Jiaju Chen, Yuxuan Lu, Xiaojie Wang, Huimin Zeng, Jing Huang, Jiri Gesi, Ying Xu, Bingsheng Yao, Dakuo Wang. Proceedings of the 64th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2026.

Intelligent systems have traditionally been designed as tools rather than collaborators, often lacking critical characteristics that collaboration partnerships require. Recent advances in large language model (LLM) agents open new opportunities for human-LLM-agent collaboration by enabling natural communication and various social and cognitive behaviors. Yet it remains unclear whether principles of computer-mediated collaboration established in HCI and CSCW persist, change, or fail when humans collaborate with LLM agents. To support systematic investigations of these questions, we introduce an open and configurable research platform for HCI researchers1. The platform’s modular design allows seamless adaptation of classic CSCW experiments and manipulation of theory-grounded interaction controls. We demonstrate the platform’s research efficacy and usability through three case studies: (1) two Shape FactoryHidden Profile experiment for information pooling with 16 participants, and (3) a participatory cognitive walkthrough with five HCI researchers to refine workflows of researcher interface for experiment setup and analysis.

Peer Reviewed

Educational inequalities in disadvantaged areas have long been a global concern. While Information and Communication Technologies (ICTs) have shown great potential in addressing this issue, the unique challenges in disadvantaged areas often hinder the practical effectiveness of such technologies. This paper examines live-streaming-based dual-teacher classes (LSDC) through a qualitative study in disadvantaged regions of China. Our findings indicate that, although LSDC offers students in these regions access to high-quality educational resources, its practical implementation is fraught with challenges. Specifically, we foreground the pivotal role of local teachers in mitigating these challenges. Through a series of situated efforts, local teachers contextualize high-quality lectures to the local classroom environment, ensuring the expected educational outcomes. Based on our findings, we argue that greater recognition and support for the situational practices of local teachers is essential for fostering a more equitable, sustainable, and scalable technology-driven educational model in disadvantaged areas.

The integration of Large Language Models (LLMs) into recommender systems has led to substantial performance improvements. However, this often comes at the cost of diminished recommendation diversity, which can negatively impact user satisfaction. To address this issue, controllable recommendation has emerged as a promising approach, allowing users to specify their preferences and receive recommendations that meet their diverse needs. Despite its potential, existing controllable recommender systems frequently rely on simplistic mechanisms, such as a single prompt, to regulate diversity-an approach that falls short of capturing the full complexity of user preferences. In response to these limitations, we propose DLCRec, a novel framework designed to enable fine-grained control over diversity in LLM-based recommendations. Unlike traditional methods, DLCRec adopts a well-designed task decomposition strategy, breaking down the recommendation process into three sequential sub-tasks: genre prediction, genre filling, and item prediction. These sub-tasks are trained independently and inferred sequentially according to user-defined control numbers, ensuring more precise control over diversity. Furthermore, the scarcity and uneven distribution of diversity-related user behavior data pose significant challenges for fine-tuning. To overcome these obstacles, we introduce two data augmentation techniques that enhance the model's robustness to noisy and out-of-distribution data. These techniques expose the model to a broader range of patterns, improving its adaptability in generating recommendations with varying levels of diversity. Our extensive empirical evaluation demonstrates that DLCRec not only provides precise control over diversity but also outperforms state-of-the-art baselines across multiple recommendation scenarios.

Jiaju Chen, Yuxuan Lu, Shao Zhang, Bingsheng Yao, Yuanzhe Dong, Ying Xu, Yunyao Li, Qianwen Wang, Dakuo Wang, Yuling Sun. Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing. 2024.

Recommender systems learn personalized user preferences from user feedback like clicks. However, user feedback is usually biased towards partially observed interests, leaving many users' hidden interests unexplored. Existing approaches typically mitigate the bias, increase recommendation diversity, or use bandit algorithms to balance exploration-exploitation trade-offs. Nevertheless, they fail to consider the potential rewards of recommending different categories of items and lack the global scheduling of allocating top-N recommendations to categories, leading to suboptimal exploration. In this work, we propose an Uplift model-based Recommender (UpliftRec) framework, which regards top-N recommendation as a treatment optimization problem. UpliftRec estimates the treatment effects, i.e., the click-through rate (CTR) under different category exposure ratios, by using observational user feedback. UpliftRec calculates group-level treatment effects to discover users' hidden interests with high CTR rewards and leverages inverse propensity weighting to alleviate confounder bias. Thereafter, UpliftRec adopts a dynamic programming method to calculate the optimal treatment for overall CTR maximization. We implement UpliftRec on different backend models and conduct extensive experiments on three datasets. The empirical results validate the effectiveness of UpliftRec in discovering users' hidden interests while achieving superior recommendation accuracy.

Interactive storytelling is vital for preschooler development. While children's interactive partners have traditionally been their parents and teachers, recent advances in artificial intelligence (AI) have sparked a surge of AI-based storytelling and reading technologies. As these technologies become increasingly ubiquitous in preschoolers' lives, questions arise regarding how they function in practical storytelling and reading scenarios and, how parents, the most critical stakeholders, experience and perceive these technologies. This paper investigates these questions through a qualitative study with 17 parents of children aged 3-6. Our findings suggest that even though AI-based storytelling and reading technologies provide more immersive and engaging interaction, they still cannot meet parents' expectations due to a series of interactive and algorithmic challenges. We elaborate on these challenges and discuss the possible implications of future AI-based interactive storytelling technologies for preschoolers.

While deep neural networks (DNNs) have led to a paradigm shift, its exorbitant computational requirement has always been a roadblock in its deployment to the edge, such as wearable devices and smartphones. Hence a hybrid edge-cloud computational framework is proposed to transfer part of the computation to the cloud, by naively partitioning the DNN operations under the constant network condition assumption. However, real-world network state varies greatly depending on the context, and DNN partitioning only has limited strategy space. In this paper, we explore the structural flexibility of DNN to fit the edge model to varying network contexts and different deployment platforms. Specifically, we designed a reinforcement learning-based decision engine to search for model transformation strategies in response to a combined objective of model accuracy and computation latency. The engine generates a context-aware model tree so that the DNN can decide the model branch to switch to at runtime. By the emulation and field experimental results, our approach enjoys a 30% − 50% latency reduction while retaining the model accuracy.