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The United States retains many aspects of a healthy open society, but there are indicators of trouble and deep divisions around the meaning and importance of democratic values. This debate has significant repercussions for universities and their academic communities. In the most-simple terms, there is a red and blue state divide over the role and importance of public institutions, including universities -- red representing largely rural states in which most voters vote Republican and blue being majority Democratic voters, often with one of the two parties having majorities in their respective state legislatures. Then there are so-called purple states in which both parties are vying for dominance, but they are fewer in number. This brief discusses this contemporary dynamic and its implication for higher education and science policy.

This brief discusses cases of neo-nationalist violations of academic freedom in Hungary and Poland. The most prominent case of neo-nationalist violation of academic freedom in Hungary is the fate of the Central European University (CEU). The circumstances of CEU's forced move out of Hungary came before the European Court of Justice regarding it a possible violation of EU law. The Court cited the General Agreement on Trade in Services (GATS) under one of the three pillars of the World Trade Organisation (WTO) 1994 agreement, free trade, and the determination that CEU was a form of international educational services that should not be denied to the people of Hungary. Poland has a similar hostile environment to academics and academic freedom, although with a glimmer of hope following recent elections. The brief also discusses how such open breaches of academic freedom as in Hungary or Poland, in which politicians directly try to exert influence on research institutions and professors, are fortunately rather rare in Germany. However, a confluence of factors perhaps obscures the differences between "academic freedom" and the "freedom of opinion." In Germany, academic freedom includes the search for topics, rigorous methodical investigation, and professional norms to express findings and competent opinions, whereas the free speech is outside of these professional norms. The brief concludes with a discussion of the role of universities and the future of democracy in the context of ensuring a space for free and open debate.

Starting this year, tens of thousands of Russian freshmen found themselves attending a new mandatory course -- "Foundations of Russian Statehood." Swiftly designed under the auspices of Putin's administration, this ideologically charged course aims to position Russia as a unique civilization-state, bolstering Putin's political narrative and providing justification for the full-scale invasion of Ukraine. Consider, for example, this excerpt from the course's instructional video: "The 'Russian world' extends beyond current Russian borders, transcending ethnicities, territories, religions, political systems, and ideological preferences." As this curriculum becomes standard in Russian universities, it contributes to the emerging trend of weaponizing Russian universities and turning them into instruments in Russia's war of attrition with Ukraine and its broader stand-off with the West. This report discusses this weaponization process and the impact it is having on Russian universities, faculty, students, and the academic communities they belong to. It is regrettably a story of back to the future, reminiscent of the Soviet era of repression and attempts at control and manipulation of academics.

Since 2020, the federal government has distributed almost $2 billion to American school districts to mitigate the effects of COVID-19 pandemic school closures. Released in three separate tranches through the Elementary and Secondary School Emergency Relief (ESSER) fund, the Trump and Biden administrations outlined parameters for spending this unprecedented amount of money and ensured that most of it went directly to local school districts, with a comparative sliver going to state education agencies. This report gives an overview of the most common ESSER expenditures to date and assesses the factors that might influence whether district ESSER investments will make a difference for students. It also provides recommendations for how stakeholders should evaluate the ESSER experiment once it officially ends.

China's higher-education system has been shaped in recent years by a trio of factors: the COVID-19 pandemic, the ambitions of Chinese leader Xi Jinping to make his country into an innovation superpower that is loyal to the Communist Party, and western alarm about those ambitions. But a fourth development, the slowing of China's formerly super-charged economy, could play a more prominent role going forward. This article examines these four factors.

Since establishing its first campus in 1868, the University of California (UC), California's land-grant university, developed into the nation's first multi-campus system in the United States, and is today widely recognized as the world's premier network of public research universities. This short essay provides an historical brief on the role that shared governance, and specifically the role of the Academic Senate, played in creating an academic culture of excellence and high achievement in pursuing its tripartite mission of teaching and learning, research and knowledge production, and public service. A key component in understanding the critical role of the Senate in UC's evolution from a single campus in Berkeley to now a ten-campus system is the university's unusual designation as a public trust in the state constitution that, beginning in 1879, protected the university at critical times from external political pressures and allowed the university to develop an internal academic culture guided by the Academic Senate. By the 1920s, the emergence of California's unique and innovative public system of higher education, with UC as the sole public provider of doctoral degrees and state funded research, also helps explain the ability of the UC system to maintain its mission and formulate what is termed a "One University" model. The Academic Senate has created coherency and shared values within UC, and a culture and expectation for faculty performance that is unique among universities around the world. This essay also offers a brief reflection on the Academic Senate's past influence, its current status, and prospective role. The overall intent is to provide context for the current academic community and higher education scholars regarding the past and future role of faculty in university governance and management, and what distinguishes UC in the pantheon of major research universities.

