We introduce a system that leverages large language models (LLMs) to produce learning objectives, instructional text, assessments, pro­jects, and auto-graders from a minimal course description. Quality is ensured through a hierarchical generation strategy, schema enforcement with JSON function-calling, and iterative refinement via instructor and student feedback. Tested on a graduate-level data science course and delivered on an online platform with auto-graded coding projects, the system produced full modules in under an hour.

Multimodal summarization with multimodal output (MSMO) has emerged as a promising research direction. Nonetheless, numerous limitations exist within existing public MSMO datasets, including insufficient maintenance, data inaccessibility, limited size, and the absence of proper categorization, which pose significant challenges. To address these challenges and provide a comprehensive dataset for this new direction, we have meticulously curated the MMSum dataset. Our new dataset features (1) Human-validated summaries for both video and textual content, providing superior human instruction and labels for mul-timodal learning. (2) Comprehensively and meticulously arranged categorization, spanning 17 principal categories and 170 subcategories to encapsulate a diverse array of real-world scenarios. (3) Benchmark tests performed on the proposed dataset to assess various tasks and methods, including video summarization, text summarization, and multimodal summarization. To champion accessibility and collaboration, we released the MMSum dataset and the data collection tool as fully open-source resources, fostering transparency and accelerating future developments, at https://mmsum-dataset.github.io/.

We conduct a preliminary study of the effect of GPT's temperature parameter on the diversity of GPT4-generated questions. We find that using higher temperature values leads to significantly higher diversity, with different temperatures exposing different types of similarity between generated sets of questions. We also demonstrate that diverse question generation is especially difficult for questions targeting lower levels of Bloom's Taxonomy.