access to modern technology, and resource constraints. Precision agriculture (PA) using AI can address many of these issues for small-scale farmers by improving access to technology, reducing production costs, enhancing skills and training, improving data management, and providing better irrigation infrastructure and transport access. However, there is a dearth of research on the application of precision agriculture using artificial intelligence (AI) by small scale farmers (SSF) in South Africa and Africa at large. The preferred reporting items for systematic reviews and meta-analyses (PRISMA) and Bibliometric analysis guidelines were used to investigate the adoption of precision agriculture and its socio-economic implications for small-scale farmers in South Africa or the systematic literature review (SLR) compared various challenges and the use of PA and AI for small-scale farmers. The incorporation of AI-driven PA offers a significant increase in productivity and efficiency. Through a detailed systematic review of existing literature from inception to date, this study examines 182 articles synthesized from two major databases (Scopus and Web of Science). The systematic review was conducted using the machine learning tool R Studio. The study analyzed the literature review articled identified, challenges, and potential societal impact of AI-driven precision agriculture.

To address the escalating online romance scams within telecom fraud, we developed an Adaptive Random Forest Light Gradient Boosting (ARFLGB)-XGBoost early warning system. Our method involves compiling detailed Online Romance Scams (ORS) incident data into a 24-variable dataset, categorized to analyze feature importance with Random Forest and LightGBM models. An innovative adaptive algorithm, the Adaptive Random Forest Light Gradient Boosting, optimizes these features for integration with XGBoost, enhancing early Online romance scams threat detection. Our model showed significant performance improvements over traditional models, with accuracy gains of 3.9%, a 12.5% increase in precision, recall improvement by 5%, an F1 score increase by 5.6%, and a 5.2% increase in Area Under the Curve (AUC). This research highlights the essential role of advanced fraud detection in preserving communication network integrity, contributing to a stable economy and public safety, with implications for policymakers and industry in advancing secure communication infrastructure.