Computers and Society
Learning and Predicting Patent Technology Reuse Trajectories from Emergence-Time Signals
Ayham Yousef, Qiang Ye and Qiang Cheng
Forecasting how a newly emerged patent technology will be reused is central to technology intelligence, but reuse-pattern labels do not exist in advance: they must be constructed from the trajectories themselves, and how they are constructed determines what a forecast means. We study $201{,}710$ novel patent technologies (first-time IPC code pairings, USPTO 2002–2022). Our primary labeling applies $k$-means in the latent space of a GRU autoencoder trained on the $20$-year reuse trajectories, using no hand-crafted features; to our knowledge this is the first use of a learned sequence representation for this task. Seven emergence-time features, observable in a technology's first year, recover these labels at a one-vs-rest macro ROC-AUC of $0.914$, but the calendar year of emergence alone reaches $0.874$. A replication on technologies observed for ten full years, none of them right-censored, indicates that this calendar-year effect mainly reflects change over time in what was patented. Separately we cluster the emergence-time features themselves, after Fractal Autoencoder feature selection. That emergence-profile partition agrees with the GRU-based labels only marginally above chance (Adjusted Rand Index $\approx 0.04$), so a partition of emergence-time features is not a reuse-pattern taxonomy and should not be read as one. On a trajectory-shape task following the published construction, GBDT reaches $0.831$ with all seven features and $0.740$ with the selected subset; the published $0.728$, from a different corpus and labeling, is a reference point rather than a
benchmark. Two features are additionally left-truncated for the earliest cohorts, which we quantify.
Read the full abstract ▾
Computers and Society
Visibility on Terrains
Laura Toma
This chapter surveys terrain visibility models,
algorithms, and applications. It introduces basic definitions, from line-of-sight, single-viewpoint viewsheds to cumulative and total viewsheds, on both Triangulated Irregular Network (TIN) and regular grid terrains. Reviews exact and approximate
algorithms and highlights key application domains including archaeology, visual exposure to greenery, scenic route management, and land conservation.
Read the full abstract ▾
Computers and Society
From Scroll to Sale: Exploring the Impact of Interaction Type and Device Price on TikTok Advertisements
Nazanin Sabri, Cat Mai, Haodi Zou et al. (+4 more)
Companies and brands increasingly use dynamic pricing, including targeting social media ads to users based on their income. In this work we audit TikTok's feed using 56 automated accounts, which collect data on over 80,000
videos, across two studies. We test the impact of device price on ad load and ad types, using 12 phones of low ($0-$250), medium ($400-$650), and high ($750-$1,000+) price as a proxy for income. We also test the impact of interaction type (i.e., like, comment, share), age, and gender on the frequency and content of ads. Overall, the ad load was 29.4%, but liking and sharing content increased ad load significantly. We also found that as accounts spend more time on TikTok, the ad load steadily increases. We found some evidence that device price impacts both ad load and content – more expensive devices were targeted with fewer ads, while the least expensive devices received more discounts.
Read the full abstract ▾
All authors Nazanin Sabri, Cat Mai, Haodi Zou, Isha Varada, Damon McCoy, Deepak Kumar, Kristen Vaccaro
Computers and Society · Cross-listed from cs.AI
Legal Research Bench: Measuring End-to-End Reliability in Long-Horizon Legal Research Agents
Katrina Drozdov, Oliver Chen, Langston Nashold et al. (+1 more)
Legal research is a core and time-consuming legal workflow. Lawyers must identify controlling authority, verify that it remains valid, reconcile statutes and cases, and synthesize a grounded answer.
Language model
agents are a natural fit for this
retrieval-intensive workflow, and automating even part of it would be valuable. But that value depends on reliability: a single missing authority, stale citation, or wrong legal conclusion can make an otherwise plausible answer unusable. We introduce Legal Research Bench (LRB), a
benchmark of 413 open-ended U.S. legal research questions written by experts, each paired with a gold answer, supporting authorities, and a binary grading rubric. We evaluate thirteen frontier models in a harness with web search, case-law search, page parsing, and
retrieval tools. We score
agent responses through all-pass grading with source verification, where a response is correct only if every required criterion is satisfied and its cited authorities verify. We also validate the LLM judge against expert attorneys ensuring that
benchmark scores track attorney judgment.
