Digital Commons Network^™

How Behavioral Science Can Improve The Return On Ai Investments, David De Cremer, Shane Schweitzer, Jack Mcguire, Devesh Narayanan

Research Collection Lee Kong Chian School Of Business

Many AI projects fail because leaders treat adoption as a tech purchase instead of a behavioral change problem. People resist tools that disrupt routines, overreact to visible AI errors, and prefer familiar human judgment. As a result, even good systems fail to gain purchase. Leaders can address this problem by applying “Behavioral Human-Centered AI” across the AI adoption cycle. In the design phrase, companies should co-design with diverse users, add purposeful friction where it improves scrutiny, require beta tests with subgroup results and behavioral input. During adoption, they should frame AI as an augmenter, disclose limits and safeguards, use explainability …

Possibilities Of Digitizing And Applying Artificial Intelligence To National Occupational Classification (Noc-2025) In Uzbekistan, Shohrux Nurali O‘G‘Li Narzullayev

Chemical Technology, Control and Management

This article examines the process of digitizing National Occupational Classification (NOC-2025) in Uzbekistan, developed on the basis of the International Standard Classification of Occupations (ISCO-08), and the possibilities of applying artificial intelligence technologies to it. Although this classification exists today in a national form, and its digitization and the introduction of artificial intelligence elements to it based on modern technologies remain a pressing issue. In order to digitize the classification, international systems such as the International Standard Classification of Occupations (ISCO-08, ILO), European Skills, Competences, Qualifications and Occupations (ESCO), Occupational Information Network (O*NET, USA) and National Occupational Classification (NOC, Canada) …

Smart Learning In An Ai-Driven World, Joseph Rene Corbeil, Maria Elena Corbeil

Teaching and Learning Faculty Publications

SMART learning is an approach that combines sustainability, motivation, adaptability, results-oriented practices, and technology enablement. AI-driven SMART learning prioritizes flexibility, cost-efficiency, and knowledge retention, equipping teachers and students with the tools to adapt to an ever-evolving world. By envisioning AI's role in fostering SMART learning attributes, we pave the way for a future where technology and education intertwine to empower learners and redefine teaching practices.

Innovations In Thrombectomy Training: A Systematic Review And Expert Recommendations From The Society Of Vascular And Interventional Neurology-Mission Thrombectomy Initiative, Fawaz Al-Mufti, Mohamed Elfil, Abdallah Abbas, Haneen Sabet, Hazem S Ghaith, Ariel Sacknovitz, Ankita Jain, Victor Urrutia, Nabeel Herial, Gabor Toth, Mohamad El-Ghanem, Krishna Amuluru, Viktor C Szeder, Jonathan Crowe, Karol Budohoski, Zurab Nadareishvili, Kaustubh Limaye, Fazeel Siddiqui, Boris Pabon, Ossama Yassin Mansour, Atilla Ozcan Ozdemir, Houman Khosravani, Hamza Shaikh, Nishita Singh, Hesham Masoud, Sushanth R Aroor, Shashvat Desai, Santiago Ortega-Gutierrez, Fredrick Echols, Thanh N Nguyen, Pankajavalli Ramakrishnan, Priyank Khandelwal, Dileep R Yavagal, Kaiz Asif

Faculty, Staff and Student Publications

Background: Mechanical thrombectomy is a critical intervention for patients with acute ischemic stroke with large vessel occlusion. However, significant barriers remain in its widespread implementation, particularly in low- to middle-income countries, including a shortage of trained physicians and limited access to advanced medical technologies. This systematic review and meta-analysis aimed to comprehensively evaluate current mechanical thrombectomy training methodologies and assess their effectiveness in improving procedural skills among neurointerventional teams.

