Digital Commons Network^™

Spiritan Priestly Formation In Light Of The Synod On Synodality, Justi Tarimo Cssp

Spiritan Horizons

No abstract provided.

Understanding Ai Literacy: Implications For Practice​, Devon Olson, Janet Anderson, Sara Westall

Librarian Publications

Presentation from the 2025 Mountain Plains Library Association Tri-Conference, held in Grand Forks, ND. This presentation provides an overview of basic AI concepts and frameworks for AI literacy, discussing the impact of different patron and learner demographics on learner needs, competencies, and instructional strategies.

Does The Law Ever Run Out?, Charles F. Capps

Notre Dame Law Review

Although laypeople commonly believe that a judge’s job is to decide every case as the law requires, a broad consensus exists among legal scholars that the law not infrequently “runs out,” leaving the judge to decide the case on extralegal grounds. This Article subjects that consensus to critical scrutiny. Tentatively, the Article concludes that none of the alleged sources of indeterminacy in the law—including permissive rules, balancing tests, vagueness, ambiguity, silence, contradictions, and uncertainty—actually causes the law to run out. More confidently, the Article maintains that the extent to which the law runs out, if it does at all, depends …

Habeas Corpus And Void Judgments, David Kinnaird

Notre Dame Law Review

In a string of recent opinions, Justice Gorsuch and Justice Thomas have posited that postconviction relief upon a writ of habeas corpus was historically unavailable except where the judgment of conviction was issued by a court lacking jurisdiction. In light of this history, Justices Gorsuch and Thomas have argued for a reconsideration of the modern scope of the writ, which generally allows for relitigation of alleged constitutional error in the course of criminal trial.

This Article argues that Justices Gorsuch and Thomas have the history half right. They are correct to assert that a valid judgment of conviction was a …

Beyond Phonics: Neuroaesthetic And Symbolic Divergence In Culturally Responsive Dyslexia Screening, Piper Hutson, James Hutson

Faculty Scholarship

Recent advances in neuroscience and educational technology have challenged the traditional phonological-deficit model of dyslexia, revealing the profound influence of visual-symbolic processing, neuroaesthetic resonance, and cultural context in the reading brain. This article interrogates prevailing assessment paradigms by synthesizing research on the Visual Word Form Area (VWFA), cross-linguistic literacy systems, and emerging neurodiversity frameworks. The study addresses the urgent need to reconceptualize dyslexia as a heterogeneous divergence in symbolic processing—rooted as much in neuroaesthetic and visuospatial alignment as in phonemic awareness. Existing screeners, predominantly optimized for English and other Latin-based scripts, systematically neglect the cognitive realities of learners in multilingual …

European Management Studies 2025, Vol. 23, No. 2

European Management Studies

European Management Studies 2025, vol. 23, no. 2

A Sociocognitive Approach To Second Language Acquisition: Integrating Neurodiversity And Advocacy In Language Learning, Brian Kapustka

Faculty and Staff Scholarship

This project, A Sociocognitive Approach to Second Language Acquisition: Integrating Neurodiversity and Advocacy in Language Learning, serves as a pedagogical framework and instructional resource designed for world language educators. Grounded in Atkinson’s (2011) Sociocognitive SLA model, the project synthesizes cognitive, sociocultural, ecological, and embodied perspectives to illuminate how language learning emerges through dynamic interactions among mind, body, and environment. It further integrates contemporary neurodiversity research—addressing ADHD, ASD, dyslexia, and other cognitive profiles—to propose strength-based, multimodal strategies that support diverse learners. Drawing on the work of Swain, Norton, van Lier, and others, the project emphasizes identity, emotion, and advocacy as …

The Root Cause Of Perceived Appraisal Politics Using Evidence From The South Korean Public Sector, Jinwoong Yoo, Saeyeon Roh, Mohamed Haddoud, Smita Tripathi