Agents remain far from reliable: among the models we tested, the strongest, Claude Opus 4.8, is fully correct on 42.9% of questions. Performance also varies substantially by task setting: all-pass rates differ across areas of law and are lower on questions requiring reconciliation of conflicting authorities. Across models, more turns, tool calls, and inference cost do not predict higher accuracy.
Read the full abstract ▾
All authors Katrina Drozdov, Oliver Chen, Langston Nashold, Rayan Krishnan
Computers and Society · Cross-listed from cs.IT
Interpreting Reasoning of Large Language Models via Partial Information Decomposition
Barproda Halder, Qiuyi Zhang and Sanghamitra Dutta
Large
reasoning models (LRMs) have achieved substantial improvements in solving complex mathematical problems, but often produce lengthy, repetitive, or erroneous
reasoning trajectories. In this work, we introduce a new interpretability framework, SLIDER, to evaluate the quality of the
reasoning process. SLIDER leverages an emerging body of work from information theory called Partial Information Decomposition to disentangle the information about the final answer between two consecutive
reasoning steps into non-negative components: unique information (in preceding steps or current step), redundant information, and synergistic information. Building on this decomposition, we propose the *Step-wise Repetitive
Reasoning Index (Step-RRI)*, a theoretically grounded measure that assesses whether the answer-relevant information in the current step $S_i$ is predominantly redundant with the past steps $S_{<i}$, relative to its unique and synergistic contributions. To evaluate the effectiveness of Step-RRI in detecting repetitiveness, we apply SLIDER to the redundancy class of the PRMBench
dataset where Step-RRI improves step-level redundancy identification accuracy by over $10$ points compared to
embedding-similarity and information-gain baselines. Next, we define *Trajectory-RRI*, an aggregate measure of repetitiveness for an individual
reasoning trajectory. To demonstrate its practical relevance, we show that average Trajectory-RRI strongly correlates with actual
reasoning length across QwQ-32B, DeepSeek-R1-Distill-Qwen-32B, and GPT-4.1, motivating its use as a signal for improving
reasoning efficiency. Finally, we introduce *Trajectory-RRI-guided data selection for fine-tuning*, demonstrating that selecting training data based on Trajectory-RRI can improve a fine-tuned model's
reasoning efficiency while largely preserving its task performance.
Read the full abstract ▾
Computers and Society · Cross-listed from cs.AI
Ontology-Based Contextual AI Evaluations (OB-CAIE) Methodology
Julie Krugler Hollek, Michael Zargham and Mala Kumar
The ontology-based contextual AI evaluation (OB-CAIE) methodology was developed to address a lack of scientific rigor that arises from unclear testing coverage, to balance human expertise and automations, and to address a lack of reproducibility of AI evaluation testing environments. OB-CAIE strengthens the current state of AI evaluations by addressing the first step in the scientific method by clearly defining what will be tested. Two ontologies represent the tractable problem space in the OB-CAIE methodology: the Domain-Specific Ontology (DSO) and the Evaluation Process Ontology (EPO). The DSO is the what; the EPO is the how. An OB-CAIE problem space can be used for one or multiple AI evaluations. The OB-CAIE methodology allows for human judgment at specific points, in scientifically grounded ways, and in complex subject areas where human feedback is genuinely irreducible or machine irreplaceable. A key advantage of the OB-CAIE methodology is that failure points can be traced, visualized and analyzed within the canonical OB-CAIE methodology problem space.