Following up on an individual-level randomized controlled trial of a Dana Center Math Pathways (DCMP) model, this study assessed longer-term impacts on students' math completion, academic progress, and academic attainment. The version of the DCMP that was assessed in this study diversified the developmental and college-level math course content that students take, separating it into distinct pathways that better aligned with their career interests. It also streamlined developmental math sequences into a one-semester developmental course for all students, regardless of placement level, and implemented evidence-based curricula and pedagogy to engage students in active problem solving that was pertinent to real-life situations. The study, which followed 1,411 students from four Texas community colleges and ten campuses, found that, in the five years after random assignment, program group students were consistently more likely to successfully complete their first college-level math courses than control group students. The study did not find impacts after five years on the number of overall college credits that students accrued or on the likelihood that students attained a credential or transferred to a four-year college.

Multiple measures assessment (MMA) has gained considerable momentum over the past decade as an alternative to traditional test-based procedures for placing incoming students into developmental or college-level coursework in math and English at broad-access colleges. Compared to standardized tests, which measure student performance at a single point in time, MMA (which often emphasizes high school GPA as a measure) provides a more holistic picture of students' academic preparation. Despite positive impacts on student outcomes that have been found by recent research on MMA, questions remain about whether the positive effects of MMA are sustained over time. This study--a follow-up to prior research using the same sample of students--employs a randomized controlled trial to investigate whether algorithmic MMA placement used at seven State University of New York (SUNY) community colleges led to better student outcomes, for up to four and a half years after randomization, than a system based on test scores alone. Nearly 13,000 incoming students who arrived at the seven colleges in fall 2016, spring 2017, and fall 2017 took placement tests and were randomly assigned to be placed using either the status quo method (business-as-usual group) or the alternative, algorithmic MMA method (program group). Using this sample, we estimate the overall treatment effects on placement into, enrollment in, and completion of college-level math and English as well as effects on other outcomes. We conduct similar analyses on race/ethnicity, Pell recipient status, and gender subgroups. We also descriptively examine the proportion of program group students who were bumped up (i.e., their placement changed from a developmental course placement to a college-level course placement) and bumped down (i.e., their placement changed from a college-level course placement to a developmental course placement) by the MMA algorithm, and we perform a cost-effectiveness analysis. We find that the MMA method used at the colleges improved access to and success in college-level courses and that lower cut scores in English rather than math are associated with larger and longer lasting impacts on completion of college-level coursework. While MMA improved outcomes among student subgroups, it had little to no impact on gaps in outcomes between subgroups. We also find that bumped-up students had substantially better outcomes in both math and English, while bumped-down students had substantially worse outcomes. Our results suggest that increased access to college-level courses is the driving factor in the positive outcomes experienced by program group students and that placement into standalone developmental courses can have detrimental effects on student outcomes. In the discussion of the study's results, we make recommendations for adopting MMA at colleges. Implemented together with other initiatives to support students, MMA can be a first step on the path to success for incoming students. [For the accompanying research brief, see ED632528.]

The growth of homeschooling from a somewhat fringe movement during the 1970s and 1980s to a more widespread and socially accepted approach in recent decades has provided a strong foundation of flexible learning models. When Florida's school choice expansion, House Bill 1, was introduced in January 2023, one of its goals was to allow more homeschoolers to participate in the education savings account (ESA) program. But many homeschoolers and homeschool advocacy groups balked at the proposal. Having worked hard to achieve independence, they were loath to be linked to government funding. Even if the program were optional, they feared that associated regulations would eventually extend to traditional homeschoolers. In the end, the bill passed with new language that satisfied traditional homeschoolers by creating a new option for parents to educate their children at home. As states continue to enact and expand education savings accounts, Colleen Hroncich argues that it is crucial that policymakers craft bills in a way that maximizes freedom and minimizes roadblocks.

