Advances in Social Science and Culture
ISSN 2640-9682 (Print) ISSN 2640-9674 (Online)

Vol. 7, No. 6, 2025
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Original Paper

Wisdom’s New Frontier: AI and the Quest for Understanding
Katryna M. Johnson1* & Jack Lesser1

1 Metropolitan State University, Department of Marketing, Supply Chain Management and

International Business, Minneapolis, U.S.A.
* Katryna M. Johnson, E-mail: Katryna.johnson@metrostate.edu

Received: August 22, 2025 Accepted: October 29, 2025 Online Published: November 11, 2025

doi:10.22158/assc.v7n6p12 URL: http://dx.doi.org/10.22158/assc.v7n6p12

Abstract

Artificial intelligence is increasingly used to support human judgment and decision-making. As its role

expands, a deeper question emerges: Can AI meaningfully contribute to human wisdom—not just by

improving access to information or enhancing efficiency, but by engaging with the more profound

dimensions of human understanding? While AI excels at identifying patterns and relationships, wisdom

encompasses far more—it demands discernment, ethical reflection, contextual sensitivity, and the

capacity to navigate competing values. This article considers how AI may support wisdom. It also

examines the extent to which AI limits judgment and impairs morality. The authors propose an

“AI–Human Synergy Wisdom Model (AHSWM)” to examine possible roles of AI in wise

decision-making. The model views humans as being the origin of wise decision-making. This paper also

acknowledges the potential of AI to assist when making wise decisions when used with care. A model is

developed from cognitive psychology, moral philosophy, and research in technology management. The

model advocates for creating AI that does not supplant common-sense human knowledge, but instead

aligns with it, enabling AI to assist in establishing moral responsibility and serving broader social ends.

Thus, the article aims to provide a framework for fostering sound reasoning in complex situations,

while highlighting the human capacities that are essential to wisdom.

Keywords

AI wisdom, cognitive processes, ethical decision-making, knowledge management, theoretical models,

artificial intelligence, philosophical perspectives.

1. Introduction

Generative intelligence has become a defining feature of contemporary artificial intelligence (AI). The

systems that include large language models and image generators show their ability to generate new

outputs by learning from extensive datasets (Casacuberta, 2012). The basic idea of generative



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intelligence is to learn patterns, structures, and meanings from previous data to create new

combinations that adapt to specific contexts. The technological innovation of learned adaptive synthesis

mirrors a fundamental cognitive principle that humans have used for reasoning throughout history: that

the persistent examination of prior knowledge leads to expertise and discovery.

Indeed, the generative structure of intelligence is not unique to machines. For centuries, human wisdom

has been understood as a similarly generative faculty—one that draws upon accumulated experience,

memory, and moral reflection to respond to complex and uncertain situations. Wisdom and generative

AI both involve transforming prior knowledge into new insights. Wisdom achieves this through moral

reasoning, emotional awareness, and consideration of social context. For example, Baltes and

Staudinger (2000, p. 122) define wisdom as "expert knowledge in the fundamental pragmatics of life,"

emphasizing its developmental and experiential foundations. Their Berlin Wisdom Paradigm identifies

five core criteria of wisdom-related performance, each of which depends on the integration of learned

experience into novel, contextually appropriate judgments. Sternberg (2023) defines the WICS model

(Wisdom, Intelligence, and Creativity Synthesized) as the process of creating and applying experience

and creative insight in the interest of the common good, encompassing wisdom as well. Wisdom,

according to Glück (2022), arises from the ongoing interaction among cognitive, affective, and

motivational processes that evolve through reflective engagement with diverse life experiences.

Kaufman, Sternberg, and Roberts (2019) explain that wisdom, creativity, and intelligence function as

connected abilities that help people create meaning while adapting to uncertainty and augmenting

human potential. These models demonstrate that wisdom extends beyond knowledge storage, as it

requires continuous interpretation and judgment activities. Kaufman, Sternberg, and Roberts (2019)

explain that wisdom, creativity, and intelligence function as connected abilities that help people create

meaning while adapting to uncertainty and enriching human development. These models demonstrate

that wisdom extends beyond knowledge storage, as it requires continuous interpretation and judgment

activities. According to Kaufman, Sternberg, and Roberts (2019), wisdom, along with creativity and

intelligence, forms an interconnected set of abilities that help people create meaning, adapt to

uncertainty, and promote human flourishing. The models present wisdom as a knowledge application

rather than a knowledge storage, because it requires active interpretation and the development of

judgment.

systems can demonstrate wisdom. The rapid evolution of AI has given rise to a new perspective on the

role of machines in social order. The work on AI has evolved from its early days of pattern detection

and language processing to encompass currently sophisticated subjects, such as responsibility and

judgment (Ferguson, 2025). While these developments may have great potential in various areas, such

as medicine or education, there is also the question of what they mean concerning human agency,

interpretation authority, and the ethically responsible design of increasingly independent systems

(Coeckelbergh, 2020; Goertzel, 2009; Vallor, 2016).