Plymouth Business School

The purpose of this study is to investigate the root causes of Perceived Appraisal Politics (PAP) within the public sector in South Korea. Scholars in the field of Human Resource Management (HRM) have identified the determinants of PAP in an effort to mitigate it. However, the complex interactions among those determinants and root causes have been overlooked. Therefore, we adopt a novel methodology in this study that combines Interpretive Structural Modelling (ISM) and fuzzy-Decision-Making Trial and Evaluation Laboratory (f-DEMATEL). The findings indicate that the root causes of PAP include the presence of cliques, ambiguity of appraisal policies and goal ambiguity …

Seeing Culture: A Benchmark For Visual Reasoning And Grounding, Burak Satar, Zhixin Ma, Patrick Amadeus Irrawan, Wilfried Ariel Mulyawan, Jing Jiang, Ee-Peng Lim, Chong-Wah Ngo

Research Collection School Of Computing and Information Systems

Multimodal vision-language models (VLMs) have made substantial progress in various tasks that require a combined understanding of visual and textual content, particularly in cultural understanding tasks, with the emergence of new cultural datasets. However, these datasets frequently fall short of providing cultural reasoning while underrepresenting many cultures.In this paper, we introduce the Seeing Culture Benchmark (SCB), focusing on cultural reasoning with a novel approach that requires VLMs to reason on culturally rich images in two stages: i) selecting the correct visual option with multiple-choice visual question answering (VQA), and ii) segmenting the relevant cultural artifact as evidence of reasoning. Visual …

From Personas To Talks: Revisiting The Impact Of Personas On Llm-Synthesized Emotional Support Conversations, Shenghan Wu, Yimo Zhu, Wynne Hsu, Mong-Li Lee, Yang Deng

Research Collection School Of Computing and Information Systems

The rapid advancement of Large Language Models (LLMs) has revolutionized the generation of emotional support conversations (ESC), offering scalable solutions with reduced costs and enhanced data privacy. This paper explores the role of personas in the creation of ESC by LLMs. Our research utilizes established psychological frameworks to measure and infuse persona traits into LLMs, which then generate dialogues in the emotional support scenario. We conduct extensive evaluations to understand the stability of persona traits in dialogues, examining shifts in traits post-generation and their impact on dialogue quality and strategy distribution. Experimental results reveal several notable findings: 1) LLMs can …

Adasteer: Your Aligned Llm Is Inherently An Adaptive Jailbreak Defender, Weixiang Zhao, Jiahe Guo, Yulin Hu, Yang Deng, An Zhang, Xingyu Sui, Xinyang Han, Yanyan Zhao, Bing Qin, Tat-Seng Chua, Ting Liu

Research Collection School Of Computing and Information Systems

Despite extensive efforts in safety alignment, large language models (LLMs) remain vulnerable to jailbreak attacks. Activation steering offers a training-free defense method but relies on fixed steering coefficients, resulting in suboptimal protection and increased false rejections of benign inputs. To address this, we propose AdaSteer, an adaptive activation steering method that dynamically adjusts model behavior based on input characteristics. We identify two key properties: Rejection Law (R-Law), which shows that stronger steering is needed for jailbreak inputs opposing the rejection direction, and Harmfulness Law (H-Law), which differentiates adversarial and benign inputs. AdaSteer steers input representations along both the Rejection Direction …

Chain Of Strategy Optimization Makes Large Language Models Better Emotional Supporter, Weixiang Zhao, Xingyu Sui, Xinyang Han, Yang Deng, Yulin Hu, Jiahe Guo, Libo Qin, Qianyun Du, Shijin Wang, Yanyan Zhao, Bing Qin, Ting Liu

Research Collection School Of Computing and Information Systems

The growing emotional stress in modern society has increased the demand for Emotional Support Conversations (ESC). While Large Language Models (LLMs) show promise for ESC, they face two key challenges: (1) low strategy selection accuracy, and (2) preference bias, limiting their adaptability to users’ emotional needs. Existing supervised fine-tuning (SFT) struggles to address these issues, as it rigidly trains models on single gold-standard responses without modeling nuanced strategy trade-offs. To overcome these limitations, we propose a novel two-stage framework that optimizes strategy selection preferences at each dialogue turn. We first leverage Monte Carlo Tree Search to construct ESC-Pro, a high-quality …

