Abstract
Artificial intelligence systems increasingly mediate knowledge, communication, and decision making. Development and governance remain concentrated within a small set of firms and states, raising concerns that technologies may encode narrow interests and limit public agency. Capability benchmarks for language, vision, and coding are common, yet public, auditable measures of pluralistic governance are rare. We define AI pluralism as the degree to which affected stakeholders can shape objectives, data practices, safeguards, and deployment. We present the AI Pluralism Index (AIPI), a transparent, evidence-based instrument that evaluates producers and system families across four pillars: participatory governance, inclusivity and diversity, transparency, and accountability. AIPI codes verifiable practices from public artifacts and independent evaluations, explicitly handling "Unknown" evidence to report both lower-bound ("evidence") and known-only scores with coverage. We formalize the measurement model; implement a reproducible pipeline that integrates structured web and repository analysis, external assessments, and expert interviews; and assess reliability with inter-rater agreement, coverage reporting, cross-index correlations, and sensitivity analysis. The protocol, codebook, scoring scripts, and evidence graph are maintained openly with versioned releases and a public adjudication process. We report pilot provider results and situate AIPI relative to adjacent transparency, safety, and governance frameworks. The index aims to steer incentives toward pluralistic practice and to equip policymakers, procurers, and the public with comparable evidence.

AI Insights

• Imagine model cards closing the AI accountability gap by transparently reporting model behavior.
• OECD AI Recommendation pushes for human‑centered, explainable, and fair AI.
• UNESCO Ethics Recommendation embeds human values to turn AI into societal good.
• HELM from Stanford’s CRFM holistically benchmarks language models on safety and impact.
• NIST AI RMF offers a risk‑management cycle for responsible AI governance.
• WCAG 2.2 ensures AI interfaces are accessible to users with disabilities.
• Krippendorff’s content‑analysis method quantifies stakeholder participation in AI governance.

Abstract
Digital technologies are transforming democratic life in conflicting ways. This article bridges two perspectives to unpack these tensions. First, we present an original survey of software developers in Silicon Valley, interrogating how coder worldviews, ethics, and workplace cultures shape the democratic potential and social impact of the technologies they build. Results indicate that while most developers recognize the power of their products to influence civil liberties and political discourse, they often face ethical dilemmas and top-down pressures that can lead to design choices undermining democratic ideals. Second, we critically investigate these findings in the context of an emerging new digital divide, not of internet access but of information quality. We interrogate the survey findings in the context of the Slop Economy, in which billions of users unable to pay for high-quality content experience an internet dominated by low-quality, AI-generated ad-driven content. We find a reinforcing cycle between tech creator beliefs and the digital ecosystems they spawn. We discuss implications for democratic governance, arguing for more ethically informed design and policy interventions to help bridge the digital divide to ensure that technological innovation supports rather than subverts democratic values in the next chapter of the digital age.

AI Insights

• The Slop Economy shows billions consuming low‑quality AI ads, widening the information gap.
• Coders report top‑down pressures that push designs away from democratic ideals.
• A reinforcing loop links coder worldviews, platform design, and user beliefs.
• Link‑recommendation algorithms turn feeds into echo chambers, amplifying polarization.
• Responsible innovation must prioritize human values over profit, reshaping engineering ethics.
• Participatory democracy and civic literacy are key to countering AI‑generated misinformation.

