The central argument is that artificial intelligence and Universal Design for Learning should be treated as mutually constraining rather than as interchangeable technologies that can simply be combined. UDL is an evidence-informed approach to reducing barriers by designing flexible curricula around learner variability, with three principles: multiple means of engagement, representation, and action and expression. AI can operationalize those principles by supplying tools that adapt the pace or difficulty of learning, provide immediate explanations, caption media, translate content, generate alternative examples and support multiple forms of assessment. UDL, in turn, determines which applications are pedagogically appropriate by asking whether they expand students’ opportunities to engage, receive information and demonstrate understanding. An efficient system that narrows those options may perform well technically while contradicting the principles it is intended to support.

Five theoretical components explain how the proposed model works. Systems theory emphasizes that leadership, policy, infrastructure, technology and professional practice must align for sustainable adoption. Institutional theory explains why universities may imitate peer organizations, comply with external expectations or seek legitimacy by adopting similarly worded policies. Responsible AI supplies an ethical foundation based on fairness, transparency, accountability, privacy and human oversight. The model also adds a critical counter-current to these accounts. It warns that AI can encode historical bias, transfer educational judgment to proprietary systems, narrow what counts as measurable learning and extend commercial platform logics into university teaching. Critical oversight is therefore not a separate final stage. It operates across the full cycle, from leadership decisions and tool selection to faculty practice, student assessment and the metrics used to judge success.

The first pillar is institutional leadership and strategic governance. University leaders would establish a clear AI-UDL strategy, embed UDL and accessibility requirements in curriculum approval, allocate resources and define indicators for equitable implementation. The pillar also requires leaders to distinguish between tools supported by pedagogical evidence and systems adopted mainly because competing institutions possess them. That distinction is particularly important in a centralized system where national priorities, accreditation expectations and regional examples can create pressure for universities to resemble one another. The second pillar, AI-enabled UDL, maps applications onto all three UDL principles. For engagement, adaptive pathways, intelligent tutoring systems and conversational assistants can offer individualized support. For representation, text-to-speech, automatic captioning, translation and simplified explanations can provide alternative ways to access course material. For action and expression, AI-supported feedback, digital portfolios, adaptive quizzes and multimedia submissions can enable students to demonstrate learning in different formats while maintaining consistent academic standards.

None of these capabilities works automatically. The study specifically notes that machine-translation quality requires faculty review for disciplinary accuracy and cultural appropriateness before translated materials reach students. Predictive learning analytics may identify first-year students who need additional advising or instruction, but systems trained on historical institutional data can reproduce inequities associated with disability, language, gender or socioeconomic background. A dashboard does not neutrally portray a student: it constructs a profile that can shape the support, scrutiny or opportunities that person subsequently receives. The third pillar, the responsible AI ecosystem, consequently connects tool use to fairness audits, procurement rules, data governance, cybersecurity, algorithm evaluation and human review. Institutions would also need to examine whether vendors’ accessibility claims have been independently verified, whether algorithmic recommendations receive human scrutiny before affecting advising, and whether core teaching, assessment or advising functions have become dependent on one commercial platform.

Faculty capacity building forms the fourth pillar because institutional technology programs cannot deliver their intended benefits when instructors lack the knowledge or confidence to use them well. The study defines professional learning around five connected domains: AI literacy, inclusive pedagogy, responsible AI, critical evaluation of claims about artificial intelligence, and digital assessment design. Training on these subjects should be continuous rather than delivered as isolated workshops, with universities providing protected time, peer mentoring and accessible technical support. Lecturers are encouraged to redesign authentic assessment and multi-format activities rather than depend on software that attempts to detect generative AI use. Students also need instruction in responsible use, critical evaluation and understanding of their data rights. This need is particularly timely because the evidence reviewed by the study indicates that Gulf students already use generative tools for drafting, revision and exam preparation, sometimes ahead of consistent university guidance.

The fifth pillar, continuous evaluation, closes the feedback loop by examining learning, equity, accessibility, faculty outcomes and the effects of institutional AI policies. Recommended indicators are disaggregated by factors including disability, gender, language and socioeconomic background, allowing institutions to examine whether an apparently helpful tool produces different effects across groups. The framework also proposes three forms of critical oversight: an annual deprofessionalisation audit to identify grading judgments, risk flags or curriculum decisions that have moved from faculty to algorithms; a vendor-dependency register to make platform lock-in visible; and a periodic purpose check to ask whether institutional metrics have quietly narrowed the definition of successful learning. These mechanisms connect the framework to responsible governance because an institution cannot evaluate equity adequately if it monitors only overall averages, engagement metrics or aggregate performance.

For implementation, the study presents a four-stage roadmap: institutional readiness, capacity building, AI-enabled curriculum redesign and continuous improvement. Institutions would first assess their infrastructure, accessibility, faculty readiness and policy maturity. They would then develop governance policy, professional learning and accessibility standards, redesign programmes around UDL using responsible AI technologies, and monitor learning analytics, accessibility indicators and quality-assurance data to feed improvements back into the next cycle. A self-administered maturity model can locate an institution’s starting point by rating each pillar as beginning, in process or continuously improving. Stakeholder-specific actions include connecting national AI strategy with inclusive-education policy, setting minimum standards for transparency, fairness auditing and student-data protection, and investing in accessible Arabic-first infrastructure. Universities could form multidisciplinary governance committees involving academic leaders, disability specialists, technologists, legal and cybersecurity staff, and students.

The framework also assigns technology vendors a direct role. Vendors would be expected to meet accessibility standards as a contractual baseline subject to independent institutional audit, provide explainable and auditable systems, and invest in Arabic and multilingual model quality rather than treating those languages as secondary markets. The study draws on international comparisons to show why context matters. Canada’s binding accessibility requirements, New Zealand’s approach to cultural and data sovereignty, and the different governance arrangements in the United Kingdom, Australia, Singapore and Finland are treated as possible reference points, not directly transferable models. Kuwait’s simultaneous negotiation of AI governance and UDL, centralized national planning and need for Arabic-first infrastructure justify a context-specific framework. The comparison is conceptual and does not establish which national approach produces better student outcomes in the Gulf.

The paper is explicit about the empirical work still required. Its structured search covered Scopus, Web of Science, ERIC, Google Scholar and several policy databases. It returned approximately 1,450 records before deduplication and 1,120 after deduplication; title and abstract screening retained 210 records, while full-text screening retained 96 directly relevant sources, of which 42 appeared in the final reference list. The validation of the five pillars was conceptual, or face, validity based on comparison with reference frameworks. It did not establish content validity through a formal expert panel or construct validity through empirical testing. Applicability may also vary with institutions’ technological maturity, the synthesis drew predominantly on English-language research, and much of the supporting governance evidence originated outside the Gulf Cooperation Council. The proposed six-part research agenda calls for longitudinal or quasi-experimental studies of engagement, performance, retention and accessibility; tests of whether fairness auditing moderates disparate impacts; research tracing the pathway from leadership through faculty capability to outcomes; case studies involving disabled, multilingual and first-generation students; comparisons of institutional maturity; and expert consultation followed by factor analysis to validate the framework itself. The study’s principal contribution is therefore a testable institutional architecture, not proof that AI has already made Kuwaiti higher education more inclusive.

Subject of Research: A conceptual framework for integrating artificial intelligence, Universal Design for Learning and responsible governance in Kuwaiti higher education

Article Title: Artificial intelligence and universal design for learning in Kuwait’s higher education institutions: towards an AI-enabled framework for inclusive education

Article References: Alenezi, A. F. (2026). Artificial intelligence and universal design for learning in Kuwait’s higher education institutions: towards an AI-enabled framework for inclusive education. Frontiers in Education, 11, Article 1934276. https://doi.org/10.3389/feduc.2026.1934276

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1934276

Keywords: artificial intelligence, Universal Design for Learning, inclusive education, higher education, Kuwait, responsible AI, AI governance, learning analytics, faculty development, accessibility, Artificial, intelligence

The announcement describes a research and infrastructure program rather than the results of a clinical trial. Its central premise is that many AI algorithms have been proposed for aging and dementia care, but relatively few have undergone the rigorous evaluation, safe implementation and continuous monitoring required for routine practice. Duke AITC-AIGE will support pilot grants, data science resources, implementation assistance, governance tools and methods for assessing whether AI systems improve real-world outcomes for older adults and people with Alzheimer’s disease and related dementias.

