AI Frontier Institute

News & Research

The latest AI research and news with real-world stakes — each item sourced, dated, summarized in plain English, and tagged by impact area. Every item is checked against its source before it appears.

• Research

  Hybrid SEM-ANN analysis of augmented reality adoption for hospitality training: evidence from an emerging economy

  Sandeep Kumar Dey, Sinh Duc Hoang, Zuzana Vaculčíková et al.

  Technological and Economic Development of Economy · 2026-09-01

  This study examines what drives or hinders augmented reality (AR) adoption as a training tool among housekeeping staff in star-rated Indian hotels, using a hybrid SEM-ANN model with 300 hoteliers. It finds that external support, organisational flexibility, and perceived competitive advantage facilitate adoption, while cost concerns and technological anxiety act as barriers, with technological self-efficacy moderating these effects. The findings suggest AR can lower training costs and improve operational efficiency in low-margin hospitality settings, and the authors recommend 'train-the-trainer' programmes to build managerial confidence and support digital transformation.

  • Workforce
  • Enterprise

• Research

  Artificial Intelligence and Automated Decision-Making under the Nigeria Data Protection Act 2023: Evaluating the Adequacy of Nigeria’s Data Protection Framework

  G. Esq

  International Journal of Humanities Social Sciences and Education · 2026-09-01

  This paper evaluates whether Nigeria's Data Protection Act (NDPA) 2023 and its supplementary General Application and Implementation Directive (GAID) 2025 provide data subjects with protection against AI-driven automated decision-making that is comparable in specificity and enforceability to the EU's GDPR. Using doctrinal and comparative legal analysis, the authors find that while the NDPA recognises key safeguards—transparency, fairness, and the right to human intervention—these protections remain substantially underspecified. The paper concludes that the most urgent reform is not creating new rights but issuing detailed regulatory guidance to operationalise existing ones, along with targeted legislative reforms on algorithmic impact assessments and the independence of Nigeria's Data Protection Commission. This matters because AI-driven automated decisions already affect employment, credit, insurance, and healthcare outcomes across Nigeria's public and private sectors.

  • AI policy

• Research

  Evaluating the Adoption of Expert Systems, Inventory Management Practices and Digital Readiness in Nigerian Pharmaceutical Retail Organizations

  Abdurrahman Omoakhalen, Balogun Vincent Aizebeoje, Achekuogene Nihad Saliu et al.

  FUDMA Journal of Sciences · 2026-09-01

  This study surveys 293 pharmacy professionals in Nigerian pharmaceutical retail organizations to assess the challenges of manual inventory management and the readiness to adopt Expert Systems (ES). Findings show persistent operational problems including stock-outs, drug expiries, and supplier delays, yet respondents also report strong digital readiness, organizational willingness, and high behavioral intention toward ES adoption. The authors develop and validate an integrated ES framework combining automated stock alerts, machine learning-based demand forecasting, and rule-based decision support, grounded in TAM, TOE, and UTAUT models. The research highlights a practical pathway for improving medicine availability and reducing inventory waste in developing-economy pharmaceutical supply chains.

  • Enterprise
  • Quality assurance

• Research

  Governing AI and Digital Platforms: A Systematic Literature Review of Multi-Sectoral Policy Decision-Making

  Susanti Sampe Tandung, Obel Obel, Ihda 'Ainaya Zulaikha et al.

  F1000Research · 2026-09-01

  This systematic literature review synthesizes 45 peer-reviewed empirical and policy-oriented articles (2021–2026) to examine how AI and digital platforms are reshaping policy decision-making across public administration and private sectors. The study identifies four governance mechanisms—institutional restructuring, enhanced citizen participation, economic and environmental innovation, and ethical risk navigation—while revealing a persistent gap between technological adoption and the adaptive capacity of existing legal and regulatory institutions. The authors conclude that effective AI governance requires a holistic, context-sensitive approach balancing efficiency with justice and innovation with accountability, and offer an integrative framework for policymakers navigating digital transformation.

