Computing technology has deeply shaped how academic articles are written and produced, yet article formats and affordances have changed little over centuries. The status quo consists of digital files optimized for printed paper—ill-suited to interactive reading aids, accessibility, dynamic figures, or easy information extraction and reuse. Guided by formative discussions with scholarly communication researchers and publishing tool developers, we present Living Papers, a language toolkit for producing augmented academic articles that span print, interactive, and computational media. Living Papers articles may include formatted text, references, executable code, and interactive components. Articles are parsed into a standardized document format from which a variety of outputs are generated, including static PDFs, dynamic web pages, and extraction APIs for paper content and metadata. We describe Living Papers' architecture, document model, and reactive runtime, and detail key aspects such as citation processing and conversion of interactive components to static content. We demonstrate the use and extension of Living Papers through examples spanning traditional research papers, explorable explanations, information extraction, and reading aids such as enhanced citations, cross-references, and equations. Living Papers is available as an extensible, open source platform intended to support both article authors and researchers of augmented reading and writing experiences.
https://doi.org/10.1145/3586183.3606791
Efficiently reviewing scholarly literature and synthesizing prior art are crucial for scientific progress. Yet, the growing scale of publications and the burden of knowledge make synthesis of research threads more challenging than ever. While significant research has been devoted to helping scholars interact with individual papers, building research threads scattered across multiple papers remains a challenge. Most top-down synthesis (and LLMs) make it difficult to personalize and iterate on the output, while bottom-up synthesis is costly in time and effort. Here, we explore a new design space of mixed-initiative workflows. In doing so we develop a novel computational pipeline, Synergi, that ties together user input of relevant seed threads with citation graphs and LLMs, to expand and structure them, respectively. Synergi allows scholars to start with an entire threads-and-subthreads structure generated from papers relevant to their interests, and to iterate and customize on it as they wish. In our evaluation, we find that Synergi helps scholars efficiently make sense of relevant threads, broaden their perspectives, and increases their curiosity. We discuss future design implications for thread-based, mixed-initiative scholarly synthesis support tools.
https://doi.org/10.1145/3586183.3606759
Learning to read scientific papers critically, which requires first grasping their main ideas and then raising critical thoughts, is important yet challenging for novice researchers. The traditional ways to develop critical paper reading (CPR) skills, e.g., checking general tutorials or taking reading courses, often can not provide individuals with adaptive and accessible support. In this paper, we first derive user requirements of a CPR training tool based on literature and a survey study (N=52). Then, we develop CriTrainer, an interactive tool for CPR training. It leverages text summarization techniques to train readers’ skills in grasping the paper’s main ideas. It further utilizes template-based generated questions to help them learn how to raise critical thoughts. A mixed-design study (N=24) shows that compared to a baseline tool with general CPR guidance, students trained by CriTrainer perform better in independently raising critical thinking questions on a new paper. We conclude with design considerations for CPR training tools.
https://doi.org/10.1145/3586183.3606816
We explore the design of Marvista—a human-AI collaborative tool that employs a suite of natural language processing models to provide end-to-end support for reading online news articles. Before reading an article, Marvista helps a user plan what to read by filtering text based on how much time one can spend and what questions one is interested to find out from the article. During reading, Marvista helps the user reflect on their understanding of each paragraph with AI-generated questions. After reading, Marvista generates an explainable human-AI summary that combines both AI’s processing of the text, the user’s reading behavior, and user-generated data in the reading process. In contrast to prior work that offered (content-independent) interaction techniques or devices for reading, Marvista takes a human-AI collaborative approach that contributes text-specific guidance (content-aware) to support the entire reading process.
Academic writing in English can be challenging for non-native English speakers (NNESs). AI-powered rewriting tools can potentially improve NNESs' writing outcomes at a low cost. However, whether and how NNESs make valid assessments of the revisions provided by these algorithmic recommendations remains unclear. We report a study where NNESs leverage an AI-powered rewriting tool, Langsmith, to polish their drafted academic essays. We examined the participants' interactions with the tool via user studies and interviews. Our data reveal that most participants used Langsmith in combination with other tools, such as machine translation (MT), and those who used MT had different ways of understanding and evaluating Langsmith's suggestions than those who did not. Based on these findings, we assert that NNESs' quality assessment in AI-powered rewriting tools is influenced by the simultaneous use of multiple tools, offering valuable insights into the design of future rewriting tools for NNESs.
https://doi.org/10.1145/3586183.3606810
Vocabulary learning support tools have widely exploited existing materials, e.g., stories or video clips, as contexts to help users memorize each target word. However, these tools could not provide a coherent context for any target words of learners’ interests, and they seldom help practice word usage. In this paper, we work with teachers and students to iteratively develop Storyfier, which lever- ages text generation models to enable learners to read a generated story that covers any target words, conduct a story cloze test, and use these words to write a new story with adaptive AI assistance. Our within-subjects study (N=28) shows that learners generally favor the generated stories for connecting target words and writ- ing assistance for easing their learning workload. However, in the read-cloze-write learning sessions, participants using Storyfier per- form worse in recalling and using target words than learning with a baseline tool without our AI features. We discuss insights into supporting learning tasks with generative models.
https://doi.org/10.1145/3586183.3606786