Exploring Autonomous Agents: A Closer Look At Why They Fail When Completing Tasks, Ruofan Lu, Yichen Li, Yintong Huo

Research Collection School Of Computing and Information Systems

Autonomous agent systems powered by Large Language Models (LLMs) have demonstrated promising capabilities in automating complex tasks. However, current evaluations largely rely on success rates without systematically analyzing the interactions, communication mechanisms, and failure causes within these systems. To bridge this gap, we present a benchmark of 34 representative programmable tasks designed to rigorously assess autonomous agents. Using this benchmark, we evaluate three popular open-source agent frameworks combined with two LLM backbones, observing a task completion rate of approximately 50%. Through in-depth failure analysis, we develop a three-tier taxonomy of failure causes aligned with task phases, highlighting planning errors, task …

Envisioning Future Interactive Web Development: Editing Webpage With Natural Language, Truong Hai Dang, Jingyu Xiao, Yintong Huo

Research Collection School Of Computing and Information Systems

The evolution of web applications relies on iterative code modifications, a process that is traditionally manual and time-consuming. While Large Language Models (LLMs) can generate UI code, their ability to edit existing code from new design requirements (e.g., ”center the logo”) remains a challenge. This is largely due to the absence of large-scale, high-quality tuning data to align model performance with human expectations. In this paper, we introduce a novel, automated data generation pipeline that uses LLMs to synthesize a high-quality fine-tuning dataset for web editing, named Instruct4Edit. Our approach generates diverse instructions, applies the corresponding code modifications, and performs …

Internship, Practicum, And Capstone: A Guide For Health Sciences, Lisa Dunkley

Internship, Practicum, and Capstone: A Guide for Health Sciences

No abstract provided.

How Warm-Versus Competent-Toned Ai Apologies Affect Trust And Forgiveness Through Emotions And Perceived Sincerity, Joon Soo Lim, Nalae Hong, Erika J. Schneider

Media Studies - All Scholarship

As generative artificial intelligence (GenAI) becomes more integrated into corporate communication, its role in crisis messaging raises critical questions about audience perception and trust. Drawing on theories of machine heuristics, this study explores how relational cues in AI-authored crisis apologies shape emotional and cognitive responses that ultimately influence trust and forgiveness. A 3 (authorship attribution: AI vs. human vs. control) x 2 (relational tone: warmth vs. competence) between-subjects factorial design with 464 participants was conducted to assess if and how incorporating a warm tone into AI-generated apologies can help overcome AI's inherent limitations associated with machine heuristics. Results show that …

Composition Pedagogy As Ai‑Native Coding: From Design Kit To Scholarly Framework, Daniel Plate, James Hutson

Faculty Scholarship

This article advances a field-ready framework that reconceives first-year composition as AI-native coding, translating a complete “design kit” into scholarly method, evaluative protocol, and curriculum architecture. Background: Contemporary composition pedagogy emphasizes process, genre awareness, and collaborative revision; meanwhile, modern software practice operationalizes iteration through version control, test-driven development, and continuous integration. The uploaded kit demonstrates that these cultures are isomorphic: writing stages align with SDLC phases, and automated pipelines can lint prose, execute argument “tests,” and publish artifacts with auditable histories. Approach: The study systematizes that kit into (1) a conceptual map that recasts authorship as orchestration and verification, (2) …

A Framework To Retrieve Relevant Laws For Will Execution, Md Asiful Islam, Alice Saebom Kwak, Derek E. Bambauer, Clayton T. Morrison, Mihai Surdeanu