  • AI policy

• Research

  Ethical Considerations on Artificial Intelligence, Health and Health Care. All That Glisters Is Not Gold

  Piero Portincasa, Mohamad Khalil, Pierfrancesco Novielli et al.

  European Journal of Clinical Investigation · 2026-09-01

  This review paper examines how AI is transforming healthcare through deep learning, generative models, and network medicine, while highlighting persistent ethical, regulatory, and social challenges that limit widespread clinical adoption. The authors find that only a small fraction of AI tools achieve routine clinical use due to limited generalizability, opaque algorithms, and workflow incompatibility. Key mitigation strategies discussed include explainable AI, counterfactual reasoning, and ethical frameworks like the European Commission's ALTAI, which address algorithmic bias, data inequity, and variable regulatory standards across regions. The paper concludes that aligning technical innovation with ethical design and rigorous validation is essential to ensure AI becomes both transformative and trustworthy in precision and public health.

  • AI policy
  • Quality assurance

• Research

  Artificial Intelligence Tool Adoption Among Employees of the Philippine Electronics and Communication Institute of Technology (PECIT): A Qualitative Exploration of Issues and Challenges

  Gerardo Jr. S. Carlos, Daryll A. Cabagay, Maria Cecilia Z. Matillano et al.

  Proceedings of the International Conference on Statistics, Theory and Applications (ICSTA ...) · 2026-09-01

  This qualitative study examines how teaching and non-teaching staff at a Philippine private higher education institution (PECIT) are adopting AI tools such as ChatGPT, Gemini, and Copilot. Using semi-structured interviews and thematic analysis, the research finds that employees use these tools for lesson preparation, research, communication, and administrative tasks, reporting gains in productivity and efficiency. Key challenges include inaccurate AI outputs, difficulties in prompt formulation, and the need to verify AI-generated content. Participants recommended continuous AI training, clear institutional policies, and responsible AI practices to support sustainable integration.

  • Workforce
  • AI policy

• Research

  Human-AI Integration in Industry 5.0: Mapping Relational Patterns among Antecedents, Mechanisms, and Outcomes

  Mirco Avallone, Gianluca Aquilone, Antonello Cammarano et al.

  Journal of Industrial Information Integration · 2026-09-01

  This paper develops an Antecedent-Process-Outcome framework for human-AI integration in Industry 5.0 industrial settings, consolidating 70 constructs and coding 162 AI-oriented industrial practices. Using multi-layer association and network analyses, it finds that augmentation-oriented human-AI collaboration aligns more strongly with positive performance and knowledge outcomes than automation-oriented arrangements, which show weaker links to human-centric results. The study identifies structurally central constructs that differ from those most emphasized in existing literature, offering evidence-based guidance for sustainable process innovation and adaptive capacity in industrial workplaces.

  • Workforce
  • Enterprise

• Research

  Artificial Intelligence in Academic Research: Adoption, Productivity, and Skill Perceptions

  Alessandro Muscio, Sotaro Shibayama

  Technology in Society · 2026-09-01

  This survey of Italian academics finds that 39% report using AI in their research, with adoption highest among younger scholars and those in applied or interdisciplinary fields. AI use is positively associated with self-reported research productivity and more optimistic expectations about skill development, though trust in AI is uneven and skepticism persists across segments of the academic population. The study provides empirical evidence on how AI is reshaping research workflows and researcher skill perceptions in higher education.