Methods: We conducted a systematic review following Preferred Reporting Items for Systematic reviews and Meta-Analyses guidelines, searching PubMed, Scopus, and Web of Science. Eight studies were included, with 3 studies eligible …

Virtual Reality In Esl Teaching And Learning In A Hybrid Learning Environment In The United Arab Emirates, Basma Abdulla Abri

Dissertations

This study examines the impact of Virtual Reality (VR) on the oral proficiency of EFL learners in the UAE context, with exploring the instructors and students’ perspectives on using VR as a language learning tool. The study involved 44 Emirati higher education students enrolled in an English Communications Program divided into a control group using traditional methods, and an experimental group using VR tools for communicative activities. Data collection employed mixed-methods research through questionnaire surveys involving college students and interviews with EFL instructors over a ten-week period. According to the findings, the experimental group demonstrated higher oral proficiency than the …

Reading The Word And The World: Overstanding Literacy In Aboriginal And Chinese Classrooms, Gui Ying Annie Yang-Heim

Faculty Publications - College of Education

This qualitative comparative case study examines how culturally grounded philosophies of education shape the teaching and learning of reading in two cross-cultural contexts—an Aboriginal Australian classroom and urban Chinese elementary schools. Drawing on interpretive and reflexive methodologies, it investigates how Aboriginal and Confucian epistemologies influence literacy practices and how these practices align with or resist dominant, decontextualized models of reading instruction. Data sources include classroom observations, reading assessments, teacher interviews, and researcher reflections. Conceptually framed by Gadamer’s hermeneutics, Bourdieu’s theory of cultural capital, Habermas’s typology of knowledge, and the Caribbean concept of overstanding, this research finds that Aboriginal literacy is …

The Irrelevance Of “Peak Fame” On Celebrity Candidate Success, Richard T. Longoria, Dana Reyes

Political Science Faculty Publications

Objective

Does it matter “when” a celebrity runs for office? The political scientists who study celebrity candidates need to consider an important insight from communications and media scholars. Fame is a time-dependent variable. Someone who was famous 40 years ago may no longer be widely known today.

Methods

A list of celebrity political candidates who ran for office in the United States between 1865 and 2022 was created to measure campaign success.

Results

In terms of winning an election, it does not matter whether a celebrity candidate runs at the peak of their fame or waits until decades later. What …

Context-Aware Hierarchical Taxonomy Generation For Scientific Papers Via Llm-Guided Multi-Aspect Clustering, Kun Zhu, Lizi Liao, Yuxuan Gu, Lei Huang, Xiaocheng Feng, Bing Qin

Research Collection School Of Computing and Information Systems

The rapid growth of scientific literature demands efficient methods to organize and synthesize research findings. Existing taxonomy construction methods, leveraging unsupervised clustering or direct prompting of large language models (LLMs), often lack coherence and granularity. We propose a novel context-aware hierarchical taxonomy generation framework that integrates LLM-guided multi-aspect encoding with dynamic clustering. Our method leverages LLMs to identify key aspects of each paper (e.g., methodology, dataset, evaluation) and generates aspect-specific paper summaries, which are then encoded and clustered along each aspect to form a coherent hierarchy. In addition, we introduce a new evaluation benchmark of 156 expert-crafted taxonomies encompassing 11.6k …

Why Stop At One Error? Benchmarking Llms As Data Science Code Debuggers For Multi-Hop And Multi-Bug Errors, Zhiyu Yang, Shuo Wang, Yukun Yan, Yang Deng

Research Collection School Of Computing and Information Systems

LLMs are transforming software development, yet current code generation and code repair benchmarks mainly assess syntactic and functional correctness in simple, single-error cases. LLMs’ capabilities to autonomously find and fix runtime logical errors in complex data science code remain largely unexplored. To address this gap, we introduce DSDBench: the Data Science Debugging Benchmark, the first benchmark for systematic evaluation of LLMs on multi-hop error tracing and multi-bug detection in data science code debugging. DSDBench adapts datasets from existing data science task benchmarks, such as DABench and MatPlotBench, featuring realistic data science debugging tasks with automatically synthesized multi-hop, multi-bug code snippets. …

Discussion Of "Csr And Negative Corporate Events: The Moderating Role Of Managerial Overconfidence", Hye Sun Chang

