Educational question generation (EQG) is a crucial component of intelligent educational systems, significantly aiding self-assessment, active learning, and personalized education. While EQG systems have emerged, existing datasets typically rely on predefined, carefully edited texts, failing to represent real-world classroom content, including lecture speech with a set of complementary slides. To bridge this gap, we collect a dataset of educational questions based on videos from real-world lectures. On this realistic dataset, we find that current methods for EQG struggle to accurately generate questions from educational videos, particularly in aligning with specific timestamps and target answers. Common challenges include selecting informative contexts from extensive transcripts and ensuring generated questions meaningfully incorporate the target answer. To address the challenges, we introduce a novel framework utilizing large language models (LLMs) for dynamically selecting and rewriting contexts based on target timestamps and answers in lecture videos. First, our framework selects contexts from both lecture transcripts and video keyframes based on answer relevance and temporal proximity. Then, we integrate the contexts selected from both modalities and rewrite them into answer-containing knowledge statements, to enhance the logical connection between the contexts and the desired answer. Quantitative evaluation and human evaluation show that our approach improves the quality and relevance of the generated questions.

While the Question Generation (QG) task has been increasingly adopted in educational assessments, its evaluation remains limited by approaches that lack a clear connection to the educational values of test items.In this work, we introduce test item analysis, a method frequently used by educators to assess test question quality, into QG evaluation.Specifically, we construct pairs of candidate questions that differ in quality across dimensions such as topic coverage, item difficulty, item discrimination, and distractor efficiency.We then examine whether existing QG evaluation approaches can effectively distinguish these differences.Our findings reveal significant shortcomings in these approaches with respect to accurately assessing test item quality in relation to student performance.To address this gap, we propose a novel QG evaluation framework, QG-SMS, which leverages Large Language Model for Student Modeling and Simulation to perform test item analysis.As demonstrated in our extensive experiments and human evaluation study, the additional perspectives introduced by the simulated student profiles lead to a more effective and robust assessment of test items.

Developing novel research questions (RQs) often requires extensive literature reviews, especially in interdisciplinary fields. To support RQ development through human-AI co-creation, we leveraged Large Language Models (LLMs) to build an LLM-based agent system named CoQuest. We conducted an experiment with 20 HCI researchers to examine the impact of two interaction designs: breadth-first and depth-first RQ generation. The findings revealed that participants perceived the breadth-first approach as more creative and trustworthy upon task completion. Conversely, during the task, participants considered the depth-first generated RQs as more creative. Additionally, we discovered that AI processing delays allowed users to reflect on multiple RQs simultaneously, leading to a higher quantity of generated RQs and an enhanced sense of control. Our work makes both theoretical and practical contributions by proposing and evaluating a mental model for human-AI co-creation of RQs. We also address potential ethical issues, such as biases and over-reliance on AI, advocating for using the system to improve human research creativity rather than automating scientific inquiry. The system’s source is available at: https://github.com/yiren-liu/coquest.

Multi-task learning (MTL) has become increasingly popular in natural language processing (NLP) because it improves the performance of related tasks by exploiting their commonalities and differences.Nevertheless, it is still not understood very well how multi-task learning can be implemented based on the relatedness of training tasks.In this survey, we review recent advances of multi-task learning methods in NLP, with the aim of summarizing them into two general multi-task training methods based on their task relatedness: (i) joint training and (ii) multi-step training.We present examples in various NLP downstream applications, summarize the task relationships and discuss future directions of this promising topic.

Comparative reasoning is a process of comparing objects, concepts, or entities to draw conclusions, which constitutes a fundamental cognitive ability.In this paper, we propose a novel framework to pre-train language models for enhancing their abilities of comparative reasoning over texts.While there have been approaches for NLP tasks that require comparative reasoning, they suffer from costly manual data labeling and limited generalizability to different tasks.Our approach introduces a novel method of collecting scalable data for text-based entity comparison, which leverages both structured and unstructured data.Moreover, we present a framework of pre-training language models via three novel objectives on comparative reasoning.Evaluation on downstream tasks including comparative question answering, question generation, and summarization shows that our pre-training framework significantly improves the comparative reasoning abilities of language models, especially under low-resource conditions.This work also releases the first integrated benchmark for comparative reasoning.

Wearable sensors demand flexible and concealable devices that can be seamlessly integrated with apparels and clothes. E-textile sensors and devices, built from specially functionalized fibers and yarns and constructed with embroidery and woven processes, provide an ideal solution for different wearable applications. In this article, we propose a textile-based, embroidered passive strain and displacement sensor functioned as a UHF RFID antenna that can be aesthetically integrated with garment fabrics. The antenna structure consists of a dipole arm and a coupling loop mounted with an RFID chip. The displacement between the loop and dipole arm will alter the impedance of the antenna and consequently modulate the backscattered signals from the RFID chip to the reader. The sensor is passive, i.e., no battery is needed on the sensor and the sensing information can be wirelessly retrieved by the reader from a few meters away. The structure is sensitive to strains and displacement caused by body movement. Our experiments demonstrate different applications for this sensor, i.e., when the sensor is embroidered on different locations on human body, such as breast, knees, and elbows, it can be used as a respiration monitor, gesture indicator, etc. to conveniently track and log various forms of body movements.

Citing, quoting, and forwarding & commenting behaviors are widely seen in academia, news media, and social media. Existing behavior modeling approaches focused on mining content and describing preferences of authors, speakers, and users. However, behavioral intention plays an important role in generating content on the platforms. In this work, we propose to identify the referential intention which motivates the action of using the referred (e.g., cited, quoted, and retweeted) source and content to support their claims. We adopt a theory in sociology to develop a schema of four types of intentions. The challenge lies in the heterogeneity of observed contextual information surrounding the referential behavior, such as referred content (e.g., a cited paper), local context (e.g., the sentence citing the paper), neighboring context (e.g., the former and latter sentences), and network context (e.g., the academic network of authors, affiliations, and keywords). We propose a new neural framework with Interactive Hierarchical Attention (IHA) to identify the intention of referential behavior by properly aggregating the heterogeneous contexts. Experiments demonstrate that the proposed method can effectively identify the type of intention of citing behaviors (on academic data) and retweeting behaviors (on Twitter). And learning the heterogeneous contexts collectively can improve the performance. This work opens a door for understanding content generation from a fundamental perspective of behavior sciences.