  • Workforce

• Research

  Algorithmic Publicity and the Right to a Fair Trial

  Ariss Laghai

  Bond Law Review · 2026-09-01

  This article analyzes how AI-driven algorithmic systems—including social media feeds, recommender engines, synthetic media, and generative AI—threaten the right to a fair trial in Australian criminal jury proceedings by amplifying and personalizing prejudicial publicity in ways that undermine traditional broadcast-era safeguards. Drawing on empirical research on juror psychology and internet use, it evaluates existing controls such as contempt, stays, jury directions, and judge-alone trials, finding them inadequate against persistent and searchable digital content. The article proposes doctrinal refinements, targeted platform duties, and court-supervised AI monitoring tools, drawing on developments in the UK, EU, US, China, and Estonia. The work is directly relevant to policy debates about how legal systems should regulate AI and platform behavior to preserve fair-trial guarantees.

  • AI policy

• Research

  Governing the Algorithmic Black Box in Talent Acquisition: Towards a Multi-Level Framework of Contestable Accountability

  Ramniyata Jairath

  Frontiers in Social Sciences Research · 2026-09-01

  This conceptual paper argues that existing governance tools for AI-driven hiring systems—such as transparency disclosures, explainability requirements, and algorithmic audits—rest on assumptions that the recruitment context systematically violates. The authors develop a multi-level 'contestable accountability' framework spanning epistemic, procedural, institutional, and contestatory levels, and introduce two diagnostic concepts—vertical displacement and temporal displacement—to explain why governance efforts persistently fail to protect job candidates. The paper reframes the policy problem from making algorithms explainable to making hiring decisions genuinely answerable, and situates its analysis against regulatory landscapes in the EU, US, and India. The work matters because it offers a governance-theoretic vocabulary for addressing AI opacity in talent acquisition and advances nine testable propositions for future research and regulatory design.

  • Workforce
  • AI policy

• Research

  Use of artificial intelligence in education and training of radiology

  María Belén Morales-Cevallos, Canva Byron Ma Lam, María José López Pino et al.

  Frontiers in Radiology · 2026-09-01

  This scoping review of 29 studies (2020–2025) examines how artificial intelligence is being integrated into radiology education and training. Skill development was the most investigated outcome (55.2% of studies), and approximately 86% of studies reported positive or improved educational outcomes, with AI-based interventions enhancing learner confidence, AI literacy, diagnostic reasoning, and readiness for clinical implementation. Generative AI tools showed promise for tutoring and assessment but raised concerns around reliability, hallucinations, and bias. The authors conclude that sustainable AI integration requires standardized curricula, faculty development, and ethical oversight to prepare radiology professionals for AI-integrated healthcare.

  • Workforce
  • Certifications

• Research

  Stakeholder perceptions of artificial intelligence for climate adaptation in Somalia: Opportunities, readiness barriers, and community resilience

  Aweis Ahmed Hussein Jarras, Abdimalik Aden Ibrahim, Abdullahi Ilyas Osman

  Progress in Disaster Science · 2026-09-01

  This study surveys 353 stakeholders across government, NGOs, academia, and the private sector in Somalia to assess perceptions of AI for climate adaptation. Respondents broadly valued AI for early warning systems, disaster response, agricultural decision-making, and environmental monitoring, yet the strongest consensus was that Somali communities lack the technical capacity to use AI tools, followed by financial and infrastructure barriers. The authors frame this 'high demand–low preparedness' paradox through a proposed 'Readiness-Adjusted Technology Adoption' framework, arguing that perceived usefulness alone cannot drive adoption without complementary investments in capacity, infrastructure, and institutions. The paper offers practical recommendations including mobile-first, voice-based AI tools, community digital literacy programs, and governance reforms relevant to fragile state policymakers and donors.

  • AI policy
  • Workforce

• Research

  Civil Liability for Damage Caused Due to the Use of Artificial Intelligence: an Unpopular Study of a Popular Topic

  Nataliia Filatova-Bilous

  Civìlìstična platforma. · 2026-09-01

  This article analyzes civil liability for AI-caused damages, examining regulatory frameworks in the EU and US and their implications for Ukrainian law. The authors argue that Ukraine does not yet need a special AI liability regime, but should instead incorporate EU Directive 2024/2853 on defective products, adapt traditional tort concepts to address the difficulty of establishing fault and causation in AI contexts, and update procedural rules for AI-related evidence. The paper highlights core technical challenges—including AI unpredictability, opacity, and self-learning—that complicate existing legal frameworks.