Funding will provide about $5 million annually to the center, including approximately $4 million each year for research projects across the country. At least 80% of that research funding will be reserved for Alzheimer’s disease and related dementias, with the remainder supporting broader aging research. The nationwide program is designed to extend AI expertise beyond major academic medical centers and make support accessible to investigators at smaller institutions, community organizations, patients and caregivers who may have ideas for useful tools but lack the infrastructure required to build and test them.

The center is housed within the Duke Clinical and Translational Science Institute and is organized around three components. Juliessa Pavon, an associate professor in Duke’s Division of Geriatrics, leads the Administrative Core. Rishi Kamaleswaran, an associate professor of surgery and anesthesiology, leads the Clinical Implementation and Data Science Core. Sheng Luo, a professor of biostatistics and bioinformatics, leads the Research Innovation Core. Duke said the collaboration reflects the university’s capabilities in geriatrics, dementia research, translational science and responsible AI oversight.

Potential applications include detecting early cognitive decline by analyzing speech, behavior or data from wearable devices; identifying risks such as falls or wandering; supporting medication optimization and deprescribing; and helping caregivers monitor systems or access personalized guidance. These uses remain possibilities that require evaluation rather than established clinical benefits. The center’s role will include helping researchers determine which systems work in real-world settings, whether they improve outcomes and quality of life, and where their performance is insufficient or unsafe.

Transparency is a major focus of the initiative. Researchers and organizations will be able to develop and adapt model cards, standardized documents that summarize a tool’s intended purpose, training data, test performance, areas of strong performance and settings in which it may be less reliable. The cards can also document potential biases, limitations and appropriate uses. Pavon compared the idea to the nutritional information on food packaging, saying that clinicians and health systems should have a clear understanding of an AI tool’s risks and benefits before using it in a particular situation.

The Research Innovation Core will develop statistical and AI methods to identify performance limitations, investigate their causes and improve the reliability and generalizability of aging-related systems. Its work is particularly important because an AI model that performs well in a controlled research dataset may behave differently when applied to a new hospital, community or patient population. The press release does not report measured improvements from these methods; instead, it presents their development and testing as the center’s planned contribution to producing AI systems that can be assessed more systematically.

Duke will also develop the NeuroAI secure analytics and evaluation platform, which is intended to bring together medical records, brain images, laboratory results and genetic information. The platform will provide shared data and standardized methods for studying aging and dementia, developing AI tools and evaluating them. Duke said this common infrastructure could reduce the time, cost and specialized technical knowledge required to develop and test systems, making it easier for community researchers, smaller institutions, patients and caregivers to participate in innovation rather than leaving that work concentrated at large academic centers.

Beyond technical development, the center plans to provide national pilot funding and expertise in AI evaluation, data science, implementation, ethics, governance and stakeholder engagement. Its resources are intended to be reusable by investigators and organizations that are not part of a major academic AI center. Safety monitoring, implementation tools and standardized evaluation methods will sit alongside the research portfolio, reflecting the program’s emphasis that clinical adoption requires evidence about performance, oversight and continued operation in real environments.

The five-year goal is not simply to produce more algorithms, Pavon said, but to establish a clearer and reproducible route from innovation to safe, meaningful benefit. Success after five years would mean that researchers and health systems have better ways to determine which AI tools should enter practice, how they should be monitored and whether they support independence, care delivery, caregiver support and quality of life for older adults. The initiative therefore joins technical AI research with evaluation and governance infrastructure intended to address a central obstacle in translating aging-related AI from promising prototypes into dependable health tools.

Subject of Research: A national research infrastructure initiative for trustworthy artificial intelligence in aging and dementia care.

Article Title: Duke launches national hub to advance trustworthy AI for aging and dementia care

Article References: Duke launches national hub to advance trustworthy AI for aging and dementia care. Original publication (Bibliographic details unavailable.)

Image Credits: AI Generated

DOI: Not provided

Keywords: Duke University, trustworthy AI, aging care, dementia, Alzheimer’s disease, AI evaluation, clinical implementation, responsible AI, NeuroAI, National Institute on Aging, Duke, launches

Agentic AI differs from conventional systems because it can establish sub-goals, plan sequences of action and adapt its behavior with minimal human intervention, according to the review. Its possible applications include identifying discriminatory patterns in recruitment or resource allocation, tailoring healthcare and education, and supporting gender-sensitive governance. The same autonomy creates risks: when systems learn from data reflecting historical exclusion, they may reproduce or amplify unequal patterns without continuous human correction. The authors therefore describe agentic AI as both a potential tool for scalable inclusion and a mechanism capable of accelerating inequity when design and oversight fail.

The authors distinguish symbolic systems, which use persistent state representations and are described as more interpretable and verifiable, from neural or generative approaches, which offer flexibility but can be opaque. Symbolic methods may therefore be favored in safety-critical settings, while neural methods may support more open-ended tasks, but the review says the scientific community has not settled on a single architectural foundation. For agentic systems that can revise their goals or evaluation criteria after deployment, static safeguards may be inadequate. The review identifies historical, representational, selection, measurement, algorithmic, optimization and feedback bias as relevant categories, while arguing that these should not be treated as isolated lists because they can accumulate across data collection, modelling, deployment and publication.

Healthcare literature reviewed by the study supports explainability, human oversight, bias-impact assessment, phased testing and continuous monitoring, particularly where old records, structural discrimination or proxies such as cost in place of clinical need influence outcomes. Yet responsibility remains contested: some frameworks place it primarily with developers through pre-deployment audits, whereas others emphasize clinicians’ oversight after deployment. That uncertainty is especially consequential for autonomous systems that can adapt over time and potentially render an initial audit obsolete. A proposed “Regulatory Genome,” intended to let regulation evolve alongside technical change, remains a metaphor rather than an operational mechanism, and the proposed governance model has not yet been encoded into enforceable requirements.

Education research points to a similarly mixed prospect. Generative and agentic systems could support personalized learning, adaptive assessment, translation, equitable access and lifelong learning, aligning with the United Nations Sustainable Development Goals on quality education, gender equality and reduced inequalities. Risks include privacy violations, algorithmic discrimination and a widening digital divide. Personalization can also reproduce stereotypes if enrollment pathways are gendered, potentially discouraging women from advanced STEM modules. The review notes a shortage of longitudinal empirical evidence on whether implementation of AI-enabled learning-management systems actually improves gender parity in STEM achievement, so evidence of access to a platform should not be confused with demonstrated educational outcomes.

The review’s proposed human-centred framework is a conceptual hypothesis rather than an empirically validated standard. It places inclusive design, human oversight and accountability at the center of four interconnected pillars: agentic AI design and architecture, gender equality in the digital economy, sustainable development synergy, and transformative governance. Inclusive design calls for participatory development, bias audits, attention to the digital divide, workforce transformation and equitable access. The architecture pillar combines goal-driven autonomy with human-in-the-loop mechanisms that allow people to monitor, correct or override decisions. The sustainability pillar adds environmental compatibility, planetary wellbeing and responsible analysis of data related to the Sustainable Development Goals.

One of the framework’s distinguishing proposals is “Equity by Design,” which moves fairness considerations from a retrospective audit into system architecture, objective setting and planning. This is particularly relevant for agents capable of changing behavior in response to their environment or to the fact that they are being evaluated. A system able to modify its goals may make a one-time pre-deployment assessment an unreliable description of its future conduct. The framework also combines lifecycle governance with gender-equity outcome metrics and Sustainable Development Goal alignment, and proposes an adaptive regulatory mechanism. The authors are careful to position this approach as an integration of earlier responsible-AI ideas, not a claim that lifecycle governance, human oversight or adaptive regulation is novel.