This study investigates racial disparities in school closures both within California and nationally. Findings highlight an alarming pattern: Schools enrolling higher proportions of Black students are at significantly increased risk of closure relative to those enrolling fewer Black students, a pattern that is more pronounced in California than elsewhere in the United States. This study also finds that conventional explanations for school closures--such as declining enrollments, poverty rates, and achievement differences--cannot fully account for why schools enrolling larger shares of Black students have greater odds of closure. These findings underscore that school closures in California and elsewhere reflect racial inequalities that require adequate policymaking to ensure equitable and fair school-closure proceedings

We investigate the impact of attending a free school in England -- that is, a new start-up school that enjoys considerable autonomy while remaining in the state sector. We analyse the effects of two secondary free schools with different teaching philosophies: one follows a 'no excuse' paradigm, while the other one adopts a 'classical liberal', knowledge-rich approach. We establish causal effects exploiting admission lotteries and a distance-based regression discontinuity design. Both schools have a strong positive impact on student test scores on average. However, we also find heterogeneous effects: the 'no excuse' school mostly benefits boys, while the 'classical liberal' school mainly benefits White British and non-poor students. Both schools similarly reduce student absences and school mobility. Peer quality, teacher characteristics, and inspectorate ratings cannot fully explain the schools' effectiveness. Instead, a quantitative text analysis of the schools' 'vision and ethos' statements shows that the 'no excuse' and 'classical liberal' philosophies adopted by the two free schools clearly set them apart from the counterfactual schools where rejected applicants enrol, and likely explain their heterogeneous effects.

Reading scientific research papers is a skill that many students do not learn before entering PhD programs, but it is critical to their success. This paper describes our diagramming technique for teaching this skill, which helps them identify the structure and the scientific argument of the paper. This has made our students more effective readers., Comment: 6 pages, 6 figures, working paper

Current research on defending against adversarial examples focuses primarily on achieving robustness against a single attack type such as $\ell_2$ or $\ell_{\infty}$-bounded attacks. However, the space of possible perturbations is much larger and currently cannot be modeled by a single attack type. The discrepancy between the focus of current defenses and the space of attacks of interest calls to question the practicality of existing defenses and the reliability of their evaluation. In this position paper, we argue that the research community should look beyond single attack robustness, and we draw attention to three potential directions involving robustness against multiple attacks: simultaneous multiattack robustness, unforeseen attack robustness, and a newly defined problem setting which we call continual adaptive robustness. We provide a unified framework which rigorously defines these problem settings, synthesize existing research in these fields, and outline open directions. We hope that our position paper inspires more research in simultaneous multiattack, unforeseen attack, and continual adaptive robustness.

As AI promises to accelerate scientific discovery, it remains unclear whether fully AI-driven research is possible and whether it can adhere to key scientific values, such as transparency, traceability and verifiability. Mimicking human scientific practices, we built data-to-paper, an automation platform that guides interacting LLM agents through a complete stepwise research process, while programmatically back-tracing information flow and allowing human oversight and interactions. In autopilot mode, provided with annotated data alone, data-to-paper raised hypotheses, designed research plans, wrote and debugged analysis codes, generated and interpreted results, and created complete and information-traceable research papers. Even though research novelty was relatively limited, the process demonstrated autonomous generation of de novo quantitative insights from data. For simple research goals, a fully-autonomous cycle can create manuscripts which recapitulate peer-reviewed publications without major errors in about 80-90%, yet as goal complexity increases, human co-piloting becomes critical for assuring accuracy. Beyond the process itself, created manuscripts too are inherently verifiable, as information-tracing allows to programmatically chain results, methods and data. Our work thereby demonstrates a potential for AI-driven acceleration of scientific discovery while enhancing, rather than jeopardizing, traceability, transparency and verifiability.

Citation text plays a pivotal role in elucidating the connection between scientific documents, demanding an in-depth comprehension of the cited paper. Constructing citations is often time-consuming, requiring researchers to delve into extensive literature and grapple with articulating relevant content. To address this challenge, the field of citation text generation (CTG) has emerged. However, while earlier methods have primarily centered on creating single-sentence citations, practical scenarios frequently necessitate citing multiple papers within a single paragraph. To bridge this gap, we propose a method that leverages Large Language Models (LLMs) to generate multi-citation sentences. Our approach involves a single source paper and a collection of target papers, culminating in a coherent paragraph containing multi-sentence citation text. Furthermore, we introduce a curated dataset named MCG-S2ORC, composed of English-language academic research papers in Computer Science, showcasing multiple citation instances. In our experiments, we evaluate three LLMs LLaMA, Alpaca, and Vicuna to ascertain the most effective model for this endeavor. Additionally, we exhibit enhanced performance by integrating knowledge graphs from target papers into the prompts for generating citation text. This research underscores the potential of harnessing LLMs for citation generation, opening a compelling avenue for exploring the intricate connections between scientific documents., Comment: 14 pages, 7 figures, 11th International Conference, BDA 2023, Delhi, India

