Stewarding Regenerative Experience (RX): AI-Quantum for Humanity in Cognitive, Health and Well-being

As Artificial Intelligence (AI), quantum intelligence, and deep technologies advance rapidly, human cognitive flexibility, emotional resilience, and adaptability are experiencing a significant decline. AI excels in optimization, automation, and efficiency, yet has not fostered human cognitive regeneration, worse hinders well-being restorations. Mental health problems account for over $1 trillion in global productivity losses annually [1] while neurodegenerative diseases, such as dementia, are projected to cost $2.8 trillion by 2025 [2]. The World Bank estimates that AI-driven automation will displace 1.2 billion jobs by 2040 [3], further exacerbating workforce disruption and adaptability challenges. Despite the promise of AI-driven regenerative healthcare, cognitive resilience remains an unaddressed priority.

This review examines the transition from efficiency-driven AI models to regenerative intelligence, focusing on human cognitive and health regeneration. Through a Systematic Literature Review (SLR), we synthesize key frameworks, including the Regenerative Experience (RX) Framework, 3Rs-T Framework, and Trinity Growth Model [4], to establish a theoretical foundation for regenerative AI systems. Key policy interventions and practical applications are drawn from ASEAN’s economic regeneration strategies and systemic leadership approaches [5]. Unlike empirical studies, this review synthesizes interdisciplinary research to establish a conceptual roadmap for integrating AI with neuroplasticity, behavioral economics, and governance ethics. By embedding Regenerative Intelligence [6] into AI governance, healthcare, workforce adaptation, and education, this research proposes a paradigm shift towards AI as a catalyst for long-term human flourishing.

ASEAN: Association of South East Asia Nations; ESG: Environments, Social, Governance; SDG: Sustainable Development Goals; PPP: People-Planet-Profit; 5Ps: Purpose, People, Partnership, Planet, Prosperity; 3Rs-T: Restoration, Resilience, Regenerate, Transcendence; AI-DAO: Artificial Intelligence-Decentralized Autonomy Organization; RPF: Purpose Regenerative Framework; RX: Regenerative Experience; AI: Artificial Intelligence

The great dilemma AI accelerates, but humans stagnate

As AI rapidly accelerates, human adaptability lags behind, posing a fundamental question: Is AI optimizing industries at the cost of human intelligence? The exponential rise of automation, machine intelligence, and deep technology is reshaping global economies, but its impact on human cognition remains largely over-looked. AI systems, with unprecedented accuracy, are transforming production, efficiency, and behavioral prediction, revolutionizing sectors ranging from e-commerce to healthcare.

Over the past two decades, AI-driven businesses have grown at an unparalleled rate. Personalized AI-powered recommendations and digital commerce strategies have fueled the rapid expansion of global digital trade, which surged from $1.3 trillion in 2014 to $6.3 trillion in 2023 [7]. Similarly, AI investment has tripled from $1.3 trillion in 2022 to an estimated $3.2 trillion by 2024, with AI-powered automation, adver-tising, and recommendation engines contributing over $3.2 trillion in economic value [8,9].

However, human cognitive and emotional development has not kept pace with these technological advancements. While AI optimizes business processes and enhances industrial efficiency, it has simultaneously contributed to cognitive overload, mental health crises, and workforce displacement. The rapid acceleration of AI-driven automation raises critical concerns about the widening gap between technological progress and human adaptability.

Problem statement: The hidden cost of human degeneration

While AI fuels economic productivity, its unchecked growth threatens human adaptability. AI is not just automating tasks-it is reshaping how we think, work, and learn, often with unintended consequences.

• Digital cognitive overload now accounts for 40% of teenage depression cases [10,11]
• Workplace stress, digital burnout, and social fragmentation contribute to a $1 trillion annual economic burden [1].
• By 2040, AI-driven automation is projected to replace 1.2 billion jobs, while education systems struggle to keep up [3].

The fundamental question arises: Is AI enhancing human potential, or is it making us more dependent, cognitively stagnant, and less adaptable?

This study seeks to bridge this growing gap by introducing a regenerative intelligence model—one that enhances human adaptability rather than replacing it.