Abstract
This is a skeptical overview of the literature on AI consciousness. We will soon create AI systems that are conscious according to some influential, mainstream theories of consciousness but are not conscious according to other influential, mainstream theories of consciousness. We will not be in a position to know which theories are correct and whether we are surrounded by AI systems as richly and meaningfully conscious as human beings or instead only by systems as experientially blank as toasters. None of the standard arguments either for or against AI consciousness takes us far. Table of Contents Chapter One: Hills and Fog Chapter Two: What Is Consciousness? What Is AI? Chapter Three: Ten Possibly Essential Features of Consciousness Chapter Four: Against Introspective and Conceptual Arguments for Essential Features Chapter Five: Materialism and Functionalism Chapter Six: The Turing Test and the Chinese Room Chapter Seven: The Mimicry Argument Against AI Consciousness Chapter Eight: Global Workspace Theories and Higher Order Theories Chapter Nine: Integrated Information, Local Recurrence, Associative Learning, and Iterative Natural Kinds Chapter Ten: Does Biological Substrate Matter? Chapter Eleven: The Problem of Strange Intelligence Chapter Twelve: The Leapfrog Hypothesis and the Social Semi-Solution

Abstract
Accurate time-series predictions in machine learning are heavily influenced by the selection of appropriate input time length and sampling rate. This paper introduces ATLO-ML, an adaptive time-length optimization system that automatically determines the optimal input time length and sampling rate based on user-defined output time length. The system provides a flexible approach to time-series data pre-processing, dynamically adjusting these parameters to enhance predictive performance. ATLO-ML is validated using air quality datasets, including both GAMS-dataset and proprietary data collected from a data center, both in time series format. Results demonstrate that utilizing the optimized time length and sampling rate significantly improves the accuracy of machine learning models compared to fixed time lengths. ATLO-ML shows potential for generalization across various time-sensitive applications, offering a robust solution for optimizing temporal input parameters in machine learning workflows.

AI Insights

• ATLO‑ML leverages LightGBM as the top performer for real‑time CO₂ prediction.
• The system’s adaptive sampling rate is tuned per user‑defined forecast horizon, not fixed.
• Data quality and preprocessing are flagged as critical, yet the paper assumes clean inputs.
• Comparative analysis shows XGBoost and Random Forest lag behind LightGBM in both accuracy and speed.
• Potential deployments span healthcare, education, and commercial buildings, beyond data centers.
• Future work must address scalability, sensor drift, and inclusion of temperature/humidity factors.
• Recommended reading: Pattern Recognition and Machine Learning; XGBoost and LightGBM papers for deeper insight.

Abstract
We review the historical development and current trends of artificially intelligent agents (agentic AI) in the social and behavioral sciences: from the first programmable computers, and social simulations soon thereafter, to today's experiments with large language models. This overview emphasizes the role of AI in the scientific process and the changes brought about, both through technological advancements and the broader evolution of science from around 1950 to the present. Some of the specific points we cover include: the challenges of presenting the first social simulation studies to a world unaware of computers, the rise of social systems science, intelligent game theoretic agents, the age of big data and the epistemic upheaval in its wake, and the current enthusiasm around applications of generative AI, and many other topics. A pervasive theme is how deeply entwined we are with the technologies we use to understand ourselves.

AI Insights

• LLMs now act as adaptive interviewers, tailoring survey questions in real time.
• Empirical studies show LLM prompts can shift spoken communication, revealing AI‑mediated influence.
• The replication crisis in language‑model behavior research drives new safeguards and transparent benchmarks.
• Computational social science uses LLM embeddings to map large‑scale discourse networks with unprecedented detail.
• LLMs inherit systemic biases, demanding rigorous audit frameworks before policy use.
• Waldrop’s “Complexity” and Wiener’s “The Human Use of Human Beings” frame AI agents’ socio‑technical dynamics.
• Future research must balance LLMs’ discovery speed with ethical risks, calling for interdisciplinary governance.

