The research, conducted by Kavali Munisekhar and Bangalore Morarji at Vellore Institute of Technology, develops the Human-Centred Constitutional Urban AI Governance Framework and operationalises it as the Constitutional Smart-City Governance Index, or CSGI. The instrument scores urban AI against Article 21 of the Indian Constitution, human-centred AI principles, and Sustainable Development Goals 11 and 16. Six pillars—constitutional rights, human-centred AI, sustainable development, service delivery, participation, and accountability—comprise thirty-eight indicators with operational definitions, official secondary-data sources, a transparent normalisation-and-weighting model, and a validation strategy. The framework is deliberately positioned at the intersection of urban studies, public administration, law, and AI governance: the Constitution supplies the normative foundation, but the analytical machinery is that of governance research rather than doctrinal legal commentary.

What makes Article 21 an unusually rich constitutional resource for exactly this task is five decades of Supreme Court jurisprudence that have expanded it from a procedural shield into a substantive charter of dignified urban life. Shelter and livelihood were recognised in Olga Tellis v. Bombay Municipal Corporation in 1985, dignity and the bare necessities of life in Francis Coralie Mullin in 1981, a clean environment in Subhash Kumar v. State of Bihar in 1991, and, decisively for the digital era, informational privacy subject to a structured proportionality test in Justice K.S. Puttaswamy v. Union of India in 2017. When an integrated command and control centre fuses surveillance feeds, when a welfare platform authenticates biometric identity before releasing entitlements, or when a predictive model allocates sanitation resources across neighbourhoods, Article 21 is not a peripheral legal technicality; it is the constitutive standard of legitimate state action.

The CSGI was applied to four leading Indian smart cities—Bengaluru, Pune, Surat, and Indore—selected for their AI maturity, domain diversity, and prominence within national urban policy. Bengaluru’s portfolio is dominated by mobility and public safety, including adaptive sensor-driven traffic signal control and an extensive Safe City surveillance programme integrating video analytics with policing command centres. Pune centres on an integrated command centre coordinating traffic, emergency, and utility operations alongside platform-based citizen engagement channels. Surat emphasises hydrological monitoring and flood early warning linked to upstream dam management, while Indore exemplifies the operational-excellence pathway built on comprehensively re-engineered waste management with GPS-tracked collection fleets and monitored processing facilities.

The governance-opacity audit that followed produced the study’s most striking finding. The four cities are 86% to 93% opaque on the transparency and accountability artifacts a rights-based assessment requires. No city publishes a data-protection impact assessment for its AI or surveillance systems, none maintains a public register of deployed algorithms, none has adopted a municipal AI-governance policy, and none subjects its algorithmic systems to independent audit—each of these four artifacts is absent in 100% of the cases examined. Only integrated command centre operational reporting and special purpose vehicle-to-council reporting exist in even rudimentary form, and only partially. Bengaluru, the city that tops the national liveability ranking, is on this measure the most opaque of the four. The finding reframes the study’s data scarcity: the missing values on the rights and accountability pillars are not a gap in the researcher’s dataset but a measurement of a structural condition—Indian urban AI is being deployed faster than the disclosure and audit infrastructure that would make it accountable under Article 21.

The index-divergence analysis then exposed what the researchers call a constitutional blind spot in India’s urban metrics. The CSGI correlates strongly negatively with the Ease of Living Index (Spearman ρ = −0.80) yet positively with the Municipal Performance Index (ρ = +0.80) and the Swachh Survekshan cleanliness survey (ρ = +0.95). Bengaluru crystallises the divergence: first on liveability, it ranks last on municipal performance, last on cleanliness, and last on the CSGI. The Ease of Living Index, in turn, is negatively concordant with every other instrument. When such a metric conditions reputational and increasingly fiscal rewards, it steers cities toward the dimensions it can see and away from the rights and accountability dimensions it cannot. An incentive that is blind to rights and accountability will predictably produce cities that are optimised for visible liveability and hollow on constitutional substance.

A Monte Carlo weight-robustness test addressed the objection that composite index rankings are artefacts of the analyst’s weights. The CSGI was recomputed for 20,000 pillar-weight vectors drawn uniformly from the weight simplex, so that every admissible weighting—including schemes that all but ignore the constitutional pillars—is equally represented. Bengaluru ranks last under 100% of the sampled weightings: there exists no admissible combination of pillar weights on which it escapes the bottom position. Its entire CSGI distribution (90% interval 1.55–2.50) lies below those of the other three cities. The ordering among the top three is genuinely weight-dependent—Surat leads under 45% of weightings, Pune under 40%, and Indore under 15%, with none ever ranking last. The substantive claim the paper rests on is therefore the robust one: whatever normative weight a reader assigns to constitutional rights relative to efficiency, the city that leads the national liveability ranking occupies the lowest position on a rights-inclusive assessment across the entire weight space.

