The application of learning sciences with technology has been shown to boost learner interactions, yet the potential of advanced tool, particularly those that leverage Natural Language Processing (NLP), still very much untapped in learning contexts. This paper speaks to this age-old problem of generating quality Multiple-Choice questions (MCQs) – a prevalent but time-consuming mode of assessment – via the suggested comprehensive comparison study of template-based AI solutions. The study contrasts general-purpose Large Language Models (LLMs) with specialized MCQ-focused AI programs. The scientific approach employed was quite stringent, where each of the software applications was benchmarked using a common dataset of text across varying levels of complexity and topic. Results indicate that general-purpose LLMs, especially DeepSeek and ChatGPT, consistently present higher performance and reliability, especially when processing complex textual content. Whereas specialized tools offer distinctive formatting options, they exhibit decreasing performance as texts become more complex and signify strong operation constriction at the free versions. Developing solid and effective distractors turned out to be a complicated task for all the tested tools. We conclude the paper by presenting a standardized assessment model, making evidence-based recommendations for developers and teachers, and suggesting ways to incorporate various AI capabilities into modern educational assessment effectively.

Maternal childbirth experiences are crucial indicators of health care quality, patient satisfaction, and emotional health. The increasing use of social media platforms, such as Facebook, provides a unique opportunity to examine public sentiment and narratives around maternity care. However, limited studies have employed deep learning (DL)–based sentiment analysis (SA) to comprehensively analyze childbirth experiences in low-resource environments. This study utilizes a hybrid technique that integrates unsupervised topic modeling with supervised DL sentiment classification to capture both thematic breadth and emotional tone of birthing experiences. A dataset of Facebook comments was preprocessed and analyzed using word frequency analysis, latent Dirichlet allocation (LDA) for thematic extraction, sentiment classification using convolutional neural networks (CNNs), and robustly optimized BERT pretraining approach (RoBERTa). Statistical correlations between sentiment polarity and hospital service variables were examined using the chi-square test. The word frequency analysis revealed significant themes such as maternal care, labor and childbirth experiences, informal childbirth discussions, spiritual beliefs, and personal childbirth stories. SA found a majority positive sentiment (5106 instances) with widespread sentiments of trust and joy, although considerable occurrences of fear (1,851), sadness (1,564), and anger (1,114) indicated traumatic delivery experiences. The CNN model outperformed RoBERTa, which had an AUC of 0.9988 and an accuracy of 87%. Statistical study (chi-square test, p < 0.0001) showed a significant correlation between sentiment polarity and hospital service variables, indicating the influence of treatment quality on patient views. This study emphasizes the significance of SA in assessing maternal health care experiences and improving hospital practices. The findings highlight the need of increased communication, empathetic midwifery care, and patient-centered approaches in addressing unfavorable childbirth experiences. This study provides policymakers with data-driven insights to improve maternal health care policies, increase patient experiences, and ensure valuable maternity services.