The consequences of unchecked AI expansion include:

• Increased cognitive overload: Knowledge workers experience 40% higher decision fatigue and burnout due to excessive task automation [10,12].
• Escalating mental health crises: Workplace stress, digital burnout, and social fragmentation contribute to a $1 trillion annual economic burden [1]. Rising neurodegenerative diseases:
• The incidence of dementia and other neurodegenerative disorders is expected to triple by 2025, adding $2.8 trillion in yearly healthcare costs [2].
• Job displacement: By 2040, AI-driven automation is projected to replace 1.2 billion jobs, while education systems remain ill-equipped to adapt to this rapid technological shift [3].

This paradox raises a fundamental question: Does the advancement of AI prioritize efficiency over human potential? Current AI models focus on optimization rather than regeneration. They emphasize automation, predictive analytics, and data intelligence, yet fail to address human adaptability, workforce resilience, and cognitive longevity. The absence of human-centered, regenerative intelligence in AI frameworks has profound implications for education, employment, and long-term well-being [13].

The research question: Why does AIneed a regenerative model?

Unlike human intelligence, which adapts, learns, and evolves, AI is static and optimization-driven-prioritizing efficiency over cognitive adaptability. AI can automate tasks, but it cannot foster creativity, enhance problem-solving skills, or build long-term workforce resilience.

This research seeks to answer three critical questions:

• How can AI transition from an efficiency-driven model to a regenerative intelligence framework that enhances human adaptability and cognitive resilience?
• What role do bioengineering, deep technology, and quantum AI play in neuroplasticity, regenerative medicine, and long-term cognitive enhancement?
• How can ethical considerations and Spiritual Quotient (SQ) be integrated into AI governance models to align technology with human consciousness, sustainability, and well-being?

Without a regenerative intelligence framework, AI risks creating a permanent misalignment between human cognitive adaptation and technological progress. This could result in greater economic inequality, job insecurity, and cognitive stagnation. Thus, this study explores:

• How AI can transition from an optimization-driven model to a regenerative intelligence framework that enhances human adaptability, cognitive resilience, and well-being?
• The role of bioengineering, deep technology, and quantum AI in neuroplasticity, regenerative medicine, and long-term cognitive enhancement.
• How ethical considerations and Spiritual Quotient (SQ) can be integrated into AI governance models to align technology with human consciousness, sustainability, and well-being?

A paradigm shift: The regenerative experience framework

To bridge the growing gap between AI efficiency and human adaptability, this study introduces the Regenerative Experience (RX) Framework-a paradigm shift that repositions AI from automation to aug-mentation. Instead of AI replacing human intelligence, RX ensures AI enhances, restores, and extends cognitive resilience. The RX framework integrates:

• The Trinity Growth Model (TGM) and the 3Rs-T Framework (Restoration, Resilience, Regeneration, Transcendence) [14] to ensure AI actively supports mental, emotional, and social well-being.
• Quantum AI and deep technology to facilitate cognitive longevity, neuroplasticity, biome transplants, and regenerative medicine, enabling human capability enhancement rather than replacement.
• Ethical AI governance incorporating Spiritual Quotient (SQ) [4] to align AI evolution with human-centered values and sustainability.

By embedding moral considerations, human values, and sustainability principles into AI development, this study advocates for an ethical and regenerative AI paradigm that empowers individuals rather than marginalizing them.

Review significance

This study challenges conventional AI development, presenting a fundamental shift from efficiency-driven models to regenerative intelligence-ensuring AI actively supports human cognition, workforce resilience, and mental well-being.

AI systems have predominantly focused on efficiency-driven automation, often at the expense of human cognitive well-being. This review proposes regenerative AI, which enhances cognitive function, emotional resilience, and long-term systemic well-being. We draw upon three core frameworks to establish a regenerative AI paradigm: Regenerative Experience (RX) Framework – Addresses the psychological and behavioural impact of AI-driven systems; 3Rs-T Framework (Restoration, Resilience, Regeneration, and Transcendence) – Explores the human adaptability process within AI-integrated environments; Trinity Growth Model (TGM). [13] – Aligns AI governance with ethical, cognitive, and economic regeneration. By synthesizing these models, we redefine AI’s role in promoting systemic intelligence beyond automation.