Abstract
Jet substructure provides one of the most exciting new approaches for searching for physics in and beyond the Standard Model at the Large Hadron Collider. Modern jet substructure searches are often performed with Neural Network (NN) taggers which study the jets' radiation distributions in great detail, far beyond what is theoretically described by parton shower generators. While this represents a great opportunity, as NNs look deeper into the structure of jets they become increasingly sensitive both to perturbative and non-perturbative theoretical uncertainties. It is therefore important to be able to control which aspects of both regimes the networks focus on, and to develop techniques for quantifying these uncertainties. In this paper we take two steps in this direction: First, we introduce EnFNs, a generalization of the Energy Flow Networks (EFNs) which directly probes higher point correlations in jets, as motivated by recent advances in the study of energy correlators. Second, we introduce a number of techniques to quantify and visualize their robustness to non-perturbative corrections. We highlight the importance of such considerations in a toy study incorporating systematics into a search, and maximizing for the network's discovery significance, as opposed to absolute tagging performance. We hope this study continues the interest in understanding the role QCD systematics play in Machine Learning applications and opens the door to a better interplay between theory and experiment in HEP.

AI Insights

• EnFNs add N‑point energy correlators to EFNs, probing multi‑particle correlations beyond pairwise.
• Visualization tools map non‑perturbative sensitivity, letting users steer networks toward perturbative observables.
• Optimizing for discovery significance, not raw accuracy, reduces hadronization‑induced biases in toy studies.
• FastEEC benchmarking shows EnFNs match speed while keeping full angular‑correlation data.
• PyTorch + FastJet integration gives end‑to‑end training pipelines, widening community reach.
• Alaric parton‑shower model is cited as a promising way to cut simulation uncertainties.
• Recommended papers: Komiske et al. on Energy Flow Networks and Budhraja & Waalewijn on FastEEC.

Abstract
This study evaluates Artificial Intelligence (AI) agents for Dhumbal, a culturally significant multiplayer card game with imperfect information, through a systematic comparison of rule-based, search-based, and learning-based strategies. We formalize Dhumbal's mechanics and implement diverse agents, including heuristic approaches (Aggressive, Conservative, Balanced, Opportunistic), search-based methods such as Monte Carlo Tree Search (MCTS) and Information Set Monte Carlo Tree Search (ISMCTS), and reinforcement learning approaches including Deep Q-Network (DQN) and Proximal Policy Optimization (PPO), and a random baseline. Evaluation involves within-category tournaments followed by a cross-category championship. Performance is measured via win rate, economic outcome, Jhyap success, cards discarded per round, risk assessment, and decision efficiency. Statistical significance is assessed using Welch's t-test with Bonferroni correction, effect sizes via Cohen's d, and 95% confidence intervals (CI). Across 1024 simulated rounds, the rule-based Aggressive agent achieves the highest win rate (88.3%, 95% CI: [86.3, 90.3]), outperforming ISMCTS (9.0%) and PPO (1.5%) through effective exploitation of Jhyap declarations. The study contributes a reproducible AI framework, insights into heuristic efficacy under partial information, and open-source code, thereby advancing AI research and supporting digital preservation of cultural games.

Abstract
Generative AI does more than cut costs. It pulls products toward a shared template, making offerings look and feel more alike while making true originality disproportionately expensive. We capture this centripetal force in a standard two-stage differentiated-competition framework and show how a single capability shift simultaneously compresses perceived differences, lowers marginal cost and raises fixed access costs. The intuition is straightforward. When buyers see smaller differences across products, the payoff to standing apart shrinks just as the effort to do so rises, so firms cluster around the template. Prices fall and customers become more willing to switch. But the same homogenization also squeezes operating margins, and rising fixed outlays deepen the squeeze. The combination yields a structural prediction. There is a capability threshold at which even two firms cannot both cover fixed costs, and in a many-firm extension the sustainable number of firms falls as capability grows. Concentration increases, and prices still fall. Our results hold under broader preference shapes, non-uniform consumer densities, outside options, capability-dependent curvatures, and modest asymmetries. We translate the theory into two sufficient statistics for enforcement. On the one hand, a conduct statistic and a viability statistic. Transactions or platform rules that strengthen template pull or raise fixed access and originality costs can lower prices today yet push the market toward monoculture. Remedies that broaden access and promote template plurality and interoperability preserve the price benefits of AI while protecting entry and variety. The paper thus reconciles a live policy paradox. AI can make prices lower and entry harder at the same time. It prescribes what to measure to tell which force is dominant in practice.