Research Collection School Of Accountancy

Corporate social responsibility (CSR) refers to the notion that firms are accountable not only to shareholders but also to a broader set of stakeholders including customers, employees, creditors, and the communities in which they operate. Although CSR has long been part of corporate discourse, its prominence has grown with the rise of environmental, social, and governance (ESG) concerns. Today’s consumers and stakeholders are increasingly attentive to issues such as climate change, economic inequality, labor rights, and social justice, prompting firms to align their strategies with evolving societal expectations. Building on this increasingly salient backdrop, Chu et al. (2026), hereafter CCT, …

Spanish Heritage Speakers And Latinidad: How Spanish Fluency Influences Self-Perceived Latinidad Among Heritage Speakers, David Alejandro Rivera Sandoval, Gladys Valle

Undergraduate Theses and Capstone Projects

Abstract of Research: This study examines 1) how heritage speakers' self-perceived proficiency and fluency in Spanish influence their identity as Latinos, and 2) the generational similarities and differences in their lived experiences. Through surveys and interviews, the relationship between Spanish proficiency and fluency and perceived Latinidad among nine Spanish heritage speakers (with Latin American ancestry) has been assessed. The research suggests that no statistically significant relationship was found between language proficiency or fluency and perceived Latinismo. However, qualitative findings revealed that participants construct their Latino identity based on cultural values, lived experiences, and a sense of belonging rather than language …

Agility Evaluation On The Small Value Item Procurement Process Of The Department Of Education Division Of Davao Del Sur, Philippines, Save Mart E. Mondejar, Haerold Dean Z. Layaoen, Gabriel John L. De Leon, Julius Angelo D.J. Galang

Journal Article

Public procurement in the Philippines is primarily guided by Republic Act 9184 or the Government Procurement Act. In the Department of Education Division of Davao del Sur (DepEd Davao del Sur), procurement can be classified under big and small value item procurement. However, small value item procurement (SVP) projects in DepEd Davao del Sur experienced persistent delays in 2021 and 2022. In addition, gaps in the documentation and archiving system constrained the availability of reliable procurement data. To address this, the fuzzy agility evaluation method (FAEM) was utilized within a limited timeframe to determine the level of procurement agility and …

Intelligent Decision-Making Systems In Smart Greenhouses, Muso Berdiyor Ugli Allanov

Chemical Technology, Control and Management

Smart greenhouses offer a solution to sustainable food production under climate uncertainty, yet their management often depends on fixed rules or human intuition. This study proposes an intelligent decision-making framework that integrates optimization, simulation, and a neural set into a self-learning system. By generating “conditionally real data” through simulation and evolutionary algorithms, the system can predict microclimatic changes and optimize control of water, energy, and nutrients. Continuous digital feedback enables adaptive, data-efficient operation even with limited real data. Experimental results demonstrate reduced resource use and improved yield stability, advancing the development of autonomous and resilient greenhouse ecosystems.

Recommendations From Multi-Disciplinary Professionals For Survivor-Informed And Comprehensive Care For Human Trafficking Survivors, Christina Tsoi, Timothy B. Smith, Elizabeth Cutrer-Párraga, Devan Clayton, Joshua M. Marshall, Jamila Mastny

Faculty Publications

Background: Human trafficking affects millions of people worldwide with multiple adverse outcomes including psychopathology. Although research on human trafficking has become abundant in other academic disciplines (e.g., public health, criminology, social work), healthcare research specific to the mental health treatment of survivors remains limited. Objective: The purpose of this study was to gather recommendations from professionals about mental health treatment of trafficking survivors. Method: Semi-structured interviews were conducted with 21 multidisciplinary professionals working with trafficking survivors. Data were analyzed using qualitative content analysis methodology. Results: An overarching theme derived from the data concerned the recommendation to implement trafficking survivor-informed care, …

Image Captioning Through The Lens Of The Gricean Maxims: Generating Meaningful And Relevant Image Descriptions, Annika Lindh

Doctoral

Image captioning models enable us to automatically generate natural language image descriptions for previously unseen images. It combines the two fields of computer vision and natural language generation, allowing models to interpret the con tent of an image and communicate that knowledge through natural language text.