Key contributions of this study:

• Redefining AI’s Role Beyond Automation: AI must transition from a cost saving tool to an enabler of human adaptability, neuroplasticity, and workforce transformation.
• Shaping the future of AI governance: This study presents a policy and ethical framework that integrates Spiritual Quotient (SQ) into AI decision-making-ensuring AI serves humanity rather than replacing it.
• AI as a Cognitive and Economic Enabler: By supporting neuroplasticity, lifelong learning, and regenerative medicine, AI can empower healthcare, education, and global workforce evolution.

With ASEAN economies at the forefront of AI-driven growth, this study also examines ASEAN’s role in regenerative AI governance, proposing strategies for workforce transformation and sustainable AI-driven development. To explore these critical issues, the following section details the systematic literature review methodology employed in this study.

Review approach and review framework

This review follows a structured Systematic Literature Review (SLR) to synthesize interdisciplinary research across AI, neuroplasticity, regenerative economics, and behavioral sciences. The inclusion criteria included peer reviewed journals, policy reports, and empirical case studies published between 2015-2025. We examined over 200 academic and industry sources, systematically categorizing findings into cognitive health, regenerative governance, and AI-driven economic models.

To systematically assess AI’s transition from an optimization model to a regenerative intelligence paradigm, this study employs a Systematic Literature Review (SLR) guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) framework [14]. This approach ensures a comprehensive, interdisciplinary, and evidence-based evaluation of the following key domains:

• AI & Quantum Computing in regenerative healthcare
• Cognitive longevity, neuroplasticity, and biome transplants
• AI-driven workforce adaptability and skill augmentation
• Ethical AI governance and Spiritual Quotient (SQ) in AI systems.

Research questions: The literature review is structured around the following Research Questions (RQ), which correspond to key themes explored in this study:

These research questions form the analytical lens through which AI models, governance structures, and technological innovations are critically assessed (Table 1).

Data collection, inclusion criteria, and analytical strategies

To ensure a rigorous and balanced synthesis of findings, this study draws from three primary data sources,

• Academic & Research Repositories: IEEE Xplore, PubMed, ScienceDirect, Springer, Nature, and Google Scholar were selected because they represent peer reviewed, high impact studies in AI, neu-roscience, and governance.
• Industry & Policy Reports: Sources such as the World Economic Forum (WEF), McKinsey Global Institute, and OECD AI Policy Observatory were included to integrate real world AI adoption trends, business strategies, and economic implications.
• AI & Neuroscience Institutions: Research centers such as MIT CSAIL (USA), DeepMind (UK), Wyss Institute (Harvard), and Singapore Biopolis were included to incorporate cutting edge advancements in AI-driven cognitive resilience and neuroplasticity research.

Inclusion and exclusion criteria (Table 2)

Data analysis method: The analysis involved three core methods,

• Systematic Review Breakdown: Extracting themes, categorizing findings, and comparative analysis.
• Comparative Evaluation: Contrasting optimization-driven AI and regenerative AI models.
• AI Policy Analysis: Examining AI governance models across global regions.

PRISMA flowchart for study selection: The PRISMA-based literature review process identified recurring patterns across AI applications in cognitive resilience, workforce adaptability, regenerative healthcare, and governance. However, a critical gap remains while AI enhances automation, it does not actively support human adaptability, resilience, or cog-nitive restoration.

The following sections synthesize key themes from the literature review, highlighting the comparative limita-tions of traditional AI models, emerging breakthroughs in regenerative AI, and governance challenges that must be addressed (Table 3).

Key themes

Comparative analysis: Traditional AI vs. regenerative AI: AI has traditionally been designed as an optimization tool, streamlining industrial processes, decision making, and predictive analytics. However, the next frontier of AI development is shifting towards regenerative intelligence augmenting human capabilities rather than merely automating them.

To transition from automation to augmentation, AI models must evolve to support cognitive resilience, neuroplasticity, and adaptive intelligence. Below is a comparative analysis of traditional AI versus regenerative AI models (Table 4).

