This paper introduces an intelligent lecturing assistant (ILA) system that utilizes a knowledge graph to represent course content and optimal pedagogical strategies. The system is designed to support instructors in enhancing student learning through real-time analysis of voice, content, and teaching methods. As an initial investigation, we present a case study on lecture voice sentiment analysis, in which we developed a training set comprising over 3,000 1-minute lecture voice clips. Each clip was manually labeled as either engaging or non-engaging. Utilizing this dataset, we constructed and evaluated several classification models based on a variety of features extracted from the voice clips. The results demonstrate promising performance, achieving an F1-score of 90% for boring lectures on an independent set of over 800 test voice clips. This case study lays the groundwork for the development of a more sophisticated model that will integrate content analysis and pedagogical practices. Our ultimate goal is to aid instructors in teaching more engagingly and effectively by leveraging modern artificial intelligence and big data techniques.

The triples of a knowledge graph (KG), comprised of a subject-object pair and a predicate linking the two, share common structural properties to natural language (NL) sentences. Recent advances in data compression via neural networks, specifically low-dimensional embeddings, have been applied to both data domains (KG and NL), yet little work has been done to explore how these two different representations may be linked together in an attempt to align the knowledge of one domain to another. In this work, we develop an unsupervised deep learning methodology for such alignment using tools of optimal transport and the Wasserstein distances between the respective representations. Due to the size and complexities of each embedding space, we introduce a novel process to improve scalability and reduce training time we call Batched Information Guided Optimal Transport (BIG-OT). We experiment on two datasets (NYT-Freebase and Wikidata) without using the supervision of aligned pairs and show that our solution can outperform the current benchmarks while only operating on sub-samples of each respective space, thus providing gains in algorithmic efficiency. Our algorithm scales to Big Data use cases regardless of the baseline embedding methodologies utilized, and we additionally show a simple procedure to improve those embeddings again leads to performance gains.

Building a knowledge graph is a time-consuming and costly process which often applies complex natural language processing (NLP) methods for extracting knowledge graph triples from text corpora. Pre-trained large Language Models (PLM) have emerged as a crucial type of approach that provides readily available knowledge for a range of AI applications. However, it is unclear whether it is feasible to construct domain-specific knowledge graphs from PLMs. Motivated by the capacity of knowledge graphs to accelerate data-driven materials discovery, we explored a set of state-of-the-art pre-trained general-purpose and domain-specific language models to extract knowledge triples for metal-organic frameworks (MOFs). We created a knowledge graph benchmark with 7 relations for 1248 published MOF synonyms. Our experimental results showed that domain-specific PLMs consistently outperformed the general-purpose PLMs for predicting MOF related triples. The overall benchmarking results, however, show that using the present PLMs to create domain-specific knowledge graphs is still far from being practical, motivating the need to develop more capable and knowledgeable pre-trained language models for particular applications in materials science.