Keyword analysis found three main thematic groups: digital transformation, covering industry, digitalisation, sustainable business and innovation; gender and equity, including women’s empowerment, entrepreneurship and STEM; and sustainability governance, involving sustainable development, green transition, corporate social responsibility and policy. Connections between these groups were sparse. Only 3% of keyword pairs linked the gender cluster with sustainability governance, while “agentic AI” and “autonomous systems” appeared in fewer than 5% of records. The corpus was also geographically skewed: 61% of first authors were affiliated with institutions in the European Union, North America or Australia, while South Asia and sub-Saharan Africa each contributed less than 10%. The authors caution that their corpus is a proxy for an emerging field and that scarce representation of intersectional experiences could conceal distinct forms of disadvantage.

The study’s central message is that greater autonomy makes conventional, one-time governance inadequate. Its proposed response is continuous lifecycle management combining gender-disaggregated data, pre-deployment and ongoing audits, human override capabilities, logging of goal modifications, phased deployment, interdisciplinary teams and cooperation among regulators, developers, civil society and affected communities. The review also identifies an unresolved ecological question: although algorithmic and social risks are widely discussed, the carbon and resource footprint of training and operating autonomous multi-agent systems receives little attention in equity-focused governance. The authors call for lifecycle environmental assessments, longitudinal studies of gender outcomes, participatory design led by women and gender minorities from the Global South, and empirical validation through deployments, consultations and cross-cultural comparisons before the framework can be treated as an evidence-based governance standard.

Subject of Research: Governance of agentic AI for gender equity, human oversight and sustainable development

Article Title: Advancing gender equity and inclusive digital futures through responsible agentic artificial intelligence governance

Article References: Jain, B., & Gupta, R. (2026). Advancing gender equity and inclusive digital futures through responsible agentic artificial intelligence governance. Discover Global Society, 4(1), Article 281. https://doi.org/10.1007/s44282-026-00624-y

Image Credits: AI Generated

DOI: 10.1007/s44282-026-00624-y

Keywords: agentic AI, gender equity, algorithmic bias, responsible AI, human oversight, equity by design, sustainable development, AI governance, bibliometric analysis, digital transformation, Advancing, gender

The research, published in Frontiers in Education by Abdullah F. Alenezi of the Public Authority for Applied Education and Training in Kuwait City, proposes a governance framework called the AI- and Learning-Analytics-Enabled Inclusive Support Framework for Learning Difficulties, or ISF-LD. Its central claim is deceptively simple: the question facing universities is no longer whether to adopt AI, but how to adopt it responsibly so that it promotes equitable and inclusive learning. The framework was developed through a structured conceptual methodology that synthesised four bodies of evidence, including research on learning difficulties and disability inclusion, scholarship on AI and responsible technology, national and international policy documents, and established educational and disability theories.

The urgency of the problem is rooted in a paradox familiar to disability researchers worldwide. Many learning difficulties are invisible disabilities, and students frequently choose not to disclose them because of stigma, fear of discrimination or worries about how faculty and peers will perceive them. In Kuwait, studies suggest that disclosure decisions are shaped by cultural expectations surrounding academic success, family reputation and societal attitudes towards disability. Because disclosure is typically the gateway to formal accommodations, students who stay silent often progress through their degrees without the specialist support they need. A nationwide survey by the Kuwait Dyslexia Association once estimated dyslexia prevalence at roughly six percent, though the authors caution that this figure has not been updated through recent peer-reviewed epidemiological research and should be treated as indicative.

Kuwait presents a striking case study in policy disconnection. On one side, the country has strengthened disability rights through Law No. 8 of 2010 and its ratification of the United Nations Convention on the Rights of Persons with Disabilities in 2013. On the other, Kuwait Vision 2035 and the national AI strategy have poured investment into digital transformation and AI-enabled education. What the policy analysis reveals, however, is that these agendas have evolved almost entirely in parallel. The national AI strategy sets targets for AI adoption across economic sectors but never mentions accessibility or learning analytics governance, while the Ministry of Education’s AI in education strategy focuses on curriculum integration and teacher training without referencing disability inclusion or universal design. The paper’s review of publicly available documents found no documented intersection between the two domains.

This governance gap is not unique to Kuwait. Across the Gulf Cooperation Council, including Saudi Arabia, the United Arab Emirates, Qatar, Bahrain and Oman, national disability legislation and ambitious AI strategies have advanced simultaneously but with little policy integration. Disability frameworks emphasise participation and reasonable accommodation, while AI strategies emphasise innovation and economic diversification. Few, if any, explicitly consider how AI should be governed to support students with learning difficulties, how ethical learning analytics should operate, or how algorithmic fairness and accessibility should be enforced in higher education. As universities across the region rapidly deploy AI-supported teaching and assessment, the authors warn that existing barriers risk being reproduced at scale rather than dismantled.

To close this gap, the ISF-LD rests on six theoretical pillars. The Social Model of Disability reframes the problem: barriers arise from inaccessible curricula, assessment practices and institutional cultures, not from individual impairment, so technologies must remove barriers rather than reinforce them. Vygotsky’s Sociocultural Theory positions AI tools such as intelligent tutoring and speech recognition as scaffolds within a learner’s zone of proximal development, augmenting rather than replacing human teaching. Self-Determination Theory insists that motivation depends on autonomy, competence and relatedness, meaning predictive systems must strengthen learner agency rather than make opaque, compulsory decisions. Universal Design for Learning pushes accessibility into mainstream curriculum design, reducing dependence on disclosure-based accommodation. Learning analytics ethics and responsible AI principles supply the safeguards of privacy, transparency, fairness, accountability and explainability.

Structurally, the framework comprises five interconnected implementation layers: national policy and governance, institutional leadership and disability services, faculty capability, AI-enabled inclusive pedagogy and assessment, and student self-determination. Crucially, these layers operate through an iterative, non-linear model rather than a top-down sequence. A university might begin by establishing an AI governance committee before national guidance exists, or through faculty-led accessibility initiatives, provided the missing layers are developed later. Student experience feeds back into institutional and national policy through advocacy and participation, ensuring that implementation remains responsive to the people it is meant to serve. Four cross-cutting principles, namely rights-based governance, stigma-aware disclosure, universal accessibility and responsible AI, apply across every layer, from procurement standards to classroom practice.

The technical risks the framework addresses are concrete rather than hypothetical. Algorithmic bias may arise where training data underrepresent students with disabilities or Arabic-language users, meaning AI systems can perform unevenly across linguistic and cultural contexts. Predictive learning analytics can infer disability status from behavioural data without explicit disclosure, raising acute concerns about privacy, informed consent and surveillance. Generative AI can produce hallucinated content, and proprietary platforms create commercial dependence without disability-specific data governance arrangements. The framework’s response includes data minimisation, systems that do not infer disability status without consent, explainable recommendations that students can understand and challenge, and meaningful human oversight of every AI-supported decision affecting learning or accommodation.

What distinguishes the ISF-LD from existing frameworks, the paper argues, is integration. Universal Design for Learning guides curriculum design but says little about governing AI systems; the SHEILA framework supports institutional learning analytics policy but pays little attention to disability; the OECD and UNESCO recommendations establish broad ethical principles without explaining how universities should operationalise them for students with learning difficulties. The new framework deliberately bridges these domains, positioning AI as a governed educational capability rather than a stand-alone innovation, and inclusion as a governance challenge rather than merely a pedagogical or technological one. It also generates five testable research propositions linking policy alignment, institutional capability, inclusive pedagogy, responsible AI practices and student-centred implementation to measurable outcomes such as accessibility, engagement, retention and trust.

The author is candid about the study’s limitations. The framework is conceptual and has not yet been empirically validated; it was developed without direct consultation with students with learning difficulties, which future participatory research should remedy under the disability movement’s principle of nothing about us without us; and the policy analysis relied on publicly available documents that may not capture internal institutional practice. A staged research agenda proposes qualitative case studies in Kuwaiti universities, expert Delphi panels, institutional surveys across the GCC, quasi-experimental studies of AI-enabled pedagogy and longitudinal tracking of student outcomes. Even so, the message travels well beyond the Gulf. As universities everywhere rush to embed AI in teaching and assessment, the Kuwaiti study offers a sobering reminder that technological innovation alone will not deliver educational equity. Sustainable inclusion requires governance models that place disability rights, ethical AI and learner participation at the centre of institutional decision-making, and the ISF-LD offers one blueprint for building them.

