Massive Open Online Courses (MOOCs) have a great potential for sustainable education. Millions of learners annually enrol on MOOCs designed to meet the needs of an increasingly diverse and international student population. Participants’ backgrounds vary by factors including age, education, location, and first language. MOOC authors address consequent needs by ensuring courses are well-organised. Learning is structured into discrete steps, prioritising clear communication; video components incorporate subtitles. Variability in participants’ language abilities inevitably create barriers to learning, a problem most extreme for those studying in a language which is not their first. This paper investigates how to identify ESL participants and how best to predict factors associated with their course completion. This study proposes a novel method for automatically categorising (English as Primary and Official Language; English as Official but not Primary Language; and English as a second Language groups) 25,598 participants studying FutureLearn “Understanding Language: Learning and Teaching” MOOC using natural language processing. We compared algorithms’ performance when extracting discernible features in participants’ engagement. Engagement in discussions at the end of the first week is one of the strongest predictive features, while overall, learner behaviours in the first two weeks were identified as the most strongly predictive feature.

Sentiment analysis is essential for understanding consumer opinions, yet selecting the optimal models and embedding methods remains challenging, especially when handling ambiguous expressions, slang, or mismatched sentiment–rating pairs. This study provides a comprehensive comparative evaluation of sentiment classification models across three paradigms: traditional machine learning, pre-transformer deep learning, and transformer-based models. Using the Amazon Magazine Subscriptions 2023 dataset, we evaluate a range of embedding techniques, including static embeddings (GloVe, FastText) and contextual transformer embeddings (BERT, DistilBERT, etc.). To capture predictive confidence and model uncertainty, we include categorical cross-entropy as a key evaluation metric alongside accuracy, precision, recall, and F1-score. In addition to detailed quantitative comparisons, we conduct a systematic qualitative analysis of misclassified samples to reveal model-specific patterns of uncertainty. Our findings show that FastText consistently outperforms GloVe in both traditional and LSTM-based models, particularly in recall, due to its subword-level semantic richness. Transformer-based models demonstrate superior contextual understanding and achieve the highest accuracy (92%) and lowest cross-entropy loss (0.25) with DistilBERT, indicating well-calibrated predictions. To validate the generalisability of our results, we replicated our experiments on the Amazon Gift Card Reviews dataset, where similar trends were observed. We also adopt a resource-aware approach by reducing the dataset size from 25 K to 20 K to reflect real-world hardware constraints. This study contributes to both sentiment analysis and sustainable AI by offering a scalable, entropy-aware evaluation framework that supports informed, context-sensitive model selection for practical applications.

The vision of 6G requires autonomous, intelligent, and adaptive systems that can manage complex and dynamic communication environments. In this context, artificial intelligence (AI)-based agents are seen as key enablers, combining perception, knowledge, decision, and execution functions to operate across distributed network layers. This study provides a comprehensive overview of agentic systems for 6G, examining their architecture, core capabilities, and enabling AI techniques. We propose a novel framework for agentic intelligence levels which conceptualizes agentic AI systems as a gradual and multidimensional spectrum. Additionally, trust and governance levels, which characterize system deployability independently of agentic competencies, are introduced. We analyze how methods such as reinforcement learning (RL), federated learning, graph neural networks, transfer learning (TL), and meta-learning can be integrated into agents to support tasks including network optimization, predictive traffic management, spectrum allocation, energy-efficient resource control, and security enhancement. The potential of foundation model-based agents in 6G networks is examined, comparing them to classical RL-based agents to highlight their complementarity. Practical 6G use cases—including smart resource orchestration, environmental coordination, industrial applications, reliability and security assurance, and situational awareness—are presented to demonstrate the applicability of agents in real-world scenarios. Their strengths, limitations, and complementary roles within agent-based frameworks are highlighted. The current technological and industrial maturity levels of use cases are mapped to the proposed levels of agentic intelligence and trust/governance. The study bridges theoretical advances and practical 6G implementations by linking AI methods with concrete deployment challenges. Finally, the study identifies challenges related to scalability, interoperability, explainability, and trust, and highlights future directions for building reliable, adaptive, and ethically aligned agent-based systems in 6G.