For philosophers, mentions of the names of other philosophers and scientists are an important indicator of relevance and influence. However, they don’t always come in neat citations, especially in older works. We evaluate various approaches to named entity recognition for person names in 20th century, English-language philosophical texts. We use part of a digitized corpus of the works of W.V. Quine, manually annotated for person names, to compare the performance of several systems: the rule-based edhiphy, spaCy’s CNN-based system, FLAIR’s BiLSTM-based system, and SpanBERT, ERNIE-v2 and ModernBERT’s transformer-based approaches. We also experiment with enhancing the smaller models with domain-specific embedding vectors. We find that both spaCy and FLAIR outperform transformer-based models, perhaps due to the small dataset sizes involved.

This preliminary study aims to investigate whether AI, when prompted based on individual learning styles, can effectively improve comprehension and learning experiences in educational settings. It involves tailoring LLMs baseline prompts and comparing the results of a control group receiving standard content and an experimental group receiving learning style-tailored content. Preliminary results suggest that GPT-4 can generate responses aligned with various learning styles, indicating the potential for enhanced engagement and comprehension. However, these results also reveal challenges, including the model’s tendency for sycophantic behavior and variability in responses. Our findings suggest that a more sophisticated prompt engineering approach is required for integrating AI into education (AIEd) to improve educational outcomes.