Read the full abstract ▾
Computers and Society · Cross-listed from cs.LG
Efficient Active Auditing of Multi-Group Fairness with Bias Probes
Ayoub Ajarra and Debabrota Basu
Over the past decade, Machine Learning (ML) has been trained under dual objectives: minimizing prediction error via Empirical Risk Minimization (ERM) while controlling unfairness bias. In practice, however,
fairness-aware training often yields limited improvements over standard ERM, making reliable post hoc auditing essential. Existing auditing approaches for black-box models either rely on model reconstruction –exposing systems to extraction attacks– or directly estimate
fairness metrics, offering limited insight into which regions of the data distribution drive bias. More fundamentally, property-specific auditing –aimed at extracting only targeted
fairness information without reconstructing the model– remains poorly understood. In this work, we introduce the bias probe framework, which enables targeted and adaptive querying to reveal bias structure while preserving model confidentiality. Building on this framework, we propose ALeBi, an active auditor that learns such probes to efficiently estimate multi-group
fairness metrics. We establish novel sample complexity guarantees governed by a property-specific complexity measure, resolving a previously posed open question, and extend our analysis to
adversarial settings where the model owner may strategically obscure bias. Our results uncover a fundamental trade-off between model confidentiality and reliable auditing, and show that property-specific probing enables both accurate estimation and interpretable identification of high and low-bias regions. Extensive experiments support our theoretical findings and demonstrate the practical effectiveness of our approach.
Read the full abstract ▾
Computers and Society · Cross-listed from cs.HC
Engagement-Led Segmentation of Gamified Participation Data in a Large-Scale Remote Internship: A Mixed-Methods Study
Sakshi Sharma, Pavani Ayinampudi, Aditya B.M.V. et al. (+5 more)
Gamified points are widely used to represent learner participation, but cumulative point totals provide limited information about how participation changes over time. This limitation matters in continuously enrolling programmes, where learners have different opportunities to accumulate points. This study examines how gamified participation data can be interpreted as indicators of behavioural engagement in a large-scale, continuously enrolling remote internship. Using an anonymised operational
dataset, 3,607 distinct started learners were first classified by participation status, separating dormant learners from those with observable participation. The 1,871 active learners were then grouped using K-means clustering on normalised longitudinal and opportunity-aware participation features. Four participation patterns emerged: Thriving Core, Tapering, Fast Starters, and Occasional Participants. Their trajectories differed in both level and direction. A perception survey of 597 respondents provided complementary learner-reported evidence, integrated with the behavioural strand through a joint display by segment. Perceptions differed across segments, while individual-level correlations between perceptions and behaviour were small, and learners with low recorded participation could report positive perceptions of the points system alongside external barriers. Segments assigned from the first four weeks were then checked against eleven weeks of later platform records: 94% of the Thriving Core attended at least ten further sessions and 22% completed the internship, against under 7% and under 1% in the low-engagement segments. The findings suggest that gamified participation data are more informative when interpreted as longitudinal, opportunity-aware behavioural indicators rather than cumulative scores alone.
Read the full abstract ▾
All authors Sakshi Sharma, Pavani Ayinampudi, Aditya B.M.V., Jinal Gupta, Prakash Hegade, Rohit Sharma, Meenakshi V, S.R.S. Iyengar
Computers and Society · Cross-listed from cs.AI
Trust Is Not a Score: Runtime Assurance Contracts for High-Risk AI Agents
Serhii Zabolotnii
Benchmarks, audits, and
agent protocols describe performance, permissions, and repair, but not how observed evidence should change an
agent's authority during a consequential task. We call this the assurance-transition gap. We propose a Runtime Assurance Contract (RAC), a policy-level formal schema binding autonomy boundaries, component eligibility, evidence state, transition policy, human-review capacity, and non-compensatory gates. Under RAC, soft metrics may inform routing, whereas a failed or unknown mandatory gate forces retry, switch, escalation, deferral, or stop; aggregate performance cannot authorize action. We define the contract, an evidence record, a permission rule, and five invariants, and illustrate them in clinical, industrial, and judicial failure probes. We then report a deterministic failure-injection study in agentic coding: 280 constructed cases evaluated by a gate conjunction, a score-only rule, and a restricted protocol baseline. At the published example weights and threshold, the score rule admits 80 of 100 block-required injections and all 40 review-required injections. Tuned in hindsight, it matches the conjunction on this corpus. For positive weights, a positive threshold, binary risk signals, zero-signal controls, and an injected case firing each signal alone, we show that exact agreement holds if and only if the threshold does not exceed the smallest weight. A separate set of 18 hand-authored traces checks version-pinned evidence and review transitions against simpler policy variants. In a further prospective synthetic holdout of 24 episodes, two blinded LLM judges assign identical labels to all 72 action attempts; RAC and a separately implemented full stateful baseline both match these labels. These studies test mechanisms on synthetic cases; they establish neither deployed safety nor cross-domain effectiveness.