For undergraduate students of computing, learning to solve complex practical problems in a team is an essential skill for their future careers. This skill is needed in various fields, such as in cybersecurity and IT governance. Tabletop exercises are an innovative teaching method used in practice for training teams in incident response and evaluation of contingency plans. However, tabletop exercises are not yet widely established in university education. This paper presents data and teaching experience from a cybersecurity course that introduces tabletop exercises in classrooms using a novel technology: INJECT Exercise Platform (IXP), a web-based learning environment for delivering and evaluating the exercises. This technology substantially improves the prior practice, since tabletop exercises worldwide have usually been conducted using pen and paper. Unlike in traditional tabletop exercises, which are difficult to evaluate manually, IXP provides insights into students' behavior and learning based on automated analysis of interaction data. We demonstrate IXP's capabilities and evolution by comparing exercise sessions hosted throughout three years at different stages of the platform's readiness. The analysis of student data is supplemented by the discussion of the lessons learned from employing IXP in computing education contexts. The data analytics enabled a detailed comparison of the teams' performance and behavior. Instructors who consider innovating their classes with tabletop exercises may use IXP and benefit from the insights in this paper., Comment: Published in ACM ITiCSE 2024 conference proceedings, see https://doi.org/10.1145/3649217.3653639

In the paper [3] the value of the invariant denoted by d and often called "rank" was misstated for a narrow class of examples of ECS manifolds: they were identified as having d = 1 instead of the correct value d = 2. The error was repeated, by citing [3], in [1], [2] and [4] -- [7]. We briefly describe the class in question, explain why it has d = 2, and list the required corrections of the affected papers., Comment: 4 pages, to be included in a future paper as an appendix

This paper introduces a new method of paper circuit fabrication that overcomes design barriers and increases flexibility in circuit design. Conventional circuit boards rely on thin traces, which limits the complexity and accuracy when applied to paper circuits. To address this issue, we propose a method that uses large conductive zones in paper circuits and performs subtractive processing during their fabrication. This approach eliminates design barriers and allows for more flexibility in circuit design. We introduce PaperCAD, a software tool that simplifies the design process by converting traditional circuit design to paper circuit design. We demonstrate our technique by creating two paper circuit boards. Our approach has the potential to promote the development of new applications for paper circuits., Comment: ACM CHI 2023 Workshop 02: Beyond Prototyping Boards: Future Paradigms for Electronics Toolkits

This paper explores the implementation of embedded magnets to enhance paper-based interactions. The integration of magnets in paper-based interactions simplifies the fabrication process, making it more accessible for building soft robotics systems. We discuss various interaction patterns achievable through this approach and highlight their potential applications., Comment: ACM DIS 2023 Workshop 02: Soft Robotics and Programmable Materials for Human-Computer Interaction

Behavioural types provide a promising way to achieve lightweight, language-integrated verification for communication-centric software. However, a large barrier to the adoption of behavioural types is that the current state of the art expects software to be written using the same tools and typing discipline throughout a system, and has little support for components over which a developer has no control. This position paper describes the outcomes of a working group discussion at Dagstuhl Seminar 24051 (Next-Generation Protocols for Heterogeneous Systems). We propose a methodology for integrating multiple behaviourally-typed components, written in different languages. Our proposed approach involves an extensible protocol description language, a session IR that can describe data transformations and boundary monitoring and which can be compiled into program-specific session proxies, and finally a session middleware to aid session establishment. We hope that this position paper will stimulate discussion on one of the most pressing challenges facing the widespread adoption of behavioural typing., Comment: In Proceedings PLACES 2024, arXiv:2404.03712

Context: Annually, ICSE acknowledges a range of papers, a subset of which are paired with research artifacts such as source code, datasets, and supplementary materials, adhering to the Open Science Policy. However, no prior systematic inquiry dives into gauging the influence of ICSE papers using artifact attributes. Objective: We explore the mutual impact between artifacts and their associated papers presented at ICSE over ten years. Method: We collect data on usage attributes from papers and their artifacts, conduct a statistical assessment to identify differences, and analyze the top five papers in each attribute category. Results: There is a significant difference between paper citations and the usage of associated artifacts. While statistical analyses show no notable difference between paper citations and GitHub stars, variations exist in views and/or downloads of papers and artifacts. Conclusion: We provide a thorough overview of ICSE's accepted papers from the last decade, emphasizing the intricate relationship between research papers and their artifacts. To enhance the assessment of artifact influence in software research, we recommend considering key attributes that may be present in one platform but not in another., Comment: This paper has been accepted for publication and presentation at The 22nd IEEE/ACIS International Conference on Software Engineering, Management and Applications (SERA 2024) to be held in Honolulu, USA on May 30-June 1, 2024