Research into image captioning has the potential benefit of reducing the gap in digital information availability between fully sighted individuals and those who are visually impaired. However, automatically generated captions often fail to provide the required level of detail and specificity to achieve this goal. Furthermore, current standard evaluation methods are insufficient at measuring …

Mmlu-Prox: A Multilingual Benchmark For Advanced Large Language Model Evaluation, Weihao Xuan, Et. Al.

Research Collection School Of Computing and Information Systems

Existing large language model (LLM) evaluation benchmarks primarily focus on English, while current multilingual tasks lack parallel questions that specifically assess cross-lingual reasoning abilities. This dual limitation makes it challenging to assess LLMs’ performance in the multilingual setting comprehensively. To fill this gap, we introduce MMLU-ProX, a comprehensive benchmark covering 29 languages, built on an English benchmark. Each language version consists of 11,829 identical questions, enabling direct cross-lingual comparisons. Additionally, to meet efficient evaluation needs, we provide a lite version containing 658 questions per language. To ensure the high quality of MMLU-ProX, we employ a rigorous development process that involves …

Interaction2code: Benchmarking Mllm-Based Interactive Webpage Code Generation From Interactive Prototyping, Jingyu Xiao, Yuxuan Wan, Yintong Huo, Zixin Wang, Xinyi Xu, Wenxuan Wang, Zhiyao Xu, Yuhang Wang, Michael R. Lyu

Research Collection School Of Computing and Information Systems

Multimodal Large Language Models (MLLMs) have demonstrated remarkable performance on the design-to-code task, i.e., generating UI code from UI mock-ups. However, existing benchmarks only contain static web pages for evaluation and ignore the dynamic interaction, limiting the practicality, usability and user engagement of the generated webpages. To bridge these gaps, we present the first systematic investigation of MLLMs in generating interactive webpages. Specifically, we formulate the Interaction-to-Code task and establish the Interaction2Code benchmark, encompassing 127 unique webpages and 374 distinct interactions across 15 webpage types and 31 interaction categories. Through comprehensive experiments utilizing state-of-theart (SOTA) MLLMs, evaluated via both automatic …

Distillcaps: Enhancing Audio-Language Alignment In Captioning Via Retrieval-Augmented Knowledge Distillation, Thinh Pham, Nghiem Diep, Lizi Liao, Binh Nguyen

Research Collection School Of Computing and Information Systems

Automated audio captioning (AAC) benefits from incorporatingexternal context to interpret complex sounds, but doing so withretrieval-augmented generation (RAG) at inference is sometimesinfeasible due to data availability or incurs significant latency andcomplexity. We propose DistillCaps, a novel training-time frame-work that leverages RAG to guide knowledge distillation for im-proved audio-language alignment, while lessening the relianceon retrieval during inference. In our framework, a RAG-equippedteacher model retrieves relevant textual information (e.g., simi-lar captions) for each audio clip and uses it for training to gener-ate context-enriched captions. Simultaneously, a student model istrained to imitate this teacher, learning to produce high-qualitycaptions from audio alone. We further …

Instructors’ Strategies In Creating And Implementing Constructivist Llm-Based Learning Activities, Emily Aurelia, Shun Yi Yeo, Michelle Lui, Effie Lai-Chong Law, Anthony Tang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) are increasingly being integrated into educational settings, enabling more adoption of constructivist teaching and learning approaches in classrooms. This paper explores the strategies instructors are currently using to incorporate LLMs into learning activities that align with constructivist principles, which emphasize that learners actively construct their own knowledge. Through interviews with nine instructors who have designed eleven distinct LLM-based activities and using reflexive thematic analysis, this study identifies various types of learning activities with respect to four different aspects of the constructivist learning theory. The strategies employed and challenges faced to foster constructivist student-LLM interaction were also …