The pillar-by-pillar evidence reveals an inverted governance profile: every city scores highest on the outcome pillars—sustainable development and service delivery—and lowest on the normative and institutional pillars of constitutional rights protection and institutional accountability. Indian urban AI, on this evidence, is operationally capable and constitutionally under-governed. Surat’s flood early-warning and utility analytics constitute a substantive resilience achievement aligned with SDG 11.5 and protective of life in the literal Article 21 sense, while Indore’s waste system delivers measurable SDG 11.6 outcomes. Yet the distributional caveat is systematic: area-based development concentrated smart infrastructure in selected zones, and data coverage of informal settlements lags, so gains are unevenly experienced. Human oversight is strongest where operational stakes enforce it—Surat’s flood early-warning embeds forecasts in an emergency-management chain in which officials interpret model outputs, exemplifying the high-automation/high-control combination that human-centred AI prescribes—but transparency, explainability, and independent oversight are largely absent.

The paper’s central interpretive claim is that constitutional safeguards should be understood as infrastructure for sustainable urban AI, not as external constraint upon it. Doctrinally, Article 21 already contains the evaluative materials—dignity, livelihood, environment, privacy, fair procedure—that AI ethics frameworks attempt to supply from softer sources; anchoring urban AI governance in Article 21 converts persuasive principles into enforceable standards with an existing remedial apparatus. Institutionally, constitutional anchoring supplies what the global AI-principles consensus lacks: a domestic adjudicator, a body of interpretive method in the proportionality test, and legitimacy independent of technology vendors and executive discretion. Instrumentally, the sustainability evidence suggests constitutional compliance and SDG performance are complements, not substitutes: Surat’s life-protective early warning and Indore’s environmental gains are advances in Article 21’s substantive guarantees, and the trust generated by rights-respecting design is itself a resource for the sustained citizen cooperation on which sanitation, resilience, and mobility systems depend.

The study offers tiered recommendations sequenced from disclosure through assessment to independent oversight, designed to be adoptable within existing legal authority. The Government of India should condition central assistance for AI-enabled urban projects on a published Constitutional and Data-Protection Impact Assessment applying the Puttaswamy proportionality test, and task the IndiaAI Mission’s governance apparatus with a national public register of urban algorithmic systems. The Ministry of Housing and Urban Affairs should extend the DataSmart Cities Strategy into a Model Municipal AI Governance Policy with procurement clauses for explainability, bias testing, audit rights, and exit provisions, and rebalance mission-evaluation instruments so rankings reward published impact assessments, deliberative participation, and functioning algorithmic grievance routes alongside output metrics. State governments should notify municipal AI oversight provisions under state municipal acts, including council approval for surveillance-capable deployments. Urban local bodies should institutionalise upstream participation through ward-committee deliberation on proposed deployments before procurement, and extend grievance platforms with an algorithmic-decision category carrying defined timelines and a human-review guarantee. Smart City special purpose vehicles should report AI deployments, incidents, and audit findings to the council on a fixed calendar and publish system inventories in open formats. The Data Protection Board should prioritise urban public-sector deployments and publish reasoned decisions, while the Comptroller and Auditor General should pilot performance-audit methodology for urban algorithmic systems. The researchers acknowledge that the present application is an illustrative pilot rather than a validated measurement—because comparable cross-city data remain sparse, the data-scarcity itself is a substantive finding and a precondition the field must remedy before a fully quantitative CSGI can be validated. But the diagnosis is unambiguous: the right to life is being advanced in outcome and exposed in process, and the exposure is systemic rather than city-specific. Cities that govern AI through their constitutional commitments will not merely avoid harm; they will convert the Constitution’s oldest promise, the protection of life, into the organising purpose of their newest infrastructure.

