139 papers
This paper presents a machine learning-focused survey of Computerized Adaptive Testing (CAT), exploring how ML techniques can optimize measurement models, question selection, bank construction, and test control to create more robust, fair, and efficient adaptive assessment systems across various domains.
This paper introduces DeepScope, a deep learning-based system that analyzes microscopic images of unincubated water samples to detect fecal contamination in seconds with 93% accuracy and a cost of $0.44 per test, significantly outperforming traditional incubation methods in speed and affordability.
This paper introduces XInsight, a multi-agent framework that aligns psychological support with the Exploration-Insight-Action paradigm through a structured Reason-Intervene-Reflect cycle to enhance interpretability and therapeutic effectiveness in digital well-being applications, accompanied by the XInsight-Bench evaluation protocol.
This paper proposes an AI-driven framework that leverages CodeBERT embeddings and XGBoost to perform static, explainable risk scoring of SQL-based metric definitions, enabling healthcare institutions to proactively identify and mitigate privacy risks in aggregated data before deployment while ensuring regulatory compliance and auditability.
This paper presents a pioneering case study of *R v F* (2025) that demonstrates how digital forensic investigators can empirically counter the "hacking" or "SODDI" defence by collaborating with law enforcement to provide evidence that helps courts distinguish between innocent and guilty defendants.
This study analyzes Italian music listeners' narratives to reveal their routine yet uncritical engagement with recommender systems and limited awareness of gender-related representational harms, arguing for the integration of psychosocial insights into the design of culturally sensitive algorithms.
This study evaluates Shiksha Copilot, an AI-assisted tool deployed in Karnataka, India, which successfully reduced teachers' administrative burdens and stress while fostering activity-based pedagogy through a human-AI collaborative workflow, though its broader pedagogical impact was limited by systemic challenges like staffing shortages.
This report summarizes the outcomes of the September 2024 NSF Workshop on Algorithm-Hardware Co-design for Medical Applications, outlining a strategic roadmap and key recommendations to advance next-generation medical technologies across four thematic areas through fundamental shifts in design, validation, and infrastructure.
This study introduces a scalable, spatially explicit framework using geographically weighted classification models to demonstrate that population and built form in Czechia exhibit linear, spatially conditioned relationships, revealing how specific built form types reinforce socio-spatial inequalities.
The paper introduces WalkGPT, a pixel-grounded vision-language model that unifies language reasoning and segmentation to provide depth-aware pedestrian navigation guidance, alongside the PAVE benchmark for evaluating accessibility-aware scene understanding.
This paper presents the proceedings of the inaugural CHI 2026 workshop held in Barcelona, which aims to disentangle the fragmented politics, values, and imaginaries within Human-Computer Interaction by examining them through the lens of ideologies.
This paper integrates AI computing into the Global Change Analysis Model to demonstrate that future U.S. AI electricity demand is determined by the interplay between efficiency improvements and income-driven growth, revealing that price-based mechanisms have limited impact while the balance between efficiency and demand trajectories dictates whether emissions remain moderated or surge by mid-century.
This paper introduces "Machinagogy," an AI tutoring system grounded in Hegelian and Freudian theories that utilizes recognition-enhanced prompts and a multi-agent architecture to significantly improve teaching performance, while simultaneously employing a reflexive "vibe scholarship" methodology where an AI collaborates with a human to build, evaluate, and document the research itself.
This paper presents an empirical study demonstrating that personalizing AI-driven hint explanations in an Intelligent Tutoring System for students with low Need for Cognition and Conscientiousness significantly increases their engagement, understanding, and learning outcomes.
This paper proposes a novel "discriminative risk" measure that unifies group and individual fairness, establishes theoretical margin-dependent bounds demonstrating that ensemble combination can improve fairness, and introduces pruning methods to create accurate and fair sub-ensembles.
This paper synthesizes findings from interviews with 16 experts to identify methodological challenges in applying randomized controlled trials to evaluate frontier AI's impact on human performance and proposes practical solutions to address validity issues in high-stakes decision-making.
This paper presents a human-centred system design that leverages conversational AI and function-calling capabilities to enable natural language querying and visual-spatial exploration of nearly 1.7 million digitised natural history specimen records at the Australian Museum, overcoming the limitations of traditional keyword-based search tools.
This paper diagnoses the legal challenges of identifying autonomous AI agents due to their lack of physical bodies and fluid nature, proposing the "Algorithmic Corporation" (A-corp) as a novel legal entity that ties AI actions to human owners while enabling AI agents to self-organize into persistent, liable units with coherent goals.
This integrative review synthesizes evidence from over 500 studies to argue that while digital technology optimizes short-term task performance, it may simultaneously erode long-term cognitive capabilities and reserve through mechanisms like functional interference and neurochemical dysregulation, particularly as generative AI shifts risks from resource allocation disruptions to the atrophy of higher-order generative skills.
This paper proposes a governance and evaluation framework for deterministic, rule-based clinical decision support systems in empiric antibiotic prescribing that prioritizes transparency, auditability, and conservative behavior by formally separating decision logic from scope constraints and utilizing synthetic case validation to ensure behavioral alignment with predefined rules.