Enhancing The Acceptability Of Decision-Making System Using Xai Case: Crime Profiling System, Mohamed Saeed Al Shamsi

Theses

In the current world, we need to place more emphasis on how easily interpretable, accurate, and acceptable data analysis results are, given that essential operations in law enforcement, among other sectors, are backed up by the use of complex computing systems. Crime profiling systems that use crime data for profiling encounter major problems because they depend on algorithm-based methods. These methods can be ambiguous and inaccurate, leading to low public acceptability. The study investigates major problems with Complex Crime profiling systems (CPS) because their unexplained algorithms result in system performance issues and public scepticism. XAI provides a solution to handle …

Love, Community, Errantry: Relational Approaches Toward Mathematical Mistakes, Melissa Adams Corral, Romina S. Peña-Pincheira, José Martínez Hinestroza

Teaching and Learning Faculty Publications

In this article we explore ways of approaching what educators and researchers perceive as mistakes that are consistent with views of mathematics that tolerate ambiguity, contradiction, and multiple possible truths. The approaches we describe draw on works within and beyond the field of mathematics education and detail theories from biologist Humberto Maturana, community organizers Bob Moses and Ella Baker, and poet-philosopher Edouard Glissant. These alternative theoretical perspectives are distinctly relational in their approach. They can build on the field’s current conceptualizations of pedagogical responses to errors in ways that attend to calls for broadening what it means to do mathematics …

Differences In Patient And Parent Preferences For Prognostic Communication Across Disease Milestones In Advanced Childhood Cancer, Rachel A Kentor, Shoshana Mehler, Caroline Christianson, Harmony Farner, Tara M Brinkman, Justin N Baker, Pamela S Hinds, Jennifer W Mack, Erica C Kaye

Faculty, Staff and Students Publications

Background: Navigating prognostic communication is challenging for clinicians caring for adolescents and young adults (AYAs) with poor-prognosis cancer. Presently, little is known about how AYA and parent preferences for prognostic disclosure evolve over time. This study explored communication preferences at key illness milestones of disease progression to inform individualized, person-centered approaches to optimize prognostic disclosure.

Procedure: Sixty-five participants, including 25 AYAs aged 12-25 years and 40 parents, were recruited at distinct illness timepoints-diagnosis, relapse/progression, Phase I/II trial enrollment, and bereavement-to participate in semi-structured interviews about their preferences for timing, style, and content of prognostic communication. Rapid analysis was conducted to …

Assessing Teachers’ Practices In Supporting Gifted/Talented And Twice-Exceptional Students In Schools And Centers In Abu Dhabi, Zayed Jaber

Theses

Gifted/talented and twice-exceptional (2e) students, especially those whose needs are often overlooked in mainstream classrooms, are more likely to benefit from consistently implemented Differentiated Instructional Practices (DIPs). This study aimed to examine implelemntation of DIPs for gifted/talented and 2e students and to compare the practices implemented between of Special Educational Needs (SEN) teachers and General Education teachers in schools and centers within Abu Dhabi. This research used a quantitative, cross-sectional survey design to collect data from eighty-six teachers from Abu Dhabi. A questionnaire was designed to rate teachers’ self-reported implementation of DIPs across the four domains of content, process, product, …

How Do Emirati University Students Develop Their Academic Biliteracy? A Mixed-Method Study, Eman Kamel Rababa

Dissertations

Framed by Hornberger’s continua of biliteracy model, this study investigated the developmental dynamics of academic biliteracy among Emirati university students. The research employed a sequential mixed-methods design, conducted across three public universities in the UAE. In the initial qualitative phase, interview data were collected from 30 students. Informed by these findings and the extant literature, a quantitative survey was then developed and administered to a sample of 299 students from various disciplines. The qualitative data were analyzed using reflexive thematic analysis, while the quantitative data were examined using advanced statistical analyses, including structural equation modeling. Three primary themes emerged from …

Effectiveness Of Ai-Powered Language Learning Tools In The Workplace, Iswariya Baskar