  • AI policy

• Research

  Digital adoption, AI integration, and labor productivity: empirical insights from the European Union

  Mercy Minoo Kavele

  Labour & Industry a journal of the social and economic relations of work · 2026-09-01

  This study analyzes the relationship between enterprise AI adoption, workforce digital skills, and labor productivity across all 27 EU member states using Eurostat and OECD data, OLS regression, and Pearson correlation. While AI adoption and digital skills show positive bivariate associations with labor productivity, GDP per capita is the only statistically significant predictor once economic development is controlled for. The findings suggest that digital technologies alone do not guarantee productivity gains and that complementary economic capabilities are necessary to translate AI and digital transformation into measurable labor productivity improvements. The study contributes empirical evidence relevant to EU-level debates on workforce upskilling and enterprise digital investment strategies.

  • Workforce
  • Enterprise

• Research

  Auditing the Algorithmic Leviathan: A Tiered Accountability and Reporting Standards Framework for Democratic Public Administration

  Haris Alibašić

  Standards · 2026-09-01

  This paper develops a tiered accountability and reporting standards framework—organized around five auditable primitives (provenance tracking, decision logging, role attribution, contestability, and post-deployment audit)—for algorithmic systems used in public administration. Drawing on case studies including the DOGE-Treasury access episode, Australia's Robodebt scheme, and governance arrangements in six countries, the authors argue that current standardization focuses too narrowly on AI system certification and neglects institutional answerability. The framework introduces a Public Sector Algorithmic Accountability Statement (PAAS) with ten disclosure fields crosswalked to GRI standards, escalating obligations across minimum, heightened, and systemic/constitutional tiers based on the severity of public authority consequences. The proposal aims to bridge the gap between technical AI assurance and democratic governance accountability.

  • AI policy
  • Certifications

• Research

  Should Businesses Trust AI Advice? A Methodology to Audit the Ethical Integrity of Chatbots

  Manuel Chaves-Maza

  Computers in Human Behavior Reports · 2026-09-01

  This paper introduces the Adaptive Ethical Evaluation Protocol (AEEP), a validated audit methodology for assessing whether AI chatbots maintain consistent ethical stances when subjected to business pressure in small and medium-sized enterprise (SME) advisory contexts. The protocol stages structured five-node adaptive dialogues across ten real-world SME dilemmas and was applied to five frontier LLMs, achieving 93.8% algorithm–expert agreement (Cohen's κ = 0.728). Results revealed meaningful behavioral differences between models—Claude was most consistent under pressure while Grok wavered most—providing enterprise advisors, regulators, and SME managers with a reusable tool to identify where AI advice can be trusted and where human oversight remains necessary.

  • Enterprise
  • Quality assurance
  • AI policy

• Research

  Development and validation of a human-supervised AI-augmented living oncology evidence platform: a breast cancer pilot study

  H. Rugo, A. Forsythe, D. Flora et al.

  ESMO Real World Data and Digital Oncology · 2026-09-01

  This paper describes and validates a living oncology evidence platform (Living-OEP) for breast cancer that uses an agentic AI system—combining GPT-4.1, o3, and Claude Sonnet-4—to perform daily, human-supervised systematic literature review compliant with Cochrane standards. Trained on over 29,000 annotated clinical trial abstracts, the system achieved review accuracy of 95.1%–97.2% and extraction accuracy up to 99.4% against human annotations, outperforming general AI chatbots on comprehensiveness and accuracy across eight breast cancer treatment scenarios. By integrating structured evidence with guideline-based treatment pathways and FDA labels in real time, the platform aims to help oncologists keep pace with rapidly evolving clinical data. The authors note that future studies are needed to assess impact on physician workflows and clinical decision-making.