Read the full abstract ▾
Computers and Society · Cross-listed from cs.AI
Values as Style: Disentangling Values from Semantics with One-Way Mixing for Low-Damage LLM Steering
Jiale Dai, Hongcan Deng, Liuxian Ma et al. (+2 more)
Value steering should change an LLM's normative priorities while preserving the scenario, facts, and task constraints underlying its answer. Conventional activation edits often change both. We introduce an editable semantic-value interface on frozen residual states, with a one-way semantic-to-value pathway that grounds value recognition in context. Stop-gradient blocks feedback through this pathway; swap consistency, topic de-confounding, and decorrelation encourage selective codes. At inference, editing the value code produces a residual delta while holding the semantic code fixed. On two instruction-tuned backbones, this interface improves semantic preservation and reduces benign refusals at comparable value
alignment. A matched mixing-by-gating ablation separates representation learning from selective edit activation, and dimension-matched probes establish improved code selectivity. Against validation-selected prompting on LLaMA-3.1-8B, the method achieves comparable
alignment (0.750 vs. 0.748), higher BERTScore (0.938 vs. 0.923), and fewer contradictions (5.1% vs. 7.6%). Human ratings and cross-taxonomy controls provide complementary evidence for low-damage value steering.
Read the full abstract ▾
All authors Jiale Dai, Hongcan Deng, Liuxian Ma, Xiaoke Niu, Guojie Song
Computers and Society
A Reusable Semantic Web Framework for Evidence-Grounded Fundamental Rights Impact Assessments under the EU AI Act
Faith Olopade, Delaram Golpayegani and David Lewis
The EU AI Act (Art. 27) requires deployers of high-risk AI systems to conduct Fundamental Rights Impact Assessments (FRIAs) before deployment, yet the evidence needed for credible assessments is fragmented across incompatible incident repositories, risk vocabularies, and legal texts. We present a reusable Semantic Web-based framework that consolidates this evidence for two high-risk public sector categories: employment and worker management (Annex III(4)) and access to essential public services (Annex III(5)(a)). A curated 150-record corpus is annotated along four axes using keyword, LLM, and hybrid methods and serialised as a SPARQL-queryable knowledge graph of 1,351 RDF triples. Five FRIA demonstration scenarios surface 103 records (68.7% coverage). Evaluation against a 69-record gold standard reveals that LLM-assisted classification of the employment domain achieves only $\kappa = 0.045$, a cautionary result for automated
fairness-related evidence
retrieval in this domain. All artefacts are released openly to support adoption by regulators, national authorities, and SMEs.
Read the full abstract ▾
Computers and Society
From Knowledge to Legitimacy: A Philosophical Problem Discovery of AI Implementation Readiness in Public Health Disease Surveillance
Tithi Mitra, Souvik Pramanik, Most. Aysha Siddika Sumona et al. (+3 more)
Despite being an emerging credible means of using artificial intelligence (AI) for improving disease surveillance via early detection of outbreaks, epidemics prediction and evidence-based decision-making, there continue to be challenges in the use of AI tools in many low- and middle-income countries (LMICs) due to various factors including fragmented health information system, digital inequality, governance problems and institutional incapability. Most of the research conducted so far has concentrated on the efficacy and accuracy of AI in terms of predicting outbreaks, with little focus on other conditions necessary for its deployment. This study adopts a qualitative problem-discovery research design, integrating thematic analysis with philosophical analysis to examine the structural and normative barriers surrounding AI implementation in disease surveillance. The analysis identifies four interrelated dimensions of implementation readiness: epistemic adequacy, distributive justice, ethics of governance, and institutional legitimacy. These dimensions provide a framework for understanding how limitations in knowledge integration, unequal digital infrastructure,
privacy and accountability concerns, and deficits in institutional and public trust can constrain the practical adoption of AI-enabled surveillance. Rather than proposing another predictive model, this study develops a theoretical framework that conceptualizes AI implementation in disease surveillance as simultaneously a socio-technical and normative process. The framework provides a foundation for subsequent empirical investigation and offers a structured perspective for designing more context-sensitive, ethically grounded, and institutionally sustainable AI-enabled disease surveillance systems in resource-constrained healthcare settings.