Abstract
This article proposes predictive economics as a distinct analytical perspective within economics, grounded in machine learning and centred on predictive accuracy rather than causal identification. Drawing on the instrumentalist tradition (Friedman), the explanation-prediction divide (Shmueli), and the contrast between modelling cultures (Breiman), we formalise prediction as a valid epistemological and methodological objective. Reviewing recent applications across economic subfields, we show how predictive models contribute to empirical analysis, particularly in complex or data-rich contexts. This perspective complements existing approaches and supports a more pluralistic methodology - one that values out-of-sample performance alongside interpretability and theoretical structure.

AI Insights

• Lucas critique warns that predictive models may fail when agents adapt, highlighting structural safeguards.
• Black‑box algorithms raise accountability issues in justice and health, demanding interpretable ML.
• Predictive economics turns data streams into real‑time policy nudges.
• Personalized forecasts from ML ensembles enable tailored welfare interventions.
• Methodological pluralism lets predictive accuracy, causal inference, and theory coexist.
• Read The Economics of Artificial Intelligence: An Agenda for a multidisciplinary AI‑economics view.
• Predictive economics: a paradigm that prioritizes out‑of‑sample accuracy as a scientific objective.

Abstract
Over the past decade, an ecosystem of measures has emerged to evaluate the social and ethical implications of AI systems, largely shaped by high-level ethics principles. These measures are developed and used in fragmented ways, without adequate attention to how they are situated in AI systems. In this paper, we examine how existing measures used in the computing literature map to AI system components, attributes, hazards, and harms. Our analysis draws on a scoping review resulting in nearly 800 measures corresponding to 11 AI ethics principles. We find that most measures focus on four principles - fairness, transparency, privacy, and trust - and primarily assess model or output system components. Few measures account for interactions across system elements, and only a narrow set of hazards is typically considered for each harm type. Many measures are disconnected from where harm is experienced and lack guidance for setting meaningful thresholds. These patterns reveal how current evaluation practices remain fragmented, measuring in pieces rather than capturing how harms emerge across systems. Framing measures with respect to system attributes, hazards, and harms can strengthen regulatory oversight, support actionable practices in industry, and ground future research in systems-level understanding.

Abstract
Despite conflicting definitions and conceptions of fairness, AI fairness researchers broadly agree that fairness is context-specific. However, when faced with general-purpose AI, which by definition serves a range of contexts, how should we think about fairness? We argue that while we cannot be prescriptive about what constitutes fair outcomes, we can specify the processes that different stakeholders should follow in service of fairness. Specifically, we consider the obligations of two major groups: system providers and system deployers. While system providers are natural candidates for regulatory attention, the current state of AI understanding offers limited insight into how upstream factors translate into downstream fairness impacts. Thus, we recommend that providers invest in evaluative research studying how model development decisions influence fairness and disclose whom they are serving their models to, or at the very least, reveal sufficient information for external researchers to conduct such research. On the other hand, system deployers are closer to real-world contexts and can leverage their proximity to end users to address fairness harms in different ways. Here, we argue they should responsibly disclose information about users and personalization and conduct rigorous evaluations across different levels of fairness. Overall, instead of focusing on enforcing fairness outcomes, we prioritize intentional information-gathering by system providers and deployers that can facilitate later context-aware action. This allows us to be specific and concrete about the processes even while the contexts remain unknown. Ultimately, this approach can sharpen how we distribute fairness responsibilities and inform more fluid, context-sensitive interventions as AI continues to advance.