Subject of Research: AI and learning analytics governance for inclusive support of students with learning difficulties in Kuwaiti and Gulf higher education

Article Title: Toward an AI- and learning-analytics-enabled inclusive support framework for students with learning difficulties in Kuwait's higher education institutions

Article References: Alenezi, A. F. (2026). Toward an AI- and learning-analytics-enabled inclusive support framework for students with learning difficulties in Kuwait's higher education institutions. Frontiers in Education, 11, Article 1931442. https://doi.org/10.3389/feduc.2026.1931442

Image Credits: AI Generated

DOI: 10.3389/feduc.2026.1931442

Keywords: artificial intelligence, learning analytics, inclusive education, learning difficulties, disability inclusion, higher education, Kuwait, Gulf Cooperation Council, responsible AI, Universal Design for Learning, education policy, algorithmic bias

The motivation is straightforward. Since the COVID-19 pandemic accelerated online and distance learning, institutions have wrestled with examination integrity, candidate authentication, and the credibility of remotely administered tests. Weak oversight creates openings for impersonation, unauthorised assistance, and prohibited devices. Meanwhile, automated grading promises speed and consistency but raises questions about validity, explainability, and where human academic judgement must remain in charge. The authors argue that the educational AI literature has typically treated monitoring, scoring, feedback, and analytics as distinct application areas, and that the real innovation worth testing is not any single component but the coordinated workflow that binds them together.

Eduvate CBT is built on a four-layer architecture: a presentation layer with role-specific dashboards for administrators, instructors, and students; an application layer handling examination scheduling, question management, session control, proctoring, grading, and analytics; a data and intelligence layer combining relational databases with computer vision-based proctoring and language model-based evaluation modules; and an infrastructure layer running on PHP 8.x and MySQL/MariaDB with HTTPS/TLS encryption. The technology choices were deliberately conventional, the authors explain, so that resource-limited institutions could deploy the system without specialised infrastructure. Role-based access control ensures that administrators manage configurations, instructors handle examination content, and students interact only with restricted assessment interfaces.

The proctoring engine is the platform’s most technically ambitious element. It captures real-time webcam video and applies computer vision to monitor facial presence, head movement, and eye gaze direction. Environmental signals such as background noise and system activity patterns are analysed for external interference, while mouth-movement detection flags possible unauthorised communication. Across the 250 sessions, the subsystem logged 1,847 behavioural events, with tab switching accounting for 33.1 percent, window-focus loss 26.4 percent, camera-frame absence 20.6 percent, and gaze deviation 14.5 percent. Crucially, the researchers validated performance at the session level rather than treating each event as an independent classification case, a distinction that matters enormously for interpreting the statistics honestly.

Three instructors independently reviewed recorded session evidence and reached consensus on whether each session contained a violation, providing the ground truth for the confusion matrix. The results were striking: session-level accuracy of 94.8 percent (95 percent CI 91.3 to 96.9), precision of 94.6 percent, recall of 96.6 percent, specificity of 92.3 percent, and an F1-score of 95.6 percent. Only 13 of 250 sessions were misclassified, comprising 8 false positives and 5 false negatives. The high recall is particularly important in examination contexts, where a missed violation undermines assessment credibility. Yet the authors are careful to note that the validation dataset did not preserve which behavioural event categories drove the errors, and that automated flags must be interpreted within transparent, fair, and privacy-conscious review processes because normal behaviour can sometimes be misclassified as suspicious.

Automated grading delivered equally impressive numbers. The platform processed 7,500 responses across the cohort, and in a randomly selected validation sample of 750 responses, 742 automated grades agreed exactly with instructor-consensus scores, corresponding to 98.9 percent agreement (95 percent CI 97.9 to 99.5). Eight discrepancies were identified but not categorised by cause, and the authors treat them simply as cases requiring human review. The efficiency gains were dramatic: the system completed grading in approximately 9 seconds, compared with an estimated 23.1 hours of manual grading staff time for the same 250 examinations, an operational reduction of roughly 9,240-fold. The authors stress that this is a staff-time benchmark rather than a monetary saving, and that actual savings will vary with institutional workflows and assessment formats.

Beyond raw performance, the analytics dashboard demonstrated a closed-loop function that connects assessment evidence to instructional action. The system identified six examination items with difficulty indices below 0.30, concentrated in algorithmic complexity notation within a Data Structures and Algorithms course. Rather than limiting outcomes to score reporting, the platform enabled instructors to pinpoint specific knowledge areas where learners struggled. The authors frame this within Assessment for Learning principles: the dashboard surfaces patterns, but instructors must determine whether the cause is conceptual difficulty, ambiguous wording, pacing, or a mismatch between instruction and assessment before choosing an intervention such as reteaching or item revision.

Usability results were equally strong. Fifteen instructors and five administrators completed the System Usability Scale after extensive interaction with the platform during examination setup, administration, monitoring, grading, and result management. The aggregate SUS score was 87.4 (SD 5.8, 95 percent CI 84.7 to 90.1), placing it within the commonly interpreted excellent range. The relatively narrow confidence interval indicates the high mean was not driven by extreme ratings. However, the study reports only the aggregate result rather than item-level response profiles, so it cannot identify which individual usability statements were rated highest or lowest, a gap the authors acknowledge for future refinement.

The security architecture is documented as a defence-in-depth design rather than a certified guarantee. Cross-site scripting prevention and Content Security Policy headers protect the browser layer, PDO prepared statements with parameter binding mitigate SQL injection, session-based CSRF tokens validate state-changing requests, and IP whitelisting restricts access during high-stakes examinations. Examination-specific safeguards include exam window enforcement, session integrity validation, and question randomization. The authors explicitly note that no independent penetration test or external security certification was performed, and that the controls should be understood as an architectural description rather than proof of resistance to all possible attacks.

The study’s limitations are candidly acknowledged. The evaluation was conducted at a single tertiary institution within computing-related courses, creating possible sampling and context bias. No competing proctoring or grading platform was included as a control condition, so the results demonstrate within-system performance rather than comparative superiority. Individual rater labels were not retained after consensus was established, precluding formal inter-rater reliability analysis for both proctoring and grading validation. The SUS result relied on self-report from only 20 staff users. The authors also emphasise that institutional deployment requires explicit governance for privacy notice, lawful basis for processing, retention periods, access to evidence, deletion procedures, appeals, and reasonable accommodations, particularly under frameworks such as the Nigeria Data Protection Act 2023. Future work should prioritise multi-institution replication, comparative evaluation, formal rater-reliability analysis, event-specific error analysis, independent security testing, and longitudinal educational-outcome analysis before broader claims of generalisability can be made.

Subject of Research: Educational technology

Article Title: Development and evaluation of eduvate CBT as an integrated artificial intelligence powered assessment ecosystem

Article References: Awofesobi, P., & Obiyemi, O. (2026). Development and evaluation of eduvate CBT as an integrated artificial intelligence powered assessment ecosystem. Discover Education, 5(1), Article 1197. https://doi.org/10.1007/s44217-026-02317-9

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02317-9

Keywords: computer-based testing, AI-assisted proctoring, automated grading, examination integrity, learning analytics, educational technology, assessment ecosystem, System Usability Scale, role-based access control, responsible AI, Development, evaluation

The article, authored by Layli Navruz-zoda of Bukhara State University and colleagues at institutions across Uzbekistan, does not present new experimental data. Instead, it offers something arguably more consequential for the field: a carefully constructed conceptual framework, the Human-Centered AI Adoption Framework, that stitches together two research literatures which have largely evolved in isolation. One literature, rooted in the dynamic capabilities tradition, shows that internal digital and dynamic capabilities predict AI adoption and performance more strongly than external support. The other, drawn from human-centered and responsible AI research, specifies what a workable human-AI configuration looks like. What has been missing, the authors contend, is the junction between them, along with the methodological consequences that follow.