UF Law Faculty Publications

Wills must comply with jurisdiction-specific statutory provisions to be valid, but retrieving the relevant laws for execution, validation, and probate remains labor-intensive and error-prone. Prior legal information retrieval (LIR) research has addressed contracts, criminal law, and judicial decisions, but wills and probate law remain largely unexplored, with no prior work on retrieving statutes for will validity assessment. We propose a legal information retrieval framework that combines lexical and semantic retrieval in a hybrid pipeline with large language model (LLM) reasoning to retrieve the most relevant provisions for a will statement. Evaluations on annotated will-statement datasets from the U.S. states of …

Presidentialism In Post-Conflict Kachin Constitution: Political Imagination, Institutional Realities, And Executive Design, Dashi Blessing Seng Htoi Lu

Maurer Theses and Dissertations

This thesis examines the relationship between cultural identity, political imagination, and constitutional design in the Kachin context, focusing on the strong preference for presidentialism among political thinkers and interim constitution drafters. Rooted in traditions of armed resistance, charismatic leadership, and the desire for visible sovereignty, presidentialism is widely viewed as the most viable executive model for a post-conflict Kachin future. Yet this preference is often shaped more by symbolic ideals than by a clear understanding of the structural demands of democratic governance.

At the heart of this vision lies the expectation of a strong president: directly elected, culturally rooted, and …

The Use Of Science In Climate Governance: Bridging Diplomacy, Public Engagement, And Negotiation Interfaces, Janice Golding

New England Journal of Public Policy

Climate science appears to have come of age, marked by methodological rigor, interdisciplinary integration, and broad consensus. Negotiators in climate multilateralism navigate a complex terrain shaped by politics, economics, and social dynamics, where science may be treated as an insouciant factor in negotiation strategies. As climate change intensifies beyond the 1.5°C threshold, there is a growing risk that science will become even more marginalized in global governance. This could drown out the scientific authority of mandated institutions and create a disconnect between negotiation processes and the evolving role of science, undermining science’s potential to offer renewed relevance and solutions. Urgent …

How Warm-Versus Competent-Toned Ai Apologies Affect Trust And Forgiveness Through Emotions And Perceived Sincerity, Joon Soo Lim, Erika Schneider

Media Studies - All Scholarship

As generative artificial intelligence (GenAI) becomes more integrated into corporate communication, its role in crisis messaging raises critical questions about audience perception and trust. Drawing on theories of machine heuristics, this study explores how relational cues in AI-authored crisis apologies shape emotional and cognitive responses that ultimately influence trust and forgiveness. A 3 (authorship attribution: AI vs. human vs. control) x 2 (relational tone: warmth vs. competence) between-subjects factorial design with 464 participants was conducted to assess if and how incorporating a warm tone into AI-generated apologies can help overcome AI’s inherent limitations associated with machine heuristics. Results show that …

The Myth Of Mark-To-Market Trader Taxation, Misunderstood, Misused, And Remedied, Simyllina Chen

University of the Pacific Law Review

No abstract provided.

Equality For All In Employment Law: Section 1981 And The Struggle For Equality Amongst Citizens And Non-Citizens, Rebekah Mar

University of the Pacific Law Review

No abstract provided.

Integrating Goal-Setting Into An Urban Community College Developmental Mathematics Course: A Multi-Perspective Learning Experience, Tamie S. Dickson

Education Doctorate Dissertations

Developmental mathematics (DM) students often encounter systemic and contextual obstacles in higher education. Low self-efficacy, math anxiety, multiple external demands, and pervasive gaps in prerequisite knowledge can all undermine the strategic behaviors needed to manage the academic environment independently. Although DM reforms are widely researched, the literature lacks a firsthand perspective on the daily processes students apply as they assume control over their learning. This qualitative action research study investigated the integration of intentional goal-setting into a recently redesigned one-level pilot DM course at an open-access, urban community college. The practitioner-researcher created the PATHway framework—rooted in Zimmerman’s (2000) three-phase Self-Regulated …