AI systems today do not possess the conceptual features that define wisdom, despite exhibiting



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generative capabilities. AI systems possess data processing capabilities and predictive modeling and

optimization functions, which do not provide enough support for the extensive reasoning abilities that

define wise decision-making. The definition of wisdom encompasses three essential elements:

contextual understanding, emotional insight, moral deliberation, and reflective abilities to handle

multiple priorities and uncertain outcomes (Baltes & Staudinger, 2000; Grossmann et al., 2020a; Jeste

et al., 2020). The reflective ethical processes that define wisdom are absent in AI systems that produce

outputs that mimic human reasoning. The system cannot comprehend social situations, manage

emotions, and assess outcomes through human value systems (Chukwuere, 2024; Jackson, 2024;

Teachflow. AI, 2023). The generative nature of AI operates through mechanisms that differ

fundamentally from the generative reasoning processes that define human wisdom.

The field of AI ethics has expanded quickly, but wisdom remains underdeveloped in system design and

governance (Taddeo & Floridi, 2018; Vallor, 2016). The absence of wisdom in AI domains becomes

most critical when AI systems operate in morally sensitive areas, including healthcare triage, automated

hiring, criminal justice, and elder care. Technical performance alone is insufficient in these specific

settings. Systems need to be evaluated based on both efficiency metrics and alignment with ethical

principles and the achievement of social priorities—highly functional systems without wisdom

approximation risk generating ethically inadequate and socially disruptive outcomes.

This paper, therefore, explores the question of whether and how AI might support wise

decision-making—not by replicating human consciousness, but by approximating the ethical and moral

reasoning, perspectival awareness, and contextual sensitivity that wisdom entails. The research draws

from psychological, philosophical, and technological literature to analyze theoretical models of wisdom,

evaluate their applicability to AI, and develop a conceptual framework to support the development of

systems that can facilitate wiser human-machine interactions. We also consider the role of human-AI

collaboration, recognizing that wisdom may emerge not from artificial systems alone, but from how

they extend and enhance human moral judgment in complex environments.

Crucially, we approach the challenge of artificial wisdom not as a binary question of possibility or

impossibility, but as a design problem. Moral psychology research has studied the relationship between

intelligence and wisdom since Ambrose (2016) and Sternberg (2023). The emergence of generative AI

necessitates that researchers investigate how human wisdom can be understood and partially replicated

through non-human systems to develop ethical AI and enhance wisdom.

1.1 Scope and Structure of the Study

This article examines the conceptual and design foundations for developing artificial intelligence that

supports informed decision-making. It builds on the distinction made in the introduction between

generative intelligence and human wisdom, considering whether AI can be intentionally structured to

promote context-sensitive, ethically grounded judgment. The paper reflects this question in four parts.

First, it considers the core psychological and philosophical dimensions of wisdom that remain largely

absent from today's AI systems. Second, it highlights real-world and technical challenges that occur in



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computationally simulating wisdom, specifically issues related to context, emotion, and ethical

sensitivity. Third, it offers a rough outline of artificial wisdom as a proposed architecture, synthesizing

insights from metacognition, moral psychology, and system ethics.

This research addresses the utilization and development of AI Systems that enhance human

decision-making. The paper examines

1. Philosophical and Theoretical Models of Wisdom: This segment examines the limitations of

wisdom often found in modern-day AI systems.

2. Examination of Human Wisdom's Role and Challenges for AI: While AI excels at data

processing and pattern recognition, it lacks human consciousness, true wisdom, and the ability to

understand complex moral and emotional contexts, posing significant philosophical and practical

challenges for creating "sage AI" and underscoring the need for human guidance and ethical

frameworks to augment rather than replicate human decision-making, especially when computationally

simulating wisdom.

3. Outline of Artificial Wisdom Architecture: This framework proposes a synthesis of insights

from metacognition, moral psychology, and systems ethics.

The overarching goal of this article is not to argue that AI can replicate human wisdom, but rather to

define the conditions under which AI might support wiser human-machine collaboration.

1.2 Contribution to Literature

This article contributes to the growing body of scholarship about how AI can be designed to reflect

more than just intelligence or efficiency. It adds to current scholarship by bringing together multiple

perspectives—philosophical, theoretical, psychological, and technical—to create a well-rounded

understanding of what artificial wisdom might involve. Rather than treating wisdom as something AI

must fully replicate, the paper suggests that AI can be developed to support wiser outcomes and

decision-making processes.

By presenting both theoretical foundations and practical considerations, the article helps clarify what it

means to think about wisdom in the context of AI. It also offers researchers and designers a clearer

direction for building systems that connect with ethical values and help people make better, more

thoughtful decisions in complex situations.