The framework rests on a deliberately restricted set of four human-centered principles: meaningful human control, transparency and explainability, augmentation where appropriate, and trust and accountability. The authors are explicit that this is a subset, not a comprehensive charter. Each principle earned its place through a retention rule requiring both recurrence across the human-centered AI literature and a stated mechanism linking it to continued use at small-firm scale. The logic is pragmatic: in a small enterprise, a tool that staff cannot understand or override is simply abandoned rather than formally contested. Broader requirements such as fairness, privacy, worker participation, safety, limits on surveillance and accessibility enter the framework as a cross-cutting safeguards dimension, acknowledged as normative design requirements whose SME-scale mechanisms remain unspecified.

At the technical heart of the framework lies a distinction that sounds subtle but carries real analytical weight: the difference between reconfiguring and adaptation. Reconfiguring is an activity, the work of redesigning roles, procedures and decision rights around a new tool. Adaptation is a state, the realized configuration of formal arrangements, shared norms and skills that the activity produces. The correspondence between the two is imperfect. Firms attempt redesigns that revert, procedures get rewritten but not followed, training is delivered while roles remain unchanged. That residual variance, the authors argue, is not statistical noise but the very location of symbolic use, the phenomenon where tool ownership is recorded while routines stay frozen.

This distinction underpins the article’s six propositions, organized under two objectives. The first objective concerns how human-centered design conditions the conversion of AI access into capability. Proposition P1.1 holds that a human-centered foundation raises the likelihood that sensing, seizing and reconfiguring occur, the classic microfoundations of dynamic capabilities. Proposition P1.2 predicts that where the foundation is absent, adoption stalls at symbolic use. Proposition P1.3 rejects the assumption that AI is uniformly augmentative, arguing instead that the appropriate configuration depends on task criticality, error cost, employee expertise, regulatory constraint and system type; full automation remains appropriate for some routine, low-stakes, high-volume tasks.

The second objective addresses how organizational adaptation converts capability into competitiveness. Proposition P2.1 asserts that reconfiguring produces structural, cultural and human-capital adaptation as a realized state downstream of it, and that the two are empirically separable. Proposition P2.2 goes further: adaptation carries a substantial part of the association between AI capability and competitiveness, so controlling for it should attenuate, though not necessarily eliminate, the direct link between tool ownership and performance. Proposition P2.3 proposes a temporal ordering in which competitiveness dimensions realize in sequence, with efficiency emerging earliest because it follows from task-level substitution once use is embedded, responsiveness and innovation arriving later, and realized resilience observable only across an actual disruption.

What elevates the article beyond conceptual synthesis is its methodological rigor. Each proposition is paired with candidate indicators and a research design capable of testing it. For P1.2, the authors suggest measuring the gap between tool ownership and the share of relevant transactions actually passing through the tool, using longitudinal panels with archival and log data. For P2.1, they propose comparing a one-factor measurement model against distinct but correlated factors, treating discriminant validity as a test of the claim rather than an assumption. For P2.2, they recommend three-wave panels with baselines, difference-in-differences designs around exogenous deployment timing, and matched comparisons to address the self-selection that plagues adoption research. The authors are candid that self-reported use cannot separate symbolic from embedded use, and that only behavioral and archival traces, such as system access logs and documented procedure revisions, can.

The framework’s engagement with emerging and transition economies adds a distinctive dimension often absent from AI adoption research. The authors note that public cloud AI compute is concentrated in a small number of jurisdictions, meaning most countries consume AI services hosted elsewhere, a structural constraint they expect to limit reconfiguring to what foreign platforms permit. Language compounds the challenge: model performance in lower-resource languages remains materially weaker than in English, and Uzbek is a documented low-resource case where basic annotated resources have only recently been constructed. The authors expect the transparency principle to bind harder in such settings, because staff cannot verify outputs they cannot fully read. They also flag that Uzbekistan’s regulatory framework addresses data localization and national AI capacity but does not yet specify obligations concerning algorithmic management or the contestation of automated decisions, meaning safeguards are least likely to be supplied by law and most likely to require deliberate design.

The practical implications are refreshingly concrete. Firms should conduct readiness assessments with staff rather than for them, treating the proportion of employees able to explain what a tool does as a deployment gate. They should define per-tool success criteria before piloting, including a stated override rate at which the tool is reviewed. And they should schedule an explicit reconfiguring step, revising written procedures and role descriptions within a defined period after go-live, treating the absence of such revision as the diagnostic signal that symbolic use has taken hold. Which measure binds first depends on context: in a small manufacturer deploying predictive maintenance, the override measure matters most because unexplained recommendations get disregarded; in a professional-services firm using generative drafting, procedure revision matters more because output is easy to adopt informally but hard to govern.

The authors acknowledge four limitations with unusual candor: the framework is conceptual and untested, its human-centered layer is deliberately restricted, its constructs derive largely from high-income settings, and its indicators are unvalidated. Each limitation comes paired with a remedy, from multi-wave designs to comparative work in transition economies. Perhaps the most consequential extension they identify is the inclusion of customer and community effects, which are currently absent from the framework entirely. Worker-level outcomes such as autonomy, workload, skill use and job quality, by contrast, appear as ends in their own right, capable of diverging from firm competitiveness rather than tracking it. The article’s closing message is deceptively simple: an AI purchase is the beginning of a change project rather than the end of a decision, and researchers should follow firms past the point of purchase. Whether the framework’s propositions survive empirical testing remains an open question, but the authors have done the harder preliminary work of specifying exactly what would have to be observed for the connection between human-centered design and small-firm competitiveness to hold.

Subject of Research: A conceptual framework linking human-centered AI design principles to capability formation and organizational adaptation in small and medium-sized enterprises

Article Title: Human-centered AI adoption for small and medium-sized enterprise competitiveness: a methodological perspective on digital capabilities and organizational adaptation

Article References: Navruz-zoda, L., Najmiddinov, S., Norboev, S., Ibadullaev, E., & Zakirov, M. T. (2026). Human-centered AI adoption for small and medium-sized enterprise competitiveness: a methodological perspective on digital capabilities and organizational adaptation. Frontiers in Human Dynamics, 8, Article 1939008. https://doi.org/10.3389/fhumd.2026.1939008

Image Credits: AI Generated

DOI: 10.3389/fhumd.2026.1939008

Keywords: artificial intelligence, SMEs, human-centered AI, dynamic capabilities, organizational adaptation, digital transformation, AI adoption, competitiveness, responsible AI, transition economies, work design, research methodology

The central premise of the proposal is that a useful digital framework in autism should be construct-preserving rather than construct-erasing. Many contemporary AI systems optimize prediction directly from raw data without retaining human-interpretable inferential layers. That approach may be appropriate for some tasks, but the author argues it is poorly suited to a clinical ecosystem in which the meaning of a measurement emerges from the interaction of several inferential layers rather than from raw signals alone. FIAP®-Digital is therefore designed to integrate multimodal inputs, including biological, physiological, developmental, contextual, therapeutic-process, and longitudinal indicators, into structured estimates of clinically defined constructs such as the Biological Burden Index, energetic capacity, adaptive neurodevelopmental window accessibility, therapeutic engagement accessibility, neuroplastic accessibility, and higher-order translational profiles.

Technically, the proposed architecture is organized into six computational layers. The first is a multimodal input architecture that organizes heterogeneous data streams by modality and temporal structure, ranging from biomarkers and physiological measures to developmental and therapeutic-process variables. The second is a construct-estimation engine that converts raw data into interpretable translational layers such as the Biological Burden Index and energetic capacity. The third is a translational profile-inference engine that infers higher-order multidomain configurations, including burden-dominant, reserve-constrained, timing-sensitive, engagement-vulnerable, and mixed profiles, from the interaction among constructs rather than from raw data directly. The remaining layers handle explainability and clinician-in-the-loop review, precision-oriented research outputs, and longitudinal updating that tracks how profiles shift across time.