Llms Are Bad Judges. So Use A Classifier Instead., Jack Kieffaber, Kimo Gandall, Steven Foster, Kenny Mclaren

SMU Law Review Forum

Large Language Models suffer from prompt variance—meaning they’ll give you totally different legal answers depending on how you phrase your question. Jonathan Choi demonstrated this recently when he asked ChatGPT five legal questions, each rephrased 2,000 times, and watched as the bot spat out different answers every time. When you tell somebody that AI is going to replace the judge, the lawyer, and the legal system in the next twenty years, Choi’s article has become the go-to rebuttal; it’s the crown jewel of the “AI bad” genre.

Choi’s absolutely right that LLM’s are bad judges. And, if every AI was …

Do Code Semantics Help? A Comprehensive Study On Execution Trace-Based Information For Code Large Language Models, Jian Wang, Xiaofei Xie, Qiang Hu, Shangqing Liu, Yi Li

Research Collection School Of Computing and Information Systems

Code Large Language Models (Code LLMs) have opened a new era in programming with their impressive capabilities. However, recent research has revealed critical limitations in their ability to reason about runtime behavior and understand the actual functionality of programs, which poses significant challenges for their post-training and practical deployment. Specifically, Code LLMs encounter two principal issues: (1) a lack of proficiency in reasoning about program execution behavior, as they struggle to interpret what programs actually do during runtime, and (2) inconsistent and fragmented representation of semantic information, such as execution traces, across existing methods, which hinders their ability to generalize …

Being The “Other”: How Teaching Internationally Impacts Teacher Candidates’ Culturally Sustaining Practices, Jeanne Carey Ingle, Emily Spitzman, Sarah Thomas

Bridgewater Review

In an increasingly globalized world, preparing teachers to work with culturally and linguistically diverse (CLD) learners has become a critical component of teacher education programs. The recently revised Massachusetts Department of Elementary and Secondary Education (DESE) Candidate Assessment of Performance (CAP) guidelines emphasize the need for teacher candidates to demonstrate growth in their culturally sustaining pedagogy (DESE, 2025). However, traditional approaches to cultural responsiveness in teacher preparation often fall short of comprehensive, practice-oriented methods that foster connections to classroom practice (Gulya & Fehérváry, 2023).

Bridgewater Review Volume 43 Number 2 2025

Bridgewater Review

No abstract provided.

Efficient Integration Of External Knowledge To Llm-Based World Models Via Retrieval-Augmented Generation And Reinforcement Learning, Chang Yang, Xinrun Wang, Qinggang Zhang, Qi Jiang, Xiao Huang

Research Collection School Of Computing and Information Systems

World models achieve remarkable success in predicting future states and planning in complex environments and Large Language Models (LLMs) serve as promising foundation to build general world models. However, their performances are usually constrained by the limited external knowledge to specific environments. Existing research attempts to enhance LLM-based world models through prompting or fine-tuning approaches, which are either requiring human knowledge or computationally extensive. Therefore, we introduce Retrieval-Augmented World Models (RAWM), a novel framework that leverages retrieval-augmented generation to efficiently integrate the external knowledge to LLM-based world models. Our main contributions are threefold: (i) We introduce a memory system and …

One Planner To Guide Them All! Learning Adaptive Conversational Planners For Goal-Oriented Dialogues, Huy Dao, Lizi Liao

Research Collection School Of Computing and Information Systems

Goal-oriented dialogues, such as recommendation and negotiation, often require balancing multiple, conflicting objectives. Existing methods typically involve training separate models for specific combinations of objectives, leading to computational and scalability issues. In this work, we aim to develop a new dialogue policy method that can adapt to varying objective preferences at inference time without retraining. This raises several challenges in terms of both (1) optimization strategy and (2) knowledge utilization. To address these, we propose a novel learning framework, Preference Adaptive Dialogue Policy Planner (PADPP), for multi-objective goal-oriented dialogues. Specifically, to tackle the former, we introduce a novel policy optimization …