Read the full abstract ▾
All authors Tithi Mitra, Souvik Pramanik, Most. Aysha Siddika Sumona, Ahmed Faizul Haque Dhrubo, Farhana Sharmin, Mohammad Abdul Qayum
Computers and Society
Executive Judgement in AI-Mediated Decision-Making Environments: A Process Theory of Formation, Qualification Attrition and Authorisation
Richard Hill
Generative artificial intelligence can contribute to the representations, alternatives and evaluations through which executive judgements are formed. Existing research already explains important aspects of hybrid cognition, reliance, managerial agency and accountability. This article develops a narrower process challenge: locally competent human and AI contributions can still culminate in an inadequately warranted organisational commitment when established operational assumptions, uncertainties or dependencies are lost or transformed before authorisation. It proposes qualification attrition as the process through which such epistemically consequential content is weakened across successive transformations, and a formation–authorisation gap as the resulting discrepancy between the grounds required for warranted commitment and those that remain accessible, intelligible and challengeable to the authorising role. Executive judgement governance is defined through four dimensions: boundary setting, interpretive challenge, reliance calibration, and authorisation with answerability. Four propositions connect transformation, qualification attrition and governance arrangements to epistemic risk, accountability risk and judgement quality. The contribution is an integrative process theory of how distributed judgement formation can fail as a composition even where individual contributions, local reliance decisions and formal accountability arrangements appear competent.
Read the full abstract ▾
Computers and Society
The Concentration of Artificial Intelligence in Big Tech and Its Implications for Human Rights in the European Union
Marcin Marciniak
The development of advanced artificial intelligence is increasingly concentrated in a small group of vertically integrated technology companies. These firms control combinations of computing infrastructure, cloud services, data, foundation models, software ecosystems, and channels of distribution. This article argues that such concentration transforms
market power into a form of private governance over the conditions in which fundamental rights are exercised. Drawing on an interdisciplinary narrative review, it examines how concentration affects equality of access,
privacy, freedom of science, working conditions, access to essential resources, environmental protection, and the availability of effective remedies. Evidence from data-centre expansion in Ireland, the United Kingdom, the United States, and Chile, together with studies of automation and collective bargaining, shows that benefits may be widely distributed while decision-making power and economic returns remain concentrated. Concentration does not itself establish a rights violation, but it increases dependency, opacity, switching costs, and the risk that social and environmental costs are externalised. The article concludes that competition policy must be combined with rights-based impact assessment, interoperability, public research capacity, worker participation, and enforceable access to review and remedy.
Read the full abstract ▾
Computers and Society
Freeze, Validate, Report: Auditing Urban Station Plans with Common Evidence
Julian Teusch and Oliver Keszöcze
Transport authorities comparing urban station plans need to distinguish plan differences from differences in evaluation inputs. Reconstructing spatial units, included trips, or candidate blocks from each plan's access outcomes can make scores incomparable. Freeze–Validate–Report fixes an evaluation contract: target trips, proxy groups, reporting
cells, candidate pools, and a lexicographic selection rule. A two-point example shows that plan-specific observation selection can reverse the target mean ordering. We compare seven generators spanning clustering, facility location,
fairness-oriented adaptations, and a grid control. All plans are scored on common validation evidence using endpoint distance: the sum of origin and destination distances to their nearest stations. A five-metric rule selects one plan for a single test report. In Porto, it selects IFkCO, an adaptation of individually fair $k$-center with outliers; test overall and worst-group 90th percentiles are 805 m and 872 m. In Chicago, it selects Grid; values in a later test window on the same day are 1404 m and 1656 m. Post hoc checks show input sensitivity: expanding candidates from 300 to 600 changes selection in both cities, while holding the hour fixed across three Chicago dates selects Priority. A proportional mean fairlet diagnostic shows that plan-specific proposal pools can change diagnostic ranks; admissibility and solver bounds apply only to the sampled pool. The package supplies code, hashes, decision logs, and proposal pools. We establish auditability within a declared contract, not stable performance after deployment; proxy definitions and metric priorities remain the authority's choices.