Electronic Theses and Dissertations

This dissertation explored the experiences of multilingual employees with AI-powered language learning tools in financial and technology firms across the United States. As multilingual communication becomes increasingly vital in the workplace, the integration of artificial intelligence (AI) offers promising support in language acquisition and cross-cultural collaboration. The purpose of this basic qualitative study was to explore how AI-powered language learning tools influence the acquisition and retention of foreign languages of employees within financial and technology firms, how these tools strengthen professional identity and interpersonal communication, and how code-switching facilitated by AI impacts user experience and workplace engagement. Drawing from the …

Explainable Sentiment Analysis With Deepseek-R1: Performance, Efficiency, And Few-Shot Learning, Donghao Huang, Zhaoxia Wang

Research Collection School Of Computing and Information Systems

Large language models (LLMs) have transformed sentiment analysis, yet balancing accuracy, efficiency, and explainability remains a critical challenge. This study presents the first comprehensive evaluation of DeepSeek-R1—an open-source reasoning model—against OpenAI’s GPT-4o and GPT-4o-mini. We test the full 671B model and its distilled variants, systematically documenting few-shot learning curves. Our experiments show DeepSeek-R1 achieves a 91.39% F1 score on 5-class sentiment and 99.31% accuracy on binary tasks with just 5 shots, an eightfold improvement in few-shot efficiency over GPT-4o. Architecture-specific distillation effects emerge, where a 32B Qwen2.5-based model outperforms the 70B Llama-based variant by 6.69 percentage points. While its reasoning …

Generative Ai And Empirical Software Engineering: A Paradigm Shift, Christoph Treude, Margaret-Anne Storey

Research Collection School Of Computing and Information Systems

The widespread adoption of generative AI in software engineering marks a paradigm shift, offering new opportunities to design and utilize software engineering tools while influencing both developers and the artifacts they create. Traditional empirical methods in software engineering, including quantitative, qualitative, and mixed-method approaches, are well established. However, this paradigm shift introduces novel data types and redefines many concepts in the software engineering process. The roles of developers, users, agents, and researchers increasingly overlap, blurring the distinctions between these social and technical actors within the field. This paper examines how integrating AI into software engineering challenges traditional research paradigms. It …

Intentionframe: A Semi-Structured, Multi-Aspect Framework For Fine-Grained Conversational Intention Understanding, Zailong Tian, Zhuoheng Han, Lizi Liao, Lizi Liao

Research Collection School Of Computing and Information Systems

Understanding user intentions in multi-turn dialogues is critical for conversational AI, yet existing approaches—relying on rigid slot-value structures or unstructured free-text—fail to fully capture conversational complexity. In this paper, we propose IntentionFrame, a semi-structured framework inspired by psychological and cognitive intention theories, which organizes conversational intents into four interrelated aspects: situation, emotion, action, and knowledge. This design not only retains interpretability but also provides LLMs with a rich context to accurately parse and respond to nuanced user inputs. To efficiently scale IntentionFrame annotations, we introduce a Weakly-supervised Reinforced Generation (WeRG) method that leverages a small set of high-quality human annotations …

Language, Law, And Wealth Destruction In Puerto Rico, Scott M. Brown, Daniel J. Hall

Vanderbilt Journal of Transnational Law

No abstract provided.

Justice Through Proximity: Theoretical Foundations And Practical Applications Of Collaborative Justice, Federica Coppola, Jarrell E. Daniels

Vanderbilt Law Review

This Article introduces a collaborative justice framework as a model for addressing crime and reimagining approaches to policymaking and legal education. The framework emphasizes the prevention and resolution of criminal matters through holistic strategies that confront the structural and social determinants of crime and victimization including poverty, systemic inequality, trauma, unjust policies, and institutional neglect while also promoting broader systemic change. At its core is the active engagement between legal system actors especially decisionmakers such as judges, prosecutors, and policymakers and legal system impacted individuals and communities, fostering a more equitable distribution of decisionmaking power between justice providers and justice …