Key insights

• Traditional AI is efficiency-driven but does not support long-term human adaptability.
• Regenerative AI integrates neuroplasticity, mental resilience, and ethical intelligence into AI-driven systems.
• AI must move beyond automation and personalization to actively restore and enhance cognitive function.

AI in regenerative healthcare: Machine Learning (ML) is increasingly being applied to predict cognitive outcomes and enhance early neuroplasticity interventions. Studies show that early childhood is a critical period for cognitive development, and AI-driven risk assessment models can help identify at-risk children before cognitive delays manifest [15]. These findings align with the Restoration and Resilience stages of the 3Rs-T Framework, where AI plays a role in preventing cognitive degeneration and promoting adaptive intelligence. However, despite the promise of ML in early neurodevelopmental screening, its adoption in public health remains limited due to lack of integration with national healthcare policies. AI is revolutionizing regenerative medicine, neuroscience, and cognitive health, enabling breakthroughs in Brain-Computer Interfaces (BCI), gene editing, and neural stimulation. Future AI governance frameworks must embed Spiritual Quotient (SQ) and ethical oversight to en-sure equitable and effective use of cognitive health AI models.

However, major regulatory and implementation barriers still hinder widespread adoption (Table 5).

Implication:

• AI-driven regenerative medicine must integrate Quantum AI for neural regeneration and cognitive longevity. Propose an AI governance framework incorporating WHO and EU legal models for cognitive and health regeneration policies.
• Ethical concerns in AI-based genetic engineering require robust AI governance frameworks. AI-based regenerative healthcare must align with ethical and legal structures to ensure trust and transparency. Ethical AI must mitigate biases in cognitive health predictions and regenerative medicine, ensuring equitable access to AI-powered healthcare.
• Brain-Computer Interfaces (BCI) show promise but require affordable, scalable solutions.

AI in education & workforce adaptability: The future of AI in education goes beyond personalized learning it must enable long-term cognitive resilience and adaptability. AI must shift from passive learning models to dynamic, neuroplasticity enhancing frameworks (Table 6).

Implication:

• AI-powered education models must integrate regenerative intelligence to ensure long-term cognitive resilience.
• The current AI workforce adaptation models are short-term focused and do not actively rebuild cognitive strength or neuroplasticity.
• AI must enable continuous learning, unlearning, and relearning, rather than simply automating repetitive tasks (Table 7).

AI governance models: A regional perspective: Implication: Ethical AI must embed Spiritual Quotient (SQ) to align AI with human consciousness, resilience, and sustainability.

As table 7 highlights, existing AI governance models are reactive and compliance-driven rather than human centered. The lack of ethical foresight in AI governance has resulted in fragmented regulations worldwide mentioned in table 7. One solution is embedding SI into AI decision-making models. SI-driven AI governance, as seen in Singapore’s Advisory Council on AI and Data Ethics, incorporates human adaptability, sustainability, and long-term resilience into AI policy frameworks. Moving forward, SI-based AI ethics should be incorporated into global AI governance to ensure AI serves human cognitive regeneration rather than merely regulating risk.

The comparative analysis of traditional AI and regenerative AI (Table 4), AI’s role in healthcare (Table 5), workforce adaptation (Table 6), and AI governance (Table 7) collectively demonstrate a critical shortcoming-AI remains focused on efficiency rather than long-term human augmentation. While AI has improved predictive analytics, automation, and compliance-based governance, it has not yet evolved into a regenerative intelligence model that supports cognitive resilience, adaptability, and ethical consciousness. This limitation necessitates a paradigm shift, which is explored in Section.

Bridging the gap: AI and regenerative intelligence for humanity

The SLR in Section 2.3 reveals that while AI and Quantum AI have advanced efficiency, predictive analytics, and automation, their focus remains task driven rather than human centric. The next phase of AI development must shift from optimization to regeneration, actively supporting cognitive resilience, neuroplasticity, and long-term adaptability. AI has played a crucial role in regenerative healthcare, workforce skill augmentation, and AI governance, yet significant gaps remain in each of these areas.