  • Enterprise
  • Quality assurance

• Research

  Current Landscape of Curriculum Development and Implementation in Medical Artificial Intelligence: A Scoping Review

  Yue Wang, He Wang, Ting Wang et al.

  Journal of Multidisciplinary Healthcare · 2026-09-01

  This scoping review examines 36 implemented medical AI educational programs published between 2021 and April 2026, finding that such programs have grown rapidly but remain largely in pilot stages with small class sizes. Most curricula were developed based on expert experience rather than standardized frameworks, focused on understanding-level learning objectives, and relied on student feedback for evaluation rather than objective assessment tools. The review identifies persistent gaps including lack of standardized curriculum frameworks, limited affective learning objectives, weak integration with existing medical curricula, and insufficient involvement of instructional designers. The authors conclude that medical AI education is still in its early stages and call for accelerated curriculum development, stronger practical and affective competency training, and more rigorous evaluation systems.

  • Workforce
  • Certifications

• Research

  Literature Review of Existing Environmental Policies Related to AI Data Centers: Current Regulatory Tools, Applicability, and Shortcomings

  Andy Zhang

  Future Trends in AI Research · 2026-09-01

  This literature review examines whether existing environmental regulations in the US, EU, and subnational jurisdictions can adequately govern the environmental impacts of AI data centers, which concentrate electricity demand, cooling loads, water use, and hardware turnover at unprecedented scale. The review finds that existing law does reach many aspects of AI data center operations—through electricity regulation, water permitting, air quality rules, and waste law—but only partially and unevenly, as most policies regulate inputs or side effects rather than AI operators directly. Key shortcomings include fragmented governance across agencies, incomplete reporting on facility-level water use and embodied emissions, and an inability to address cumulative grid and aquifer impacts. The core policy problem identified is a mismatch between legacy environmental governance frameworks and the scale, speed, opacity, and local concentration of AI infrastructure growth.

  • AI policy

• Research

  Adaptive, ethical and responsible AI governance for smart cities and nations

  Z. R. M. Abdullah Kaiser

  Discover Cities · 2026-09-01

  This paper develops a six-step adaptive, ethical, and responsible AI governance framework for smart cities and nations, integrating legal rules, ethical oversight, organizational capacity, and human–AI decision-making arrangements. It identifies cross-sector governance risks including algorithmic bias, weak oversight, regulatory gaps, vendor dependence, and legitimacy deficits, and distinguishes between 'governance of AI' (regulation and oversight of AI systems) and 'governance by AI' (AI inputs into public decisions). The framework is applied illustratively to New York City and Singapore to demonstrate how its six governance dimensions function across decentralized and centralized contexts. The paper argues that AI governance must remain adaptive rather than fixed, embedding risk management, responsiveness, and ethics-by-design across the full AI lifecycle.

  • AI policy

• Research

  Fallibility, persuadability, and correctability of large language models under sustained conversational misinformation pressure

  Jordan Rodriguez, Zachary Hansen, Luis De Anda et al.

  Scientific Reports · 2026-09-01

  This study systematically evaluated seven major large language models (ChatGPT GPT-3.5/4o/4o-mini, Claude 3.5 Sonnet, Gemini 1.5 Pro, Llama-3-70B, and DeepSeek) on three dimensions of susceptibility to conversational misinformation: fallibility, persuadability, and correctability. Across 50-repetition sequences of 100 purposefully false statements, misinformation affirmation rates varied more than 150-fold across models, and a novel 'conversational reverberation' phenomenon was identified where models oscillated between accepting and rejecting the same false statement. Susceptibility was significantly influenced by informational obscurity under repetitive conditions, implicating training data frequency as a key factor, while correctability was highly heterogeneous—four models achieved 100% self-correction but the most accurate model failed to correct any of its rare errors. These findings reveal failure modes invisible to standard evaluation benchmarks, with direct implications for model selection in truth-critical deployment contexts.