AI Insights

• Generative AI can boost STEM learning in underfunded schools, yet deployment must be ethically vetted.
• Actionable Auditing shows that publicly naming biased performance can spur vendor reform.
• “Closing the AI Accountability Gap” proposes an end‑to‑end internal auditing framework for commercial models.
• “Fairness and Abstraction in Sociotechnical Systems” insists fairness be built into design, not added later.
• “The Algorithmic Society” examines how tech reshapes power, urging interdisciplinary oversight.
• Surveys now favor context‑specific bias metrics over single‑threshold tests.
• AI ethics studies moral principles guiding AI, while bias denotes unfair patterns from data or algorithms.

Abstract
AI-powered road surveillance systems are increasingly proposed to monitor infractions such as speeding, phone use, and jaywalking. While these systems promise to enhance safety by discouraging dangerous behaviors, they also raise concerns about privacy, fairness, and potential misuse of personal data. Yet empirical research on how people perceive AI-enhanced monitoring of public spaces remains limited. We conducted an online survey ($N=720$) using a 3$\times$3 factorial design to examine perceptions of three road surveillance modes -- conventional, AI-enhanced, and AI-enhanced with public shaming -- across China, Europe, and the United States. We measured perceived capability, risk, transparency, and acceptance. Results show that conventional surveillance was most preferred, while public shaming was least preferred across all regions. Chinese respondents, however, expressed significantly higher acceptance of AI-enhanced modes than Europeans or Americans. Our findings highlight the need to account for context, culture, and social norms when considering AI-enhanced monitoring, as these shape trust, comfort, and overall acceptance.

AI Insights

• Younger drivers show higher acceptance of traffic cameras, while older drivers remain skeptical.
• Empirical evidence links AI‑enhanced surveillance to measurable reductions in accidents and fatalities.
• Drivers report discomfort when cameras are perceived as intrusive, which can dampen overall acceptance.
• Regional attitudes vary sharply; some jurisdictions exhibit markedly higher trust in AI monitoring than others.
• Policymakers should weigh cultural norms and age demographics when deploying AI‑based traffic systems.
• The study’s reliance on self‑reported data and a 1,000‑driver sample may limit generalizability.
• Recommended reading: “Traffic Safety: A Review of the Literature” and “Human Factors in Traffic Safety” for deeper context.

Abstract
Accurate, scalable traffic monitoring is critical for real-time and long-term transportation management, particularly during disruptions such as natural disasters, large construction projects, or major policy changes like New York City's first-in-the-nation congestion pricing program. However, widespread sensor deployment remains limited due to high installation, maintenance, and data management costs. While traffic cameras offer a cost-effective alternative, existing video analytics struggle with dynamic camera viewpoints and massive data volumes from large camera networks. This study presents an end-to-end AI-based framework leveraging existing traffic camera infrastructure for high-resolution, longitudinal analysis at scale. A fine-tuned YOLOv11 model, trained on localized urban scenes, extracts multimodal traffic density and classification metrics in real time. To address inconsistencies from non-stationary pan-tilt-zoom cameras, we introduce a novel graph-based viewpoint normalization method. A domain-specific large language model was also integrated to process massive data from a 24/7 video stream to generate frequent, automated summaries of evolving traffic patterns, a task far exceeding manual capabilities. We validated the system using over 9 million images from roughly 1,000 traffic cameras during the early rollout of NYC congestion pricing in 2025. Results show a 9% decline in weekday passenger vehicle density within the Congestion Relief Zone, early truck volume reductions with signs of rebound, and consistent increases in pedestrian and cyclist activity at corridor and zonal scales. Experiments showed that example-based prompts improved LLM's numerical accuracy and reduced hallucinations. These findings demonstrate the framework's potential as a practical, infrastructure-ready solution for large-scale, policy-relevant traffic monitoring with minimal human intervention.