Subject of Research: AI-enabled smart cities and the constitutional right to life in urban India

Article Title: Can AI-enabled smart cities advance the constitutional right to life? A rights-based index, governance-opacity audit, and robustness analysis for sustainable urban India

Article References: Munisekhar, K., & Morarji, B. (2026). Can AI-enabled smart cities advance the constitutional right to life? A rights-based index, governance-opacity audit, and robustness analysis for sustainable urban India. Frontiers in Sustainable Cities, 8, Article 1959012. https://doi.org/10.3389/frsc.2026.1959012

Image Credits: AI Generated

DOI: 10.3389/frsc.2026.1959012

Keywords: artificial intelligence, smart cities, constitutional governance, human-centred AI, urban India, SDG 11, digital governance, AI-enabled, smart, cities, advance, constitutional

The timing of the proposal is significant. Higher education systems worldwide are straining under rising application volumes, constrained resources, and intensifying expectations around widening participation. In the United Kingdom alone, the 2023 UCAS cycle involved more than 750,000 applicants and nearly three million applications. Admissions systems designed for smaller applicant pools and standardised qualifications are buckling under the load, and AI promises scalability. Yet Alenezi’s review of the literature reveals a troubling pattern: most AI-enabled admissions scholarship evaluates systems purely on prediction accuracy, enrolment optimisation, or processing efficiency, while paying scant attention to learner agency, transparency, or educational equity.

The risks are not hypothetical. The paper documents how algorithmic systems trained on historical data can encode and perpetuate existing inequalities. Amazon’s experimental CV-screening tool systematically downgraded applications from women because it had learned from historically male-dominated hiring patterns. The COMPAS recidivism tool demonstrated racially disparate predictions. And the UK’s 2020 A-level algorithm depressed grades for students from lower-performing schools, sparking a national controversy. In admissions, the danger takes specific technical forms: representation bias when training datasets under-represent particular groups, measurement bias when proxy variables like postcode or school type capture group membership rather than merit, and feedback bias when predictions shape institutional actions that then create self-fulfilling cycles.

HCPAF responds with an architecture of five interconnected dimensions: learner profiling and contextual understanding; explainable AI decision support; equity and inclusion auditing; human oversight and shared decision-making; and personalised transition and success support. Crucially, the framework treats these dimensions as recursive rather than sequential. Profiling choices shape what can be explained and audited; fairness findings may force revisions to model features; human review can trigger model correction; and transition outcomes feed back into assumptions made at the point of admission. The result is a system of mutual constraints rather than a frictionless pipeline.

Technically, the framework leans on established explainable AI techniques such as LIME and SHAP, which generate interpretable accounts of how input variables contribute to model outputs. But Alenezi, drawing on the work of Khosravi and colleagues, insists that explainability must be calibrated to its audience. An explanation intelligible to a data scientist may be opaque to an admissions officer or an applicant, so the framework calls for layered explanation architectures serving multiple stakeholder groups simultaneously. All AI-generated recommendations must be accompanied by plain-language explanations, consistent with GDPR Article 22 rights and the EU AI Act’s requirements for high-risk systems in education.

The framework is equally candid about trade-offs that much of the AI-in-education literature glosses over. Richer contextual data can support fairer decisions, but collecting more socioeconomic, demographic, and behavioural information increases privacy risk. Postcode-level measures such as POLAR4 quintiles can identify structural disadvantage, yet the same variables may act as proxies for ethnicity, class, or disability — and removing them entirely can make disadvantage statistically invisible. HCPAF’s answer is neither uncritical inclusion nor blanket exclusion, but counterfactual and subgroup sensitivity testing, with contextual variables used preferentially to expand opportunity rather than deny access. Similarly, the framework acknowledges that demographic parity, equalised odds, and individual fairness cannot always be satisfied simultaneously, and that carrying admissions data into student-support systems may enable earlier help while creating risks of labelling and surveillance.

One of the study’s most distinctive conceptual contributions is its treatment of trust. Rather than assuming that transparency automatically builds confidence in AI systems, Alenezi frames trust as a relational, calibrated, and empirically contingent construct. More information does not necessarily generate more trust: disclosure of uncertainty, data limitations, or unequal error rates may appropriately reduce confidence. The relevant objective is calibrated trust, in which applicants and staff understand what the AI can and cannot do, can challenge its outputs, and have credible evidence that errors and inequities will be detected and remedied. Explainability, fairness auditing, and human oversight may reinforce one another — or they may reveal justified grounds for distrust.

Generative AI occupies a carefully bounded role within the framework. While predictive machine learning supports profiling and fairness monitoring, large language models are positioned mainly as an interaction and explanation layer: translating model outputs into applicant-accessible language, providing source-grounded conversational guidance about programmes and procedures, and personalising pre-arrival information. Recognising that LLMs can hallucinate, reproduce bias, and generate confident but incorrect advice, HCPAF requires retrieval from authoritative institutional sources, logging and audit of outputs, clear disclosure that applicants are interacting with AI, and escalation to human advisers wherever financial, disability, visa, safeguarding, or admissions-condition issues arise. Notably, the framework treats AI-assisted personal statements as a governance problem rather than an automatic integrity violation, urging institutions to avoid unreliable AI-detection tools and to ensure applicants are not disadvantaged by unequal access to language models.