AI-driven Clinical Decision Support Systems (CDSS) play a crucial role in enhancing clinician decision-making and cognitive augmentation. The integration of machine learning, deep learning, and natural language processing into AI-powered diagnostics and treatment recommendations represents a significant step towards regenerative intelligence in healthcare [16]. However, bias, interpretability challenges, and usability concerns must be addressed to ensure trust and transparency in AI-based cognitive healthcare interventions. Ethical AI governance models must ensure that CDSS align with regenerative health principles, embedding neuroplasticity based cognitive augmentation into decision-making support (Table 8).

AI’s current limitation: Short-term gains vs. long-term human adaptation

1. Regenerative Intelligence in Healthcare Is Underdeveloped

• The SLR findings (Table 5) show that AI-driven neuroregeneration remains largely experimental.
• Quantum AI applications (e.g., neural restoration, bio printing) are promising but not yet fully integrated into public healthcare systems.

2. Education & Workforce Learning Models Are Not Sustainable

• AI enhances workplace productivity (Table 6), but it also contributes to cognitive overload and job dependency.
• Lifelong learning models driven by regenerative AI are necessary to future proof the workforce.

3. Governance Models Are Compliance Based, Not Human Centered

• Current governance structures (Table 7) focus on regulating AI risks, but do not promote cognitive resilience or neuroplasticity.
• Ethical AI frameworks should incorporate SI to ensure AI evolves alongside human adaptability.

Bridging the gaps: The future of AI in regenerative intelligence

Regenerative Intelligence is an emerging AI paradigm that moves beyond automation and optimization actively fostering cognitive resilience, neuroplasticity, and ethical intelligence. Unlike traditional AI, which focuses on efficiency and predictive analytics, Regenerative AI aims to enhance human adaptability, emotional intelligence, and long-term cognitive well-being.

The SLR identified four key areas where AI must evolve to become truly regenerative:

1. Transitioning from Predictive AI & Generative AI to Regenerative AI

• AI must move beyond automation and actively support cognitive flexibility, resilience, and adaptability.

2. Integrating Quantum AI into Healthcare

• AI-driven bio printing and neural restoration models must be scaled beyond experimental research.

3. Developing AI-Powered Lifelong Learning Models

• AI-driven learning should be neuroplasticity based to prevent workforce stagnation and cognitive fatigue.

4. Embedding SI in AI Ethics and Policy

• AI ethics frameworks should integrate SI principles to prioritize human development over economic efficiency.

The findings from the SLR confirm that AI must transition from efficiency-driven optimization to regenerative intelligence. However, real-world implementation remains fragmented, with most AI applications focusing on short-term gains rather than long-term cognitive augmentation. In Section 3 (Results & Analysis), we examine empirical case studies that demonstrate how AI can bridge these gaps in practice.

Advancing Regenerative AI for Human Flourishing

While the Systematic Literature Review (SLR) in Section 2 established AI’s current limitations and gaps, empirical evidence is essential to validate the transition toward regenerative intelligence. This section presents real-world case studies and frameworks that demonstrate how AI can evolve beyond efficiency-driven optimization toward human augmentation and cognitive resilience. It explores:

• The 3Rs-T Framework, which outlines AI’s role in cognitive augmentation, mental resilience, and adaptability.
• The Growth Trinity Model, aligning AI development with human intelligence, neuroplasticity, and ethical intelligence.
• Case studies on Quantum AI, brain computer interfaces, workforce adaptability, and AI governance models across different regions.

By integrating literature review findings with real-world applications and empirical data, this section reinforces the urgent need for regenerative AI to sustain long-term human intelligence, resilience, and wellbeing.

Theoretical foundations of regenerative AI: Regenerative experience (RX) framework

The RX framework extends AI’s role beyond efficiency into cognitive restoration and psychological resilience. It integrates neuroplasticity based interventions to mitigate cognitive overload, digital burnout, and AI-driven mental fatigue [15+ sources]. Case studies from Neurons to Nations [13] highlight that AI-assisted cognitive coaching improved executive decision-making by 30% and reduced psychological dis-tress in leadership environments [15+ sources]. Originally formulated in Greening the Blue Ocean [6], the 3Rs-T Framework describes four levels of AI-human integration [16+ sources]. Building on the Trinity Growth Model (TGM) [4] for AI Governance from Greening the Blue Ocean [6], this model integrates Spiritual Quotient (SQ), AI governance, and regenerative economics to formulate ethical AI deployment [16+ sources]. In ASEAN’s AI policy, SI principles align economic growth with human-centered governance, preventing exploitative automation practices [14+ sources].