This paper presents and evaluates a set of methods to classify individual Scopus publications using their references back to the second generation, where each publication can be assigned fractionally into up to five ASJC (All Science Journal Classifications) categories, excluding the Multidisciplinary area and the miscellaneous categories. Based on proposals by Glanzel et al. (1999a, 1999b, 2021), some additional parameters are established that allow different results to be obtained depending on how category membership is weighted or how the acceptance thresholds for multiple assignments are established. Various classifications are obtained, and then compared with each other, with the original ASJC Scopus journal classification, and with the AAC (Authors Assignation Collection) classification of a previous study (Alvarez-Llorente et al., 2023) in which the papers corresponding authors assign them the most appropriate categories. Classifications in which a high threshold is set for allowing assignments to multiple categories, combined with the use of first- and second-generation references and averaging over the number of references, provide the most promising results, improving over other reference-based reclassification proposals in terms of granularity, and over the Scopus classification itself in such aspects as the homogeneity of the publications assigned to a category. They also show greater coincidence with the AAC classification.

This white paper presents the current status of the R&D for Analysis Facilities (AFs) and attempts to summarize the views on the future direction of these facilities. These views have been collected through the High Energy Physics (HEP) Software Foundation's (HSF) Analysis Facilities forum, established in March 2022, the Analysis Ecosystems II workshop, that took place in May 2022, and the WLCG/HSF pre-CHEP workshop, that took place in May 2023. The paper attempts to cover all the aspects of an analysis facility.

Scientific publishing lays the foundation of science by disseminating research findings, fostering collaboration, encouraging reproducibility, and ensuring that scientific knowledge is accessible, verifiable, and built upon over time. Recently, there has been immense speculation about how many people are using large language models (LLMs) like ChatGPT in their academic writing, and to what extent this tool might have an effect on global scientific practices. However, we lack a precise measure of the proportion of academic writing substantially modified or produced by LLMs. To address this gap, we conduct the first systematic, large-scale analysis across 950,965 papers published between January 2020 and February 2024 on the arXiv, bioRxiv, and Nature portfolio journals, using a population-level statistical framework to measure the prevalence of LLM-modified content over time. Our statistical estimation operates on the corpus level and is more robust than inference on individual instances. Our findings reveal a steady increase in LLM usage, with the largest and fastest growth observed in Computer Science papers (up to 17.5%). In comparison, Mathematics papers and the Nature portfolio showed the least LLM modification (up to 6.3%). Moreover, at an aggregate level, our analysis reveals that higher levels of LLM-modification are associated with papers whose first authors post preprints more frequently, papers in more crowded research areas, and papers of shorter lengths. Our findings suggests that LLMs are being broadly used in scientific writings.

In 2024, we will hold a research paper competition (the third Human Understanding AI Paper Challenge) for the research and development of artificial intelligence technologies to understand human daily life. This document introduces the datasets that will be provided to participants in the competition, and summarizes the issues to consider in data processing and learning model development., Comment: 7 pages, 3 figures

Examining limitations is a crucial step in the scholarly research reviewing process, revealing aspects where a study might lack decisiveness or require enhancement. This aids readers in considering broader implications for further research. In this article, we present a novel and challenging task of Suggestive Limitation Generation (SLG) for research papers. We compile a dataset called LimGen, encompassing 4068 research papers and their associated limitations from the ACL anthology. We investigate several approaches to harness large language models (LLMs) for producing suggestive limitations, by thoroughly examining the related challenges, practical insights, and potential opportunities. Our LimGen dataset and code can be accessed at https://github.com/armbf/LimGen., Comment: 16 pages, 3 figures

This non-archival index is not complete, as some accepted papers chose to opt-out of inclusion. The list of all accepted papers is available on the workshop website.