The author identifies a specific architectural gap in existing autism AI research. Many machine learning approaches are optimized for diagnostic classification, biomarker discovery, behavioral phenotyping, or outcome prediction, and multimodal data are often fused into statistical representations that may improve performance while reducing visibility of the intermediate clinical and biological constructs that matter for interpretation. Few computational frameworks explicitly connect biological burden, adaptive reserve, developmental timing, therapeutic engagement, neuroplastic accessibility, and intervention responsiveness. FIAP®-Digital differs from generic multimodal precision psychiatry pipelines along six dimensions: its primary goal is construct-guided stratification rather than diagnostic classification; its intermediate representations are explicitly defined constructs rather than opaque latent variables; its outputs are clinician-readable research profiles rather than autonomous decisions; its explainability is built around construct contributions, missingness, and uncertainty rather than post-hoc feature importance alone; its validation roadmap includes construct validity, usability, fairness, and uncertainty calibration rather than model performance alone; and its current context of use is hypothesis generation and prototype testing, not clinical decision support.

On the question of fusion, the paper argues that intermediate or construct-level fusion is likely the most appropriate starting point because it preserves interpretability. Rather than merging all data into a single undifferentiated feature space, modality-specific indicators would first be mapped to candidate constructs. Sleep disruption, gastrointestinal burden, fatigue, and stress reactivity, for example, would be integrated into a Biological Burden Index-related representation, while regulation, attention, persistence, and responsiveness to support would be integrated into a therapeutic engagement representation. Higher-order translational profiles would then be inferred from interactions among construct-level estimates. The objective is not only predictive accuracy but preservation of translational meaning, a distinction the author argues separates the architecture from purely performance-driven multimodal systems.

Temporal modeling is a core requirement because autism intervention responsiveness is dynamic. Biological burden, energetic reserve, engagement availability, contextual demands, and developmental readiness may vary across time, so the architecture requires longitudinal updating rather than static one-time classification. Candidate approaches include longitudinal mixed-effects models, state-space models, dynamic Bayesian updating, repeated-measures trajectory models, temporal latent profile models, recurrent architectures, or transformer-based longitudinal representations. In early feasibility phases, the paper suggests that simpler longitudinal tracking and rule-based temporal summaries may be more appropriate than complex deep learning models, with gradual methodological escalation as data volume and validation maturity increase.

Missing data and uncertainty receive equally prominent treatment. Missingness in autism research is not treated merely as a technical nuisance; it may carry interpretive meaning when certain modalities are systematically unavailable because of service access, family burden, sensory intolerance, cost, geographic location, or institutional constraints. The architecture would include modality-specific missingness flags, data-completeness scores, and uncertainty inflation when key information is unavailable. Uncertainty would be visible at multiple levels, from variable to construct to profile to longitudinal interpretation. A burden-dominant profile might be estimated with high confidence when multiple convergent indicators are present, but with low confidence when key biological or contextual data are missing. When data are insufficient to estimate a construct reliably, the system should explicitly indicate low confidence rather than generating a deterministic profile.

Human supervision is positioned as the governance layer that makes AI use proportionate and ethically bounded. AI is assigned six restricted functions: organizing heterogeneous inputs by modality, source, timestamp, quality, and missingness; harmonizing variables across records and sites; mapping indicators to provisional FIAP® constructs; estimating construct-level profiles with uncertainty flags; identifying longitudinal patterns and profile shifts; and generating clinician-readable explanations of profile drivers, data gaps, and uncertainty. The system does not make autonomous diagnostic, prognostic, eligibility, or treatment decisions. Clinicians and qualified researchers remain responsible for interpretation, contextualization, contestability, and deciding whether an output is meaningful for research use. The paper also proposes a staged validation roadmap covering feasibility testing, construct validity, internal and external validation, longitudinal robustness, uncertainty calibration, clinician usability, fairness assessment, and low-resource adaptation, with a tiered architecture that begins with clinically accessible information such as caregiver report and clinician observation before adding biomarkers, physiological sensors, or video analysis only when feasible and ethically justified.

The boundaries of the proposal are stated with unusual clarity. FIAP®-Digital is not presented as a validated diagnostic tool, treatment recommendation system, autonomous AI model, or clinically proven platform. It should not be used to diagnose autism, assign treatment, determine service eligibility, predict individual outcomes, or replace clinical judgment. The core FIAP® constructs remain hypothetical and have not undergone formal construct validation, and no clinical evidence of effectiveness is claimed. The manuscript is a conceptual article that reports no original empirical data, prototype testing, computational experiments, or clinical trial findings, and the proposed computational architecture is not an implemented algorithm. The author acknowledges that some constructs may require revision, subdivision, or replacement after empirical testing.

The primary contribution, then, is to define a responsible computational pathway for testing whether FIAP® constructs can support future precision-stratified autism research. If the constructs survive empirical evaluation, the framework may eventually contribute to precision-stratified autism research and to more responsible, interpretable, and developmentally sensitive digital phenotyping approaches. Until then, the architecture stands as a neuroinformatics foundation and validation roadmap, specifying how future prototypes could be designed, audited, and empirically tested before any clinical translation is considered.

Subject of Research: Neuroinformatics

Article Title: Toward FIAP®-Digital: an interpretable multimodal AI architecture for translational stratification and precision care in Autism

Article References: Fuamba, Y. (2026). Toward FIAP®-Digital: an interpretable multimodal AI architecture for translational stratification and precision care in Autism. Frontiers in Neuroinformatics, 20, Article 1868068. https://doi.org/10.3389/fninf.2026.1868068

Image Credits: AI Generated

DOI: 10.3389/fninf.2026.1868068

Keywords: artificial intelligence, autism spectrum disorder, explainable AI, multimodal integration, neuroinformatics, precision-stratified research, responsible AI, translational stratification, Toward, FIAP, Digital, interpretable

The study, conducted by Sunkyung Shin of Woosuk University and Jooyeun Park of Hankuk University of Foreign Studies, drew on written structured expert interviews with eleven media experts from academia, industry, and national policy research institutes in South Korea. Through qualitative content analysis, the researchers sought to understand how these experts perceive the societal impacts of AI and what policy directions they believe could mitigate associated risks. The findings reveal a landscape of interconnected concerns and opportunities spanning three domains: individual, societal, and industrial.

At the individual level, all eleven participants identified enhanced convenience as a major benefit of AI, largely attributed to recommendation systems, smart home technologies, and automation services that simplify daily decision-making. Experts also pointed to personalized services, improved access to information, and support for creative activities as individual-level advantages. Yet these benefits came with significant caveats. Six participants expressed concern about personal data protection and security, noting that AI systems must continuously learn from users’ daily data, creating persistent vulnerabilities to privacy infringement and information leaks. Others worried about declining cognitive and critical thinking abilities due to excessive reliance on AI, algorithmic bias, and the deepening of the digital divide.

The societal-level analysis revealed even starker tensions. Every single expert identified increased unemployment due to AI-induced job displacement as a major societal risk. Participants described generative AI as likely to reduce demand for human labor, leading to the disappearance of certain jobs or shifts into low-wage roles. As AI moves into professional sectors, overall labor demand may decline, reducing access to quality employment and intensifying industry competition and profit disparities. Four participants also identified the potential for AI to intensify social polarization and inequality, particularly through the concentration of AI technologies and economic benefits within a small number of large firms.

At the industrial level, the experts focused heavily on the media and entertainment sector, where AI is reshaping content production across creation, editing, distribution, and consumption. Ten participants identified increased efficiency in content production as a positive industrial impact, noting that AI-based video production and personalized content delivery contribute to content quality, competitiveness, and accessibility. However, five participants also identified job reduction and role replacement as industrial-level risks, alongside growing polarization between large and small industry actors, intellectual property and copyright disputes, and the spread of fake information and news.

When asked about policy responses to these risks, the experts converged on a striking consensus: AI literacy education was mentioned by all eleven participants as a critical policy response. This unanimous agreement underscores the experts’ belief that expanding public understanding of data practices, misinformation, and human–AI interaction is essential for risk mitigation. Other policy responses included strengthening corporate accountability through self-regulation, mentioned by nine participants, and improving government regulations and legal frameworks, mentioned by seven participants. Experts emphasized that firms developing AI systems should anticipate potential harms and take proactive measures to address them, as neglecting societal impacts could trigger more stringent state intervention.