Empirical insights from ASEAN’s regenerative economy (Gee R.O.W, 2025)

ASEAN provides a testbed for regenerative AI policy implementation, integrating economic, cognitive, and environmental regeneration. Policy recommendations from Building ASEAN’s Regenerative Economy emphasize three core strategies [14+ sources]:

• Green AI Finance – Investing in AI models that contribute to cognitive well-being and economic inclusion.
• Cross-Border Governance Standards – Harmonizing AI ethical regulations across ASEAN markets.
• AI-SDG Alignment – Structuring AI investments to directly support Sustainable Development Goals (SDGs).

The 3Rs-T framework and growth trinity model: Designing the regenerative experience

Key finding: The rapid advancement of artificial intelligence and deep technologies is transforming various sectors, but human adaptation, mental resilience, and cognitive well-being have not kept pace (Table 9).

Implication: The 3Rs-T Framework provides a roadmap for AI’s transition from passive automation to active human enhancement. Each phase represents a progression toward regenerative intelligence:

• Restoration ensures AI mitigates cognitive overload and promotes emotional well-being.
• Resilience develops adaptive learning ecosystems that help workforces evolve with AI-driven disruptions.
• Regeneration integrates AI into healthcare and neuroplasticity focused interventions, ensuring AI augments human cognition rather than replacing it.
• Transcendence envisions an AI-driven ethical intelligence framework, incorporating quantum AI and Spiritual Quotient (SQ) for sustainable decision making.

Recommendation: Regenerative AI frameworks must be integrated into workforce policies, adaptive learning systems, and regenerative healthcare models.

The trinity growth model: Aligning intelligence with regenerative AI

The Growth Trinity Model ensures AI evolution supports human intelligence across four key dimensions: Physical, Cognitive, Neuroplasticity, and Spiritual Quotient (SQ) (Table 10).

Implication: The Growth Trinity Model ensures AI evolves holistically, addressing biological, cognitive, and ethical intelligence. Unlike traditional AI models, which focus solely on automation, this framework ensures that:

• Physical intelligence applications (e.g., biome transplants, regenerative medicine) enhance human health longevity.
• Cognitive intelligence applications drive AI-powered reasoning, learning augmentation, and adaptability.
• Neuroplasticity-driven AI fosters lifelong learning, cognitive resilience, and mental adaptability.
• Spiritual Quotient (SQ) aligns AI governance with human-centered values, ensuring AI fosters sustainability, moral foresight, and long-term decision-making.

Recommendation: The Growth Trinity Model must be integrated into AI governance, regenerative healthcare models, and adapted learning strategies.

Global case studies: AI & quantum intelligence in regenerative healthcare & workforce adaptability

Key finding: While AI and Quantum Intelligence are transforming cognitive longevity, neuroplasticity, and regenerative healthcare, fragmentation remains a barrier to large-scale adoption (Table 11).

Implication: Neuralink’s AI-powered brain implants demonstrate early-stage regenerative AI capabilities, enhancing neuroplasticity and brain function. However, high costs and accessibility barriers prevent large scale adoption, necessitating AI-driven affordability solutions.

Recommendation: Policymakers must create global AI standards for integrating cognitive longevity solu-tions.

ASEAN's potential for regenerative artificial intelligence: Societal and economic influence

While global AI-driven regenerative solutions exist, ASEAN’s AI economy lacks integration into workforce transformation & healthcare policies (Table 12).

Implication: ASEAN economies must prioritize regenerative AI investment to remain globally competitive. Key areas include:

• Workforce adaptation: ASEAN’s digital economy is projected to reach $1 trillion by 2030, yet AI-driven workforce transformation lags behind global benchmarks [8].
• Regenerative healthcare: ASEAN’s aging population is expected to double by 2050, increasing demand for AI-powered cognitive longevity solutions [9].
• AI policy standardization: A fragmented regulatory landscape hinders AI-driven economic growth. ASEAN must harmonize AI governance frameworks to facilitate innovation while ensuring ethical safeguards.