Abstract
Intensive Care Unit (ICU) mortality prediction, which estimates a patient's mortality status at discharge using EHRs collected early in an ICU admission, is vital in critical care. For this task, predictive accuracy alone is insufficient; interpretability is equally essential for building clinical trust and meeting regulatory standards, a topic that has attracted significant attention in information system research. Accordingly, an ideal solution should enable intrinsic interpretability and align its reasoning with three key elements of the ICU decision-making practices: clinical course identification, demographic heterogeneity, and prognostication awareness. However, conventional approaches largely focus on demographic heterogeneity, overlooking clinical course identification and prognostication awareness. Recent prototype learning methods address clinical course identification, yet the integration of the other elements into such frameworks remains underexplored. To address these gaps, we propose ProtoDoctor, a novel ICU mortality prediction framework that delivers intrinsic interpretability while integrating all three elements of the ICU decision-making practices into its reasoning process. Methodologically, ProtoDoctor features two key innovations: the Prognostic Clinical Course Identification module and the Demographic Heterogeneity Recognition module. The former enables the identification of clinical courses via prototype learning and achieves prognostication awareness using a novel regularization mechanism. The latter models demographic heterogeneity through cohort-specific prototypes and risk adjustments. Extensive empirical evaluations demonstrate that ProtoDoctor outperforms state-of-the-art baselines in predictive accuracy. Human evaluations further confirm that its interpretations are more clinically meaningful, trustworthy, and applicable in ICU practice.

Abstract
With the rapid progress of Large Language Models (LLMs), the general public now has easy and affordable access to applications capable of answering most health-related questions in a personalized manner. These LLMs are increasingly proving to be competitive, and now even surpass professionals in some medical capabilities. They hold particular promise in low-resource settings, considering they provide the possibility of widely accessible, quasi-free healthcare support. However, evaluations that fuel these motivations highly lack insights into the social nature of healthcare, oblivious to health disparities between social groups and to how bias may translate into LLM-generated medical advice and impact users. We provide an exploratory analysis of LLM answers to a series of medical questions spanning key clinical domains, where we simulate these questions being asked by several patient profiles that vary in sex, age range, and ethnicity. By comparing natural language features of the generated responses, we show that, when LLMs are used for medical advice generation, they generate responses that systematically differ between social groups. In particular, Indigenous and intersex patients receive advice that is less readable and more complex. We observe these trends amplify when intersectional groups are considered. Considering the increasing trust individuals place in these models, we argue for higher AI literacy and for the urgent need for investigation and mitigation by AI developers to ensure these systemic differences are diminished and do not translate to unjust patient support. Our code is publicly available on GitHub.

Abstract
The growing philosophical literature on algorithmic fairness has examined statistical criteria such as equalized odds and calibration, causal and counterfactual approaches, and the role of structural and compounding injustices. Yet an important dimension has been overlooked: whether the evidential value of an algorithmic output itself depends on structural injustice. Our paradigmatic pair of examples contrasts a predictive policing algorithm, which relies on historical crime data, with a camera-based system that records ongoing offenses, both designed to guide police deployment. In evaluating the moral acceptability of acting on a piece of evidence, we must ask not only whether the evidence is probative in the actual world, but also whether it would remain probative in nearby worlds without the relevant injustices. The predictive policing algorithm fails this test, but the camera-based system passes it. When evidence fails the test, it is morally problematic to use it punitively, more so than evidence that passes the test.

Abstract
Underground water and wastewater pipelines are vital for city operations but plagued by anomalies like leaks and infiltrations, causing substantial water loss, environmental damage, and high repair costs. Conventional manual inspections lack efficiency, while dense sensor deployments are prohibitively expensive. In recent years, artificial intelligence has advanced rapidly and is increasingly applied to urban infrastructure. In this research, we propose an integrated AI and remote-sensor framework to address the challenge of leak detection in underground water pipelines, through deploying a sparse set of remote sensors to capture real-time flow and depth data, paired with HydroNet - a dedicated model utilizing pipeline attributes (e.g., material, diameter, slope) in a directed graph for higher-precision modeling. Evaluations on a real-world campus wastewater network dataset demonstrate that our system collects effective spatio-temporal hydraulic data, enabling HydroNet to outperform advanced baselines. This integration of edge-aware message passing with hydraulic simulations enables accurate network-wide predictions from limited sensor deployments. We envision that this approach can be effectively extended to a wide range of underground water pipeline networks.