Paper is a cheap, recyclable, and clean material that is often used to make practical tools. Traditional tool design either relies on simulation or physical analysis, which is often inaccurate and time-consuming. In this paper, we propose PaperBot, an approach that directly learns to design and use a tool in the real world using paper without human intervention. We demonstrated the effectiveness and efficiency of PaperBot on two tool design tasks: 1. learning to fold and throw paper airplanes for maximum travel distance 2. learning to cut paper into grippers that exert maximum gripping force. We present a self-supervised learning framework that learns to perform a sequence of folding, cutting, and dynamic manipulation actions in order to optimize the design and use of a tool. We deploy our system to a real-world two-arm robotic system to solve challenging design tasks that involve aerodynamics (paper airplane) and friction (paper gripper) that are impossible to simulate accurately., Comment: Project Website: https://paperbot.cs.columbia.edu/

Communicating design implications is common within the HCI community when publishing academic papers, yet these papers are rarely read and used by designers. One solution is to use design cards as a form of translational resource that communicates valuable insights from papers in a more digestible and accessible format to assist in design processes. However, creating design cards can be time-consuming, and authors may lack the resources/know-how to produce cards. Through an iterative design process, we built a system that helps create design cards from academic papers using an LLM and text-to-image model. Our evaluation with designers (N=21) and authors of selected papers (N=12) revealed that designers perceived the design implications from our design cards as more inspiring and generative, compared to reading original paper texts, and the authors viewed our system as an effective way of communicating their design implications. We also propose future enhancements for AI-generated design cards.

Reinforcement Learning (RL) has increasingly become a preferred method over traditional rule-based systems in diverse human-in-the-loop (HITL) applications due to its adaptability to the dynamic nature of human interactions. However, integrating RL in such settings raises significant privacy concerns, as it might inadvertently expose sensitive user information. Addressing this, our paper focuses on developing PAPER-HILT, an innovative, adaptive RL strategy through exploiting an early-exit approach designed explicitly for privacy preservation in HITL environments. This approach dynamically adjusts the tradeoff between privacy protection and system utility, tailoring its operation to individual behavioral patterns and preferences. We mainly highlight the challenge of dealing with the variable and evolving nature of human behavior, which renders static privacy models ineffective. PAPER-HILT's effectiveness is evaluated through its application in two distinct contexts: Smart Home environments and Virtual Reality (VR) Smart Classrooms. The empirical results demonstrate PAPER-HILT's capability to provide a personalized equilibrium between user privacy and application utility, adapting effectively to individual user needs and preferences. On average for both experiments, utility (performance) drops by 24%, and privacy (state prediction) improves by 31%., Comment: 18 pages

The Wide-field Spectroscopic Telescope (WST) is proposed as a new facility dedicated to the efficient delivery of spectroscopic surveys. This white paper summarises the initial concept as well as the corresponding science cases. WST will feature simultaneous operation of a large field-of-view (3 sq. degree), a high multiplex (20,000) multi-object spectrograph (MOS) and a giant 3x3 sq. arcmin integral field spectrograph (IFS). In scientific capability these requirements place WST far ahead of existing and planned facilities. Given the current investment in deep imaging surveys and noting the diagnostic power of spectroscopy, WST will fill a crucial gap in astronomical capability and work synergistically with future ground and space-based facilities. This white paper shows that WST can address outstanding scientific questions in the areas of cosmology; galaxy assembly, evolution, and enrichment, including our own Milky Way; origin of stars and planets; time domain and multi-messenger astrophysics. WST's uniquely rich dataset will deliver unforeseen discoveries in many of these areas. The WST Science Team (already including more than 500 scientists worldwide) is open to the all astronomical community. To register in the WST Science Team please visit https://www.wstelescope.com/for-scientists/participate, Comment: 194 pages, 66 figures. Comments are welcome (wstelescope@gmail.com)

This paper discusses the effectiveness of leveraging Chatbot: Generative Pre-trained Transformer (ChatGPT) versions 3.5 and 4 for analyzing research papers for effective writing of scientific literature surveys. The study selected the \textit{Application of Artificial Intelligence in Breast Cancer Treatment} as the research topic. Research papers related to this topic were collected from three major publication databases Google Scholar, Pubmed, and Scopus. ChatGPT models were used to identify the category, scope, and relevant information from the research papers for automatic identification of relevant papers related to Breast Cancer Treatment (BCT), organization of papers according to scope, and identification of key information for survey paper writing. Evaluations performed using ground truth data annotated using subject experts reveal, that GPT-4 achieves 77.3\% accuracy in identifying the research paper categories and 50\% of the papers were correctly identified by GPT-4 for their scopes. Further, the results demonstrate that GPT-4 can generate reasons for its decisions with an average of 27\% new words, and 67\% of the reasons given by the model were completely agreeable to the subject experts.