The study’s most significant contribution lies in its repositioning of AI literacy from a narrow technical skill set to a multidimensional public capability. The analysis identified four core values that should guide AI literacy policy: ethics and responsibility, practical and real-world applicability, social inclusion and accessibility, and autonomy-oriented learning approaches. Experts argued that AI literacy should encompass the capacity to recognize and manage risks such as misinformation, intellectual property conflicts, and potential societal harms, while also providing learning opportunities grounded in practical contexts and ensuring equitable access across different age, income, and social backgrounds.

The emphasis on social inclusion reflects concerns about technological polarization and unequal access to AI-related opportunities. Participants stressed the importance of ensuring that people of all ages and socioeconomic backgrounds have equitable access to AI education and resources, which they view as crucial for narrowing the digital divide and enabling everyone to benefit from AI advancements. The autonomy-oriented dimension highlights the role of user agency, critical thinking, and the capacity for independent evaluation of AI systems, with experts noting that purely regulation-centered models may be limited because coercive or top-down approaches are unlikely to be effective.

South Korea offers a particularly compelling context for this research. The country became the second nation to implement a comprehensive AI legal framework, with the Basic Act on the Promotion of Artificial Intelligence and the Establishment of a Foundation for Trust taking effect in January 2026, modeled after the European Union’s Artificial Intelligence Act. This legislative milestone, combined with vigorous investment by globally competitive firms such as Samsung, Naver, and Kakao, and a technologically literate user base, positions South Korea as a valuable case for examining how policy frameworks and literacy strategies are formulated in a rapidly digitizing environment.

The study’s findings suggest that effective AI governance requires moving beyond the traditional binary of innovation versus control. Instead, the researchers propose a multilayered governance model that simultaneously cultivates user competence, encourages responsible industry behavior, and situates regulatory authority within a broader ecosystem of shared responsibility among government, industry, and civil society. This approach reflects a shift toward co-regulation, in which multiple stakeholders jointly contribute to the development of norms and safeguards for technological advancement and social protection. The researchers acknowledge that their framework is expert-informed and interpretive rather than empirically validated, and they call for future research to engage citizens, digitally marginalized populations, affected communities, and policymakers in its participatory refinement.

Subject of Research: AI governance

Article Title: Developing an AI literacy policy framework for responsible AI governance: perspectives from South Korean media experts

Article References: Shin, S., & Park, J. (2026). Developing an AI literacy policy framework for responsible AI governance: perspectives from South Korean media experts. Frontiers in Communication, 11, Article 1966278. https://doi.org/10.3389/fcomm.2026.1966278

Image Credits: AI Generated

DOI: 10.3389/fcomm.2026.1966278

Keywords: AI literacy, AI governance, South Korea, AI policy, media experts, responsible AI, digital divide, Developing, literacy, policy, framework, responsible

The review followed the methodological framework of Arksey and O’Malley and was reported according to the PRISMA Extension for Scoping Reviews. The author searched eight major databases, including PubMed, ERIC, ACM Digital Library, ScienceDirect, Scopus, and Wiley Online Library, for peer-reviewed and selected grey literature published between January 2019 and October 2025. Out of 1,847 records identified, plus 12 from grey literature sources, 1,436 titles and abstracts were screened after duplicate removal, and 63 full-text articles were assessed. Just 19 primary studies met the eligibility criteria, supplemented by one earlier Brazilian study retained as historical background. The evidence base spanned empirical investigations, systematic reviews, policy reports, bibliometric analyses, and conceptual studies from Latin America, Sub-Saharan Africa, South Asia, East Asia, and cross-regional Global South research.

What emerged was not a single trajectory of AI adoption but a geographically differentiated maturity landscape. Latin America, represented by five studies, is dominated by collaborative capacity-building, most notably the Learning Analytics in Latin America Project, known as LALA, which spans Chile, Brazil, and Colombia. LALA built shared data standards, multi-country training programmes, and institutional dashboards, and reported gains in student retention and faculty data literacy. Yet the review documents a sobering pattern of decay: when external European Union funding concluded, the initiative weakened because sustainability frameworks had never been institutionalised. Analytics governance remained a project-level activity rather than a permanent university function, a phenomenon the review calls the pilot-to-policy gap.

Sub-Saharan Africa, by contrast, has prioritised ethics over algorithmic sophistication. Scholars such as Paul Prinsloo and Rogers Kaliisa have shifted the continental discourse toward epistemic justice, framing data privacy, student consent, and fairness as prerequisites for responsible analytics adoption. Early-warning systems piloted at South African universities have offered proof of concept but remain constrained by infrastructural inequities, low interoperability between learning management systems, and limited staff data fluency. The review argues that African scholarship contributes a normative model with global resonance: analytics as a socio-technical enterprise demanding cultural legitimacy and contextual adaptation rather than mere technical deployment.

South Asia presents perhaps the most paradoxical case. Studies from India and Pakistan report predictive accuracies exceeding 90 percent for grade-point average and dropout forecasting using machine learning models such as Random Forest, XGBoost, and Support Vector Machines. Yet the research overwhelmingly stops at model validation, rarely extending to intervention design or evaluation of learning outcomes. The review coins the term algorithmic myopia to describe this pattern: a research culture that privileges precision metrics, such as precision, recall, and area under the curve, over meaningful educational impact. Without human-in-the-loop interpretation, fairness auditing, or longitudinal evaluation, technically impressive models may identify at-risk students without improving their educational experiences. In one Brazilian dataset, early-warning alerts linked to personalised advising were associated with a 15-percentage-point reduction in dropout relative to the previous cohort, and a Pakistani institution reported roughly an 8-point improvement in first-year retention with human-mediated follow-up, but the review stresses these are single-institution, quasi-experimental estimates without matched control groups, indicators of potential rather than causal proof.

East Asia and the Middle East remain at exploratory stages, with only two eligible studies. China represents a high-capacity, state-coordinated adoption landscape with rapid bibliometric growth in generative AI, while Gulf Cooperation Council countries show early experimentation with adaptive and generative AI tutoring. Both contexts exhibit limited pedagogical evaluation and regulatory ambiguity. Across all regions, five intervention types were identified: predictive early-warning systems, adaptive and personalised learning platforms, generative AI academic support, institutional analytics dashboards, and automated assessment systems. Generative AI tools such as ChatGPT show the fastest growth, but the evidence rests almost entirely on self-reported survey data vulnerable to social desirability bias, with no objective performance verification and no longitudinal or experimental studies of learning impact, equity implications, or academic integrity risks.

Beneath these regional differences, the review identifies structural constraints that recur everywhere. Data infrastructure quality emerges as the primary determinant of feasibility, with chronic deficiencies in internet connectivity, system interoperability, and record completeness undermining algorithmic reliability and adherence to FAIR data principles remaining limited. Governance consistently lags behind adoption: many institutions import policy templates from high-income countries without adaptation, producing what the review terms regulatory dissonance, and few conduct Data Protection Impact Assessments or maintain ethical audit trails. Faculty attitudes range from cautious optimism to scepticism, with analytics frequently perceived as instruments of surveillance. Financial sustainability is similarly fragile, with nearly all regions operating in short-term, donor-funded cycles that produce temporary gains but little institutional learning.

The review is anchored in three theoretical traditions that sharpen its critique. Datafication theory, drawing on Ben Williamson’s work, describes how student behaviours such as attendance and engagement are encoded as predictive inputs, risking a reductionism that compresses the complexity of learning into metrics reflecting institutional priorities. Decolonial epistemology foregrounds data colonialism, the extraction of institutional and student data without reciprocal benefit to local communities, and challenges the uncritical import of AI systems calibrated for Western educational contexts into the Global South. Responsible AI ethics, informed by UNESCO’s human-centred framework, demands transparency, accountability, cultural sensitivity, and student agency, extending to anticipatory governance that identifies unintended consequences before deployment rather than reacting after harm occurs.

