± HEALTH Large language models (LLMs) are artificial intelligence (AI) tools specifically trained to process and gener…+ HEALTH Background Discharge letters are essential for continuity of care, yet patients often misunderstand their con…± HEALTH Hematoxylin and Eosin (H&E) glass slide preparation is fundamental to histopathology. Pathologists' expertise…+ CLIMATE Traditional soil heavy metal risk assessments suffer from fragmented indices and poor logical integration. Co…± HEALTH Background Neonatal pneumothorax can progress rapidly, yet detection on supine chest radiographs remains chal…+ HEALTH T cell exhaustion has widespread implications for the progression and treatment of chronic diseases including…+ HEALTH Background Standard pulmonary arterial hypertension (PAH) risk models may not fully apply to PAH associated w…+ HEALTH Objectives To map and synthesize the scientific evidence regarding the use of Augmented Reality (AR) and Mixe…
TruaceTracing the truth around AIMonday, September 14, 2026
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The Index

What the evidence says.What the public feels.

Ranks distinct AI gain and problem claims from the published record. Scores reward impact, independent source strength, scale, confidence, and recency.

1,404 results
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AI gains · 779

67
GainEducation· Stable· Evidence: Moderate (1 source)

AI-enhanced pedagogical practices were associated with higher mathematical language proficiency, communication and reasoning among senior high school students in Ghana, with digital literacy and learning engagement acting as mediators.

Published May 26, 2026, this peer-reviewed study investigated AI-enhanced pedagogical practices and mathematical language proficiency among 360 senior high school students in Ghana. Using structural equation modeling, it found AEPP, digital literacy, and learning engagement were significant positive predictors of proficiency, with the latter two mediating the relationship.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0180

67
GainPolicy· Stable· Evidence: Moderate (1 source)

Among active social media users in Kazakhstan, perceiving algorithmic personalization was under certain conditions associated with a higher sense of control over information choices.

By May 2026, researchers reported a mixed-methods study of 1,795 active social media users in Kazakhstan examining how perceptions of algorithmic influence and AI manipulation relate to democratic indicators. They found perceived personalization was modestly negatively linked to trust and discussion quality but sometimes linked to greater control, while perceived AI manipulation was strongly negatively linked to all indicators, especially for Telegram and YouTube users and those aged 18-29.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0179

67
GainPolicy· Stable· Evidence: Moderate (1 source)

The HIPSTer ontological framework advanced multilingual hybrid-threat handling to TRL-4 validation using high-efficiency semantic vectors and formal reasoning.

Published May 9, 2026, this scoping review assessed OSINT, SOCMINT, and NLP tools for hybrid-threat detection against operational requirements drawn from Russian and Chinese military tradecraft and European operational experience. It found individual disciplines technically advanced but defensive systems siloed, identifying a persistent semantic gap in cross-domain and cross-language reasoning.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0178

67
GainLabor· Stable· Evidence: Moderate (1 source)

Generative AI can increase meaningfulness for creatives by democratizing access to creative tools and consolidating tasks.

This peer-reviewed paper examines how recent advances in Generative AI are transforming creative industries by affecting the meaningfulness of work. It applies a framework covering task integrity, skill cultivation, task significance, autonomy, and belongingness to specialist, embedded, and support creatives.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0174

67
GainLabor· Stable· Evidence: Moderate (1 source)

AI use in workforce analytics enables proactive and predictive decision-making about employees.

A peer-reviewed study published May 16, 2026 developed and validated a Triple-Intelligence Framework for workforce analytics that combines AI intelligence for pattern detection, human intelligence for interpretation and ethics, and organizational intelligence for governance, based on a 2017-2025 literature review.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0173

67
GainEducation· Stable· Evidence: Moderate (1 source)

Deployment of Magic Note AI-assisted recording in a Scottish Local Authority social work department provided administrative relief and efficiency for practitioners.

Researchers analyzed baseline evaluation data from the rollout of Magic Note, an AI-assisted recording tool, in a Scottish Local Authority social work department, involving surveys, focus groups and free-text responses from practitioners.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0172

67
GainClimate· Stable· Evidence: Moderate (1 source)

Hybrid framework using Binary iHow for feature selection and iHow for hyperparameter tuning of a Multi-Scale Attention Network reduced forecasting error for wind and solar generation and improved computational scalability for smart-grid management.

Researchers developed a hybrid deep learning-optimization framework for renewable forecasting that pairs a Multi-Scale Attention Network with cognitively inspired metaheuristics. The Binary iHow algorithm selects features and the continuous iHow algorithm tunes hyperparameters, targeting high-dimensional inputs and sensitivity issues in wind and solar time series.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0168

67
GainScience· Stable· Evidence: Moderate (1 source)

Integrating Shuffled Frog Leaping Algorithm optimization with Boosted Trees and QSVM classifiers improved discrimination of argillic, phyllic, propylitic and iron oxide alteration zones from ASTER and Sentinel-2 data, raising AUC and achieving strong field validation.

Researchers developed a metaheuristic-optimized machine learning workflow that integrates Boosted Trees and Quadratic Support Vector Machines with the Shuffled Frog Leaping Algorithm to map hydrothermal alteration from multispectral satellite imagery. Tested in the Shahr-e-Babak district of Iran's Urumieh-Dokhtar Magmatic Belt, the method used ASTER and Sentinel-2 bands to discriminate argillic, phyllic, propylitic and iron oxide zones.

Impact 30%49
Evidence 25%95
Scale 20%35
Confidence 15%87
Recency 10%88

Updated Jul 13, 2026 · TRV-2026-0167

AI problems · 625

No problems on this page.

Recomputed live from the record · Sep 14, 2026, 2:46 PM