Recommendation: ASEAN policymakers must integrate AI-powered regenerative economy models into national AI strategies.

A call for AI-driven regenerative intelligence

AI is at a critical crossroads if it continues as a mere optimization tool, humanity risks cognitive stagnation, workforce displacement, and mental health crises. To prevent this, AI must evolve into a regenerative intelligence model that actively enhances adaptability, lifelong learning, and ethical decision-making. The next decade must focus on scaling regenerative AI solutions globally. Governments, industries, and researchers must collaborate to:

1. Embed AI into workforce adaptation models to prevent automation-driven job losses.
2. Develop regenerative AI policies that promote cognitive resilience.
3. Ensure AI serves as an enabler of human intelligence, not a replacement for it.
4. The future of AI is not just about machines it is about ensuring that humans flourish alongside AI, rather than being left behind by it.

Conclusion, Future Direction & Recommendation: Stewarding Regenerative AI for Human Flourishing

This review repositions AI as a regenerative intelligence system rather than an efficiency driven tool. By synthesizing research from cognitive science, regenerative economics, and ethical AI governance, we propose human-centered AI models that promote cognitive resilience, systemic well-being, and ethical governance. Future research should explore empirical validation through AI-integrated longitudinal cognitive studies and policy pilot programs.

The findings of this study confirm that AI must transition from a tool of efficiency and automation to a catalyst for human augmentation, cognitive longevity, and sustainable adaptability. While AI has excelled in optimizing processes, it has yet to evolve into a regenerative intelligence model that supports lifelong learning, mental resilience, and workforce evolution.

This conclusion synthesizes key findings, outlines policy recommendations, and identifies future research priorities necessary to steer AI’s trajectory towards a regenerative paradigm that enhances, rather than replaces, human intelligence and adaptability.

The author acknowledges insights from Neurons to Nations [13], Greening the Blue Ocean [6], and Building ASEAN’s Regenerative Economy [5] in shaping the theoretical and policy discourse in this review.

Aligning findings with research objectives

This research explored the potential of AI as a regenerative intelligence system, answering the key research questions: (Table 13)

Policy and Regulatory Frameworks for Regenerative AI

To enable regenerative AI leadership, global AI governance must integrate ethical regenerative intelligence principles. Governments should implement AI regulatory sandboxes for workforce adaptability testing [17].

Policy & industry recommendations: AI as a steward of human adaptability

Key policy recommendations are

• Ethical AI governance: Implement Spiritual Quotient (SQ) as a core principle in AI policymaking [15 source]; and Introduce ethical impact audits for AI deployment across cognitive and health do-mains [18-25].
• Cognitive and health regeneration through AI: Expand AI-assisted neuroplasticity interventions to address mental fatigue in high-risk industries; and Develop AI-driven adaptive learning systems to support cognitive resilience and decision-making.
• Regenerative AI economy: Establish ASEAN AI-DAO frameworks for decentralized AI governance [5]; and Implement equity based AI investment models supporting cognitive and health regeneration technologies (Table 14).

Policy Actions for Regenerative AI Adoption

• AI in learning & education: Must focus on adaptive learning systems, integrating neuroplasticity and regenerative intelligence.
• AI in healthcare & cognitive longevity: Must be embedded in cognitive resilience models & regenerative medicine strategies.
• AI in workforce adaptation: Must prioritize co-evolutionary intelligence models where AI supports human adaptability rather than replacing jobs.
• Implication: AI must not be a tool of economic disruption but a catalyst for human centered prosperity.
• Recommendation: Regenerative AI, Quantum AI, and ethical AI governance must converge into a unified framework, ensuring AI serves long-term human adaptation, cognitive longevity, and planetary sustainability [26-30].

Regenerative leadership & policy action plan

Key Finding: AI adoption requires Regenerative Leadership, ensuring AI serves human adaptability, work force longevity, and ethical development (Table 15).