Read the full abstract ▾
Computers and Society · Cross-listed from cs.HC
When Attention Guardrails Become Barriers to Learning: Towards the Tipping Point
Meenakshi V., Pavani Ayinampudi, Aditya B. M. V. et al. (+5 more)
Online learning offers flexibility but lacks the structure of a classroom, where a teacher's presence guides
attention. The platform we study restores that structure by monitoring the learner through the webcam during ordinary coursework, interrupting or restarting a
video when the learner appears distracted. What such monitoring does to a learner across a whole course, rather than in a single examination, is largely unexamined. We report a convergent mixed-methods study of one monitored course pipeline in a summer internship program. We read two free-text surveys alongside three platform channels. The dropout exit survey yielded 15 analyzable responses; the persisting-learner reflection survey, 36. The channels are camera-verification telemetry (14,529 flags from 448 students), an in-
video emotion widget (615 submissions from 273 students), and a mandatory end-of-course survey (up to 634 respondents per item). Neither survey named monitoring, so every mention analyzed here was raised by the respondent. Focus-monitoring was raised by 18 of the 51 free-text respondents: 7 of 15 dropouts and 11 of 36 persisting learners. Among the dropouts who raised it, focus-monitoring was the stated primary cause of departure in 4 of 7 cases. None of the 18 questioned being observed in principle. What learners contest is the misreading of ordinary actions, drinking water or moving the head, and the severity of what follows a flag: a
video already watched returns to the start of its segment. These findings identify two targets for redesign: the severity of the response to a flag, and the environment check, which can flag a learner before any content has been seen. We argue that the proportionality of that response marks the point at which an
attention guardrail becomes a barrier to learning, the tipping point this study approaches.
Read the full abstract ▾
All authors Meenakshi V., Pavani Ayinampudi, Aditya B. M. V., Jinal Gupta, Prakash Hegade, Rohit Sharma, Sakshi Sharma, S. R. S. Iyengar
Computers and Society · Cross-listed from cs.HC
Insights on Student Learning from Live Classroom Polls: More Than Right or Wrong
Rohit Sharma, Pavani Ayinampudi, Aditya B.M.V. et al. (+5 more)
A live classroom poll is usually read through its answer key, as a count of participation and correctness. Yet the same record holds more before any key is fixed, namely who answers early and how the class divides across the options. Whether these key-free signals are stable, and what they reveal, has not been examined at the scale of a real course. We study them in a live deployment of 340,668 submissions across 603 polls in 47 sessions from roughly 2,400 learners, reading each poll for a learner's response order and for how much the class divides. A single further step brings in the author-designated key, on items with a settled answer, to interpret the divided ones. A learner's response order is a stable individual signature. It reproduces at a corrected split-half reliability of 0.92, holds at 0.87 within single sessions, and is unrelated to whether the learner is correct. Read without a key, about one poll in five does not reach a clear majority, and division marks the questions that split the class. On the settled-answer items the majority is itself wrong on 49 of 358. The key-free measure flags most of these, though division alone does not separate a collectively wrong answer from a merely hard one. The two readings are largely independent, meeting at one point where early responders anticipate the eventual majority only on questions the class has nearly settled. Together they map the learners and questions of a session without a key.