The theoretical scaffolding is deliberately broad, integrating six traditions: Shneiderman’s human-centred AI, responsible AI and algorithmic governance, Sen’s Capability Approach, educational equity theory, Value-Sensitive Design, and human–AI collaboration theory. The Capability Approach in particular reframes the evaluation question: admissions systems should be judged not only by whether they predict academic success accurately, but by whether they expand or constrain the substantive freedoms of diverse learner populations to access and flourish within higher education. The framework also identifies normative alignments with Sustainable Development Goals 4, 10, and 16, though Alenezi is careful to stress that these are alignments of principle, not evidence of impact.

The author is refreshingly explicit about the framework’s limits. HCPAF is a conceptual synthesis, not an empirically validated admissions model, and prospective evidence on live AI-assisted admissions remains thin. The paper’s illustrative scenarios — a post-92 university using contextualised offer-making with monthly equity audits, a research-intensive university deploying a generative AI advising portal with human review of consequential guidance, and a specialist provider connecting admissions profiles to early-warning retention systems — are explicitly hypothetical design propositions rather than documented deployments. Until validation evidence accumulates through case studies, participatory co-design, and longitudinal evaluation, HCPAF should be used as a governance heuristic for interrogating AI-enabled admissions, not as proof that such systems will improve equity or trust. That honesty may prove to be the framework’s most important feature: at a moment when institutions face intense pressure to adopt AI, it offers a roadmap for doing so on educational rather than purely institutional terms.

Subject of Research: A human-centred AI and learning analytics governance framework for equitable personalised higher education admissions

Article Title: A human-centred artificial intelligence and learning analytics framework for equitable personalised higher education enrolment

Article References: Alenezi, A. F. (2026). A human-centred artificial intelligence and learning analytics framework for equitable personalised higher education enrolment. Discover Education, 5(1), Article 1084. https://doi.org/10.1007/s44217-026-02221-2

Image Credits: AI Generated

DOI: 10.1007/s44217-026-02221-2

Keywords: human-centred AI, learning analytics, higher education admissions, educational equity, explainable AI, generative AI, learner agency, algorithmic bias, responsible AI governance, personalised learning, widening participation, student success

The vision is not a vague ecological metaphor. Armstrong and Moodley ground their framework in technologies that have already been validated in laboratories and pilot projects, including bioelectrochemical systems such as microbial fuel cells, which generate electricity directly from the metabolic activity of bacteria breaking down organic waste. The idea of architecture that behaves like a living organism has deep roots, from the Japanese Metabolist movement of the 1960s to Gordon Pask’s electrochemical machines and the cybernetic experiments of Nicholas Negroponte. What is new in the CPLS proposal is the synthesis of that biological ambition with modern artificial intelligence, and specifically with the semantic technologies that allow machines to reason over knowledge rather than simply react to sensor data.

At the heart of the framework is a tripartite symbiosis, an ecosystem of three classes of actors. The first is a metabolic ‘gut’: integrated bioreactors embedded in the fabric of the building that digest waste streams, transform them into energy and useful materials, and give the house a functioning physiology. The second is a cognitive ‘brain’: a cognitive digital twin, a computational model of the home that does not merely mirror its physical state but can explain its own reasoning, learn from experience, and coordinate the building’s biological and technical subsystems. The third, and crucially not the least, is the human occupant, repositioned from a passive consumer of automated services into a literate steward who participates in and understands the home’s living processes.

The technical linchpin that makes this symbiosis computable is a proposed hybrid ontology. In computer science, an ontology is a formal, machine-readable specification of the concepts in a domain and the relationships between them. Existing building ontologies, such as the W3C’s BOT building topology ontology, the SSN/SOSA sensor ontologies and the ETSI SAREF family of smart-application standards, describe buildings in terms of spaces, devices, sensors and observations. They are excellent at answering questions like which room a temperature reading came from, but they cannot represent the dynamic, goal-directed processes of a living metabolism: the fact that a bioreactor is digesting waste, that its microbial community is adapting, or that a design pattern borrowed from biology is governing how the system should respond to change.

The hybrid ontology bridges that gap by translating architectural metabolic design patterns, codified biological design principles drawn from the pattern-language tradition of Christopher Alexander, into a machine-readable format that AI systems can reason over. This means the home’s intelligence is not limited to monitoring data streams. Instead, the AI can interpret the state of the building’s metabolism in terms of the design intentions encoded in the ontology, adapt to dynamic biological processes that no training dataset could fully anticipate, and explain its decisions in terms that both engineers and occupants can understand. Explainability, the authors stress, is not an optional add-on but a structural requirement: because the reasoning is grounded in a shared semantic framework, the cognitive digital twin can show its work, tracing any recommendation back through the metabolic concepts and patterns that motivated it.