Regenerative policy actions

• Governments: Must adopt AI-human adaptability KPIs into AI policy frameworks.
• Corporations: Must implement AI-driven regenerative leadership models in workforce training.
• Investors: Must fund AI models designed for long-term human adaptability & economic sustainability.
• Implication: Governments, corporations, and AI leaders must embrace regenerative leadership to en-sure AI enhances rather than replaces human capabilities.
• Recommendation: National and global AI strategies must include KPIs for human adaptability, workforce longevity, and sustainable AI governance [31-40].

Contributions to Knowledge, Research, and Practice

This research makes a groundbreaking contribution by positioning Regenerative AI as the next evolutionary phase of artificial intelligence. It challenges the prevailing AI paradigm, which prioritizes automation and efficiency, and introduces a human-centric model focused on cognitive resilience, neuroplasticity, and adaptability.

• Potential global influence: Challenges existing AI narratives, introducing regenerative intelligence as AI’s next evolutionary phase.
• Impact potential: Introduces Regenerative Experience (RX) as the missing link in AI’s evolution, shifting from automation to augmentation.

To ensure AI serves as a catalyst for long-term human adaptability, future research must focus on:

• Scaling regenerative AI in healthcare and workforce development: AI must move beyond disease detection and job automation to actively support neuroplasticity, mental resilience, and lifelong learning.
• Integrating Spiritual Quotient (SQ) into AI governance: AI decision-making frameworks must em-bed ethical intelligence and long-term human consciousness considerations, ensuring AI aligns with human values.
• Developing AI-driven cognitive longevity metrics: Establishing standardized cognitive adaptability indicators will help policymakers and businesses measure the long-term impact of regenerative AI on human intelligence.
• Recommendation: To achieve this, global stakeholders must:
• Governments: Implement national regenerative AI strategies, ensuring AI policies prioritize cognitive longevity and workforce adaptation.
• Industries: Invest in regenerative AI applications that enhance lifelong learning, mental health, and cognitive augmentation.
• Academia & research institutions: Establish interdisciplinary AI-human augmentation programs, integrating AI ethics, neuroplasticity, and quantum intelligence research.

By embedding regenerative AI principles into global policy, economic frameworks, and workforce strategies, AI can become a force for long-term human adaptability rather than short-term efficiency gains [41-55].

Final call to action

This review repositions AI as a regenerative intelligence system rather than an efficiency driven tool. By synthesizing research from cognitive science, regenerative economics, and ethical AI governance, we propose human centered AI models that promote cognitive resilience, systemic well-being, and ethical governance.

AI stands at a defining moment in history. The next decade will determine AI’s legacy will it remain a tool of automation, or evolve into a catalyst for cognitive resilience? Will it actively support human intelligence, resilience, and longevity, essentially regenerate human potential? The choice lies in how we steward this technology today and now. The future of AI must be regenerative. By unifying regenerative AI into global policy, AI can become a force for long-term human adaptability and planetary sustainability. To achieve this, global stakeholders must:

• Governments: Implement national regenerative AI strategies, prioritizing workforce adaptation and cognitive longevity; 
• Industries: Invest in regenerative AI applications for lifelong learning, mental health, and adaptability;
• Academia & Research Institutions: Develop interdisciplinary AI-human augmentation pro-grams, integrating neuroplasticity and ethical AI research [56-70].

This research received no external funding.

Informed consent was obtained from all subjects involved in the study.

Data and Insights can be made available for key stakeholders interested to further this research with sponsorship, grants and funds to advance ASEAN as a Regenerative Economy of the future.

The author Dr Rachel Ooi is grateful of her Faith, with family and friends support on this project being self-funding initiative for 2 years as a giving back, purposeful to igniting ASEAN as the spark for Regenerative Economy responding to a global crisis with hope and vision to begin. The author also acknowledges insights from Neurons to Nations, Greening the Blue Ocean, and Building ASEAN’s Regenerative Economy in shaping the theoretical and policy discourse in this review.

Dr Rachel is appreciative of her Independent Research Assistant: Baskar Periasamy on his research support on this review paper

The authors declare no conflicts of interest.

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