  • Quality assurance
  • Enterprise

• Research

  Artificial Intelligence in Sustainability Assurance: Accounting Challenges, Audit Risks and a Conceptual Framework for ESG Verification

  Radosveta Krasteva-Hristova, Vanya Georgieva

  Accounting and Auditing · 2026-09-01

  This conceptual paper develops the Responsible AI-Assisted Sustainability Assurance Framework to address how AI should be integrated into ESG verification and sustainability assurance workflows. It identifies five domains where AI adds analytical value—evidence extraction, criteria mapping, anomaly and greenwashing screening, external-data triangulation, and documentation support—while cataloguing risks such as data fidelity, explainability, bias, auditor overreliance, and preparer gaming. The framework sets graded reliance ceilings and prohibits autonomous AI decisions on materiality, evidence sufficiency, and conclusions, offering a testable model grounded in ISSA 5000 and illustrated in a European regulatory context. The work matters because it provides structured governance guidance for accounting and audit professionals navigating the shift from voluntary to regulated, externally assured sustainability reporting.

  • Quality assurance
  • Certifications

• Research

  Two Structures of AI Power: National Capacity, Relational Boundary Control, and Systemic Influence in the Emerging AI Order

  Shaoyuan Wu

  arXiv · 2026-09-01

  This working paper proposes a two-structure analytical framework that distinguishes between 'AI National Power' (a country's capacity to generate, mobilize, and sustain AI-relevant resources) and 'AI Boundary Power' (an actor's ability to control the conditions under which another actor can access, transfer, or deploy AI resources). Using export controls on advanced computing and EU AI regulation as illustrative mechanisms, the paper argues these two forms of power are distinct but potentially complementary. The framework is relevant for understanding how policy instruments like export controls and regulatory regimes shape the geopolitics of AI development.

  • AI policy

• Research

  Acceptance Without Choice: Mandated Use and the Limits of Acceptance Theory in AI-Enabled Recruitment

  Ramniyata Jairath

  Frontiers in Social Sciences Research · 2026-09-01

  This conceptual paper critiques how technology acceptance theory (TAT) is applied to AI-enabled recruitment, arguing the approach is fundamentally misspecified when used on populations—both recruiters and candidates—who cannot opt out of the technology. The authors trace the problem to an unresolved assumption in TAT's own lineage: that when use is mandatory, measured 'intention' reflects compliance rather than genuine acceptance, undermining the dependent variable's meaning. The paper proposes relocating the scope condition from the setting to the behavior, limiting acceptance theory to whatever discretionary latitude remains, and distinguishes behavioral conformity from attitudinal endorsement with seven propositions. Notably, it highlights that candidates subjected to AI recruitment tools have no equivalent in the enterprise systems literature from which mandatory-use arguments are typically borrowed, representing a gap with direct implications for fairness and organizational legitimacy.

  • Workforce
  • AI policy

• Research

  Conversation Coach: A Voice-enabled AI System that Helps Practice Difficult Workplace Conversations

  Fanyou Wu, Suraj Maharjan, Ainur Yessenalina et al.

  arXiv · 2026-08-31

  Conversation Coach is a voice-enabled AI system designed to help managers rehearse difficult workplace conversations, such as performance reviews and coaching sessions, in a realistic spoken format. The paper compares two architectures—an end-to-end speech-to-speech model and a cascaded approach using automatic speech recognition, a large language model, and text-to-speech—finding the end-to-end approach offers 3× lower median latency and an estimated 8× lower cost, while the cascaded approach provides superior reasoning quality for coaching feedback. The cascaded system was deployed in production, where over 40,000 managers used it across six months, with usage patterns suggesting selective engagement for high-stakes conversations. The work demonstrates that scalable AI-driven voice coaching can reduce the cost of manager training while enabling personalized, policy-aware feedback at scale.

  • Workforce
  • Enterprise

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