This approach draws on the emerging field of hybrid intelligence, which seeks to combine the complementary strengths of humans and machines rather than replacing one with the other. In the CPLS, the human is not a user issuing commands to an opaque assistant but a participant in a feedback loop that spans biology, computation and lived experience. The authors situate this within a lineage of cybernetic thinking that runs from Norbert Wiener’s control and communication theory through W. Ross Ashby’s homeostat to Gordon Pask’s conversation theory, all of which treated adaptation and dialogue between system and environment as the essence of intelligence. The home, in this view, is not a tool but a conversational partner with its own agency, what Armstrong has elsewhere described as persuadable matter.

The biological substrate is equally concrete. Microbial fuel cells, first observed by M. C. Potter in 1911 and developed intensively over the past two decades, convert the chemical energy of organic decomposition into electricity through the action of electroactive bacteria such as Shewanella oneidensis. Research teams, including collaborations involving Armstrong and ioannis Ieropoulos, have demonstrated microbial fuel cells powering lights and digital interfaces from urine and wastewater, with projects such as PEE POWER in Uganda showing real-world sanitation and energy benefits. The Living Architecture (LIAR) project built prototype metabolically engineered building units that process greywater and generate resources. The CPLS proposal scales this logic to the whole house: the gut is not a single device but a distributed digestive system woven through the architecture, managing carbon, nitrogen, water and energy flows in closed loops.

The societal implications the authors draw from this are as significant as the technology. They propose redefining comfort, the holy grail of conventional smart home design, as metabolic security: not a constant temperature and a well-stocked refrigerator, but the assurance that the household’s living systems are resilient, regenerative and productive. A metabolising home would not just reduce its environmental footprint; it would actively contribute to the household’s material economy, transforming waste into energy and nutrients, and repositioning the dwelling as a regenerative asset rather than a sink for embodied carbon and operational energy. This matters because the built environment is a major contributor to the climate crisis, both through operational emissions and through the embodied carbon of construction materials, a problem initiatives such as the European Green Deal and the World Green Building Council’s embodied carbon agenda have made impossible to ignore.

Equally transformative is the role the framework assigns to occupants. In today’s smart home, the human is largely a data source: activity recognition systems, graph neural networks and predictive energy optimisers infer what people are doing so machines can act on their behalf. The CPLS inverts this relationship. Because the ontology makes the home’s metabolic processes legible, occupants can learn to read them, to understand what their bioreactors are doing, how their energy and nutrient cycles are flowing, and where their stewardship matters. The authors describe this as transforming occupants into literate stewards, a form of domestic ecological literacy that echoes educational research on hybrid intelligence, where AI systems are designed to extend human learning rather than substitute for it. The home becomes, in effect, an instrument of ongoing education about the living world.

None of this is presented as a finished product. The paper is explicitly a conceptual contribution, a proposed foundation rather than a deployed system, and the authors are careful to note that no datasets were generated or analysed in the study itself. Its original contribution lies in the synthesis: metabolic architecture, cybernetics, semantic web technologies and human-centred AI are brought together into a single coherent framework with a methodology for hybrid human-AI systems. But the individual components are real, from validated microbial fuel cell technology to mature ontological standards and cognitive digital twin research in domains from healthcare to energy management. The wager of the paper is that the future of sustainable dwelling may lie not in smarter gadgets but in cultivating living architectural metabolisms, homes that participate in the regenerative logic of the living world rather than standing apart from it. If that wager pays off, the smart home of the coming decades may look less like a remote control with a roof and more like something closer to a body: fed by waste, warmed by microbes, guided by an explainable mind, and inhabited by people who finally understand that they are not the owners of a machine but partners in a metabolism.

Subject of Research: A hybrid ontology linking metabolic architecture and human-centred AI for regenerative smart homes

Article Title: A hybrid ontology for metabolic architecture and human-centred AI: an explainable new vision for the home

Article References: Armstrong, R., & Moodley, D. (2026). A hybrid ontology for metabolic architecture and human-centred AI: an explainable new vision for the home. AI & SOCIETY. https://doi.org/10.1007/s00146-026-03301-5

Image Credits: AI Generated

DOI: 10.1007/s00146-026-03301-5

Keywords: metabolic architecture, hybrid ontology, human-centred AI, Cyberphysical Living System, cognitive digital twin, regenerative design, smart homes, microbial fuel cells, semantic web, explainable AI, sustainability, built environment