Abstract
Recent advances in machine learning such as Long Short-Term Memory (LSTM) models and Transformers have been widely adopted in hydrological applications, demonstrating impressive performance amongst deep learning models and outperforming physical models in various tasks. However, their superiority in predicting land surface states such as terrestrial water storage (TWS) that are dominated by many factors such as natural variability and human driven modifications remains unclear. Here, using the open-access, globally representative HydroGlobe dataset - comprising a baseline version derived solely from a land surface model simulation and an advanced version incorporating multi-source remote sensing data assimilation - we show that linear regression is a robust benchmark, outperforming the more complex LSTM and Temporal Fusion Transformer for TWS prediction. Our findings highlight the importance of including traditional statistical models as benchmarks when developing and evaluating deep learning models. Additionally, we emphasize the critical need to establish globally representative benchmark datasets that capture the combined impact of natural variability and human interventions.

Abstract
Data lakehouses run sensitive workloads, where AI-driven automation raises concerns about trust, correctness, and governance. We argue that API-first, programmable lakehouses provide the right abstractions for safe-by-design, agentic workflows. Using Bauplan as a case study, we show how data branching and declarative environments extend naturally to agents, enabling reproducibility and observability while reducing the attack surface. We present a proof-of-concept in which agents repair data pipelines using correctness checks inspired by proof-carrying code. Our prototype demonstrates that untrusted AI agents can operate safely on production data and outlines a path toward a fully agentic lakehouse.

Abstract
In the rapidly evolving educational landscape, the integration of technology has shifted from an enhancement to a cornerstone of educational strategy worldwide. This transition is propelled by advancements in digital technology, especially the emergence of artificial intelligence as a crucial tool in learning environments. This research project critically evaluates the impact of three distinct educational settings: traditional educational methods without technological integration, those enhanced by non-AI technology, and those utilising AI-driven technologies. This comparison aims to assess how each environment influences educational outcomes, engagement, pedagogical methods, and equity in access to learning resources, and how each contributes uniquely to the learning experience. The ultimate goal of this research is to synthesise the strengths of each model to create a more holistic educational approach. By integrating the personal interaction and tested pedagogical techniques of traditional classrooms, the enhanced accessibility and collaborative tools offered by non-AI technology, and the personalised, adaptive learning strategies enabled by AI-driven technologies, education systems can develop richer, more effective learning environments. This hybrid approach aims to leverage the best elements of each setting, thereby enhancing educational outcomes, engagement, and inclusiveness, while also addressing the distinct challenges and limitations inherent in each model. The intention is to create an educational framework deeply attentive to the diverse needs of students, ensuring equitable access to high-quality education for all.

Abstract
The proliferation of Large Language Models in higher education presents a fundamental challenge to traditional pedagogical frameworks. Drawing on Jacques Ranci\`ere's theory of intellectual emancipation, this paper examines how generative AI risks becoming a "mechanical yes-man" that reinforces passivity rather than fostering intellectual autonomy. Generative AI's statistical logic and lack of causal reasoning, combined with frictionless information access, threatens to hollow out cognitive processes essential for genuine learning. This creates a critical paradox: while generative AI systems are trained for complex reasoning, students increasingly use them to bypass the intellectual work that builds such capabilities. The paper critiques both techno-optimistic and restrictive approaches to generative AI in education, proposing instead an emancipatory pedagogy grounded in verification, mastery, and co-inquiry. This framework positions generative AI as material for intellectual work rather than a substitute for it, emphasising the cultivation of metacognitive awareness and critical interrogation of AI outputs. It requires educators to engage directly with these tools to guide students toward critical AI literacy, transforming pedagogical authority from explication to critical interloping that models intellectual courage and collaborative inquiry.