Read the full abstract ▾
All authors Rohit Sharma, Pavani Ayinampudi, Aditya B.M.V., Jinal Gupta, Prakash Hegade, Sakshi Sharma, Meenakshi V, SRS Iyengar
Computers and Society · Cross-listed from cs.HC
An Audit of Measurement Quality and Answer Bias in a Large Classroom-Poll Corpus
Rohit Sharma, Pavani Ayinampudi, Aditya B.M.V. et al. (+5 more)
Real-time classroom polls are widely used and increasingly generated with automated assistance, yet the questions themselves are rarely evaluated as measurements. We audit a large corpus of authentic classroom polls, 604 items across 47 sessions answered 340,668 times by 2,807 learners, as a measurement instrument. For the 539 items whose correct answer could be established and verified from the lecture transcript, we place every item and every student on a common scale using item response theory and analyse the answer structure of the True/False items. Two findings emerge. First, the polls form a coherent but easy scale of moderate precision (marginal reliability about 0.60), on which roughly a quarter of items barely separate stronger from weaker students. Second, students show a robust tendency to answer True, present at the individual level (77% of students lean True), which meets a milder tendency for items to be keyed False; as a result answer direction predicts difficulty, False-keyed items being about thirteen points harder, and the effect survives controls for item content and for selective answering. Both findings rest on signals a polling system already records, so the same checks can be run as items are generated, before they reach students.
Read the full abstract ▾
All authors Rohit Sharma, Pavani Ayinampudi, Aditya B.M.V., Jinal Gupta, Prakash Hegade, Sakshi Sharma, Meenakshi V, SRS Iyengar
Computers and Society
Breaking News Out of the Filter Bubble: Generative AI Search Diversifies Collective Attention and Raises Shared Information Consumption
Heeseung Andrew Lee, Dokyun Lee, Gwanhoo Lee et al. (+1 more)
Generative AI search and AI overviews are transforming access to information and news, renewing concerns that readers will encounter a narrower range of topics and have less in common. We examine these concerns via a randomized field experiment with 37,561 readers at The Washington Post. Both groups searched the same archive, but treatment readers also received AI answers with article citations above conventional results. Measuring consumption across displayed answers and opened articles, we find that AI search expands the reach of widely read topics and increases overlap in readers' topic consumption. At the same time, consumption becomes less concentrated and shifts toward less-popular topics, both within readers and across the audience. AI answers account for most of the increase in shared information, delivering it without requiring article clicks and broadening exposure beyond the articles readers open. Cited articles also contribute to the shift toward less-popular topics. Readers shift from conventional-result clicks and browsing toward cited articles and follow-up searches. More frequent searching offsets lower article consumption per search, producing a small increase in article consumption per reader. Total information consumption per minute also rises. Generative AI search can thus diversify collective
attention while strengthening the information readers have in common.
Read the full abstract ▾
All authors Heeseung Andrew Lee, Dokyun Lee, Gwanhoo Lee, Dongwon Lee
Computers and Society · Cross-listed from cs.HC
Characterizing Questioning Patterns and Student Engagement Through Contextual Analysis of Real-Time Classroom Interactions
Rohit Sharma, Pavani Ayinampudi, Aditya B.M.V. et al. (+5 more)
Real-time classroom polling is now routine, yet the data it produces is usually read narrowly, as a correctness score or a headcount. Such readings say little about what a poll is doing within a lecture or how it shapes engagement. This is particularly relevant for short-response formats such as True/False, where the same question format can be used to test recall, check comprehension, or direct students'
attention to a deliberately misleading statement. This study asks whether a poll's answer and instructional function can be determined by reading it against its lecture transcript, what cognitive levels of Bloom's taxonomy and instructional-function clusters the corpus contains, and how student engagement relates to answering correctly. We analyse a naturalistic corpus of 47 live sessions over 39 days, comprising 604 poll questions and 340,668 responses from 2,807 learners, most items True/False, read against time-aligned lecture transcripts and attendance. Reading each poll in context proves essential: the answer to 89% of polls is locatable in the lecture, and a recurring
attention-checking device is visible only through context. Questioning is overwhelmingly lower-order and falls into seven instructional functions, and a poll's response follows its function rather than its wording. Engagement is broad but concentrated, and the class majority answers correctly 88.5% of the time, though a small set of high-consensus yet incorrect answers cannot be detected by agreement alone. An independent survey of 579 students agrees on what the polls are and on their participation, but reveals a gap between perception and reality: students cannot judge their own correctness, and the polls they find hardest are not those they answer worst.
Read the full abstract ▾
All authors Rohit Sharma, Pavani Ayinampudi, Aditya B.M.V., Jinal Gupta, Prakash Hegade, Sakshi Sharma, Meenakshi V, SRS Iyengar