From this synthesis, Alenezi proposes a Human-Centred and Contextually Responsive AI Framework integrating responsible AI, equity by design, educator participation, and contextual governance. The practical recommendations are concrete: institutions should establish data governance boards with academic, student, and IT representation before scaling any system; complete impact assessments before deploying predictive analytics; publish model cards documenting training data and known limitations; and conduct annual algorithmic audits accessible to faculty and students. Educator capacity should be developed within pedagogical cycles rather than treated as a technical add-on, and deployment should begin with high-yield, low-risk use cases, with early-warning systems linked to personalised advising in first-year programmes cited as the most empirically supported entry point. Scalability should be assessed across technical, financial, institutional, and pedagogical dimensions before any expansion.

The review’s limitations are candidly acknowledged: only 19 primary studies, a single reviewer, heterogeneous designs that precluded meta-analysis, and language restrictions to English, Spanish, and Portuguese. Yet its central message is unambiguous. The future of AI in emerging higher education will not be determined by algorithms alone but by how effectively institutions combine technological innovation with human expertise, ethical governance, and contextual understanding. As the global community pursues Sustainable Development Goal 4’s promise of inclusive, equitable quality education, the review warns that without equity-by-design principles, fairness evaluation across gender, socioeconomic status, and digital access, and sustained institutional commitment beyond donor cycles, AI risks reinforcing the very inequalities it promises to dissolve. The next generation of research, it argues, must stop asking whether AI can predict educational outcomes and start asking whether it actually improves learning.

Subject of Research: Implementation of human-centred artificial intelligence and learning analytics in higher education institutions in emerging economies

Article Title: Human-centred Artificial Intelligence and Learning Analytics in emerging higher education: a critical scoping review

Article References: Alenezi, A. F. (2026). Human-centred Artificial Intelligence and Learning Analytics in emerging higher education: a critical scoping review. Discover Education, 5(1), Article 1143. https://doi.org/10.1007/s44217-026-02066-9

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02066-9

Keywords: artificial intelligence, learning analytics, higher education, emerging economies, Global South, predictive analytics, educational equity, responsible AI, data governance, algorithmic myopia, generative AI, SDG4

One of the most striking threads in the collection concerns the way human expertise quietly moderates algorithmic decisions in high-stakes environments. Carboni and colleagues examine clinical settings where violence is a real possibility, showing how nurses intervene in and influence algorithmic outputs. The researchers highlight the importance of the doubt that nurses introduce into the decision-making process, treating this cultivated skepticism not as friction to be eliminated but as a vital contribution to collective judgment. In a related vein, Van Voorst documents what is called collaborative tinkering between humans and algorithms in the context of public health, arriving at conclusions that largely echo the clinical analysis. Both studies suggest that the fantasy of fully automated decision-making dissolves under close empirical scrutiny, replaced by a messier but more accurate account of humans and machines reasoning together.

The volume also confronts the hidden human labor on which algorithms depend. Cabitza and colleagues coin the neologism cybork, a fusion of cyborg and work, to emphasize that algorithms frequently rest on human effort. Bennett and colleagues explore the social and power dynamics that arise when humans clean data so that algorithms can operate more efficiently and more correctly. Their argument, aligned with Cabitza and colleagues, is that this labor should not be occluded as if the algorithms were completely autonomous. Instead, algorithmic processing should be understood as a form of collective networking and human-machine collaboration. This reframing carries real political weight: when the workers who make machine learning possible remain invisible, they also remain uncredited, unprotected, and excluded from conversations about how the resulting systems should be governed.

Entanglement, however, runs in both directions. Several essays trace the recursive feedback loops through which algorithms influence human behavior at the same time that humans shape algorithms. Lynch and colleagues demonstrate this dynamic at a microscale, examining how the introduction of robot tour guides into two Nevada museums helps to create the distinctive social spaces that visitors encounter. The robots are not merely props; they actively reorganize how people move, gather, and interact. Kalthoff and Link extend the analysis to the fabrication of humanoid robots designed to create social dynamics through their interactions with humans, citing the pioneering Kismet robot built by Cynthia Breazeal, an early attempt to engineer machines that could engage humans on an emotional register.

At the largest scale, Richard Groß argues that Large Language Models, through their stochastic procedures, are shaping social reality in their own image. A parallel argument has been advanced by Louise Amoore and her colleagues, who focus on the principal technical characteristics of LLMs, such as latency and tokenization, and contend that these systems create a world model that is rapidly propagating through the infrastructures of developed societies. The claim is technically grounded rather than metaphorical. Because LLMs mediate communication, search, and increasingly administrative processes, the statistical regularities embedded in their training and generation procedures can become the regularities of everyday social life, closing a loop in which human output trains the machines that then shape human output.

Beyond empirical case studies, the collection presses for a change in the frameworks, metaphors, and narratives that construct AI-human relations. Harry Halpin urges readers to think in terms of collective intelligence rather than artificial intelligence, a shift that relocates smartness from a single machine to a network of people and tools. Lewis and colleagues point toward indigenous knowledges as demonstrating what they call abundant intelligences that extend throughout the natural world. Hayles singles out this lesson as a general template for how communities can assume control over the kinds of AI that get created, the purposes those systems are designed to fulfill, and the human interests they can support and help to enact. The implication is that the trajectory of AI is not fixed by the technology itself but by the social arrangements that surround it.

Other contributors extend this critical reframing in complementary directions. Bennett and colleagues call for Responsible Artificial Intelligence, in which alignment with human values is treated as the primary design criterion rather than an afterthought. Bory and colleagues look critically at AI narratives themselves, arguing that visions with more modest ambitions, the so-called weak rather than strong versions of AI, hold more potential to lead to futures beneficial to humans. Nugent critiques the pervasive analogy between biological and technological evolution, showing that the analogy works only by suppressing crucial differences that reveal the complexities of biological processes. Together these essays suggest that the stories we tell about AI are not decoration; they actively determine which futures seem possible and which are dismissed before they are tried.

Perhaps the most technically provocative contribution comes from Fabian Offert and colleagues, whose essay on protein folding as predicted by AlphaFold and other Transformer technologies deconstructs the widespread belief that LLMs produce natural language. As the authors show, through tokenization, word embedding, and positional embedding, the linguistic qualities of language are taken apart, reconstructed as numbers in a high-dimensional vector space, and then reconstituted through stochastic procedures that differ radically from how natural language is used and understood by humans. What emerges is not language in any human sense but a new episteme, one that creates and reinforces an intuitive knowledge of sequence. That intuition about sequential structure, they suggest, is precisely what makes these architectures so powerful for predicting how chains of amino acids fold into functional proteins, a problem that had resisted solution for half a century.

The significance of this analysis extends well beyond molecular biology. Hayles notes that it resonates with her own criticism of the naturalness of machine language, and she argues that analyses like those of Offert and colleagues open new possibilities for literary criticism, cultural critique, and critical code studies. If the outputs of LLMs are the products of vector-space arithmetic and probabilistic sampling rather than meaning in the human sense, then humanists have a crucial role in explaining what these systems actually do, what they cannot do, and where their apparent fluency misleads the people who rely on it. The special issue thus positions the humanities not as a skeptical bystander to the AI revolution but as an essential participant in its technical and conceptual development.

The overall message of the collection is both a warning and an invitation. Hayles concludes that readers will find a plethora of diverse approaches, specific demonstrations, and game-changing ideas in these essays, all pointing to the ongoing necessity for critique and analysis by humanists, cultural critics, and others as powerful technologies develop at breakneck speed. We cannot afford to ignore AI, she writes, and the essays help chart paths that allow us to understand these systems, participate in their development, and have a say in how they are and will be used. In an era when headlines oscillate between utopian hype and existential dread, the volume offers a third way: careful, empirical attention to the actual entanglements of humans and machines, and a insistence that the shape of that entanglement remains a collective human choice.

Subject of Research: Human-AI entanglement and the mutual shaping of humans and algorithms in society

Article Title: Entangling humans and AI

Article References: Hayles, N. K. (2026). Entangling humans and AI. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03387-x

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03387-x

Keywords: artificial intelligence, human-AI collaboration, large language models, collective intelligence, algorithmic decision-making, data labor, responsible AI, protein folding, AlphaFold, AI narratives, posthumanism, AI & Society