The AI-Human Synergy Wisdom Model (AHSWM) presents a new conceptual framework that

integrates the latest theories on human wisdom with the emerging promise of generative AI. It shifts the

AI ethics debate from risk aversion to proactively considering how AI can empower human flourishing

through the cultivation of wisdom, and it conceives of an answer that leads to a design-based solution

to integrate ethical and contextual factors into AI systems. This interdisciplinary synthesis contributes

to our overall understanding of AI by articulating a framework for AI that incorporates human moral

judgment and intuition in decision-making.



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2. Method

The study uses conceptual and analytical approaches to investigate the applicability of AI in

decision-making. This is a qualitative, cross-disciplinary literature review grounded in philosophy,

psychology, AI ethics, and computer science literature, aiming to develop an emergent theoretical

framework.

1) Extended Literature Review: A comprehensive review was conducted to understand the current

capabilities of generative AI, their social and ethical implications, and contemporary issues in the field.

This also provided background on technological advancements and the intricate philosophical and

psychological aspects of human wisdom.

2) Philosophical and Theoretical Analysis of Wisdom: This involved a detailed analytical review of

various theories of wisdom, encompassing prevailing psychological models (e.g., Berlin Wisdom

Paradigm, Sternberg’s WICS model, Glück’s model) and philosophical concepts (e.g., ancient Greek

notions of Phronesis, modern virtue ethics). Central components, characteristics, and developmental

paths of wisdom from these theories were identified and synthesized into dedicated “Theories of

Wisdom” and “Philosophies of Wisdom” tables. This dual analysis established rigorous standards

against which AI capabilities could be measured.

2.1 Ethical Use of AI Tools

The authors utilized AI tools, including Copilot, Gemini, and ChatGPT, to generate additional research

based on complex prompts and for some editing purposes. Additionally, the authors submitted the

paper to Grammarly multiple times to ensure proper citations and to refine the grammar and syntax. To

maintain transparency, Grammarly indicated a similarity score of 6%. The authors ensured that all AI

usage was cited appropriately, and in a few instances, Grammarly indicated a similar reference.

Grammarly indicated a final score of 4% for AI. In every instance, the writing was either the author's

original writing or AI was cited for editing. The authors critically analyzed AI responses and

maintained transparency in their writing, thereby ensuring the ethical use of AI in research (Alill,

2024).

3. Literature Review

It has already been demonstrated that AI can gather and process data quickly and accurately (Riserbato,

2024; Synthetix, 2024; Teachflow.AI, 2023). However, the larger challenge lies in transforming that

data into actionable knowledge, a task the National Institutes of Health (NIH) has been attempting to

achieve (Yongjun et al., 2023). AI will evolve into a tool that generates insights and supports ethical

decisions about societal norms and contexts, in addition to its current role as a data analysis tool

(Gonzalez et al., 2021; Peschl et al., 2025; Wood, 2024). The translation of knowledge into wisdom

requires an interdisciplinary intervention that incorporates philosophical, psychological, and

technological aspects to analyze its social applicability (Valor, 2024). There must be a framework in

which AI systems can articulate the potential human consequences of their decisions and those of their



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societies (Google DeepMind, 2025).

Considering whether AI can obtain wisdom may challenge our current views of wisdom (Casacuberta,

2012; Tsai, 2023). AI may only attempt to simulate wisdom, as it lacks the contextual awareness and

subjectivity that humans possess. Additionally, AI struggles in arenas where there is no clear solution,

moral gray areas exist, context is limited, and ethical decision-making is involved (Davis, 2019; Jeste et

al., 2021; Tsai, 2023). Concerns about AI's ability to make sound ethical and moral decisions suggest

that we consider AI a complementary tool rather than a replacement for human endeavors (Wisdom

Center, 2025). Over time, AI needs to be presented with ethical dilemmas to learn to address complex

problems in a manner that reflects societal values and customs.

Furthermore, Vallor (2024, para. 13) discusses the emerging field of machine ethics, indicating that the

goal is to build “moral machines that can serve as our ethical advisers.” Vallor contrasts this with the

ambitions of transhumanist thinkers, who advocate for using technology to accelerate human evolution.

Vallor warns that such aspirations risk undermining the very capacities—such as moral reflection,

meaning-making, and existential depth—that define human wisdom. In Vallor’s opinion, the search for

artificial wisdom must be based on a commitment to preserving, rather than supplanting, human

capacity for moral knowledge about oneself.

AI's capacity to handle and process large volumes of information refines human decision-making

(Riserbato, 2024; Synthetix, 2024; Teachflow. AI, 2023). These capabilities can be beneficial in

addressing global issues. AI systems can analyze major problems, including healthcare and

environmental issues, to provide potential solutions at a faster pace than individual human efforts

(Schwab, 2025; UN Sustainable Development Goals, 2025). The exclusive use of AI systems without

human values creates substantial ethical issues (Celluci, 2023). Balancing technology and ethical

responsibility is of the highest concern, and AI's ability to employ moral reasoning necessitates human

involvement.

Prior research has also suggested that ethical considerations are one of the biggest concerns in using AI

for decision-making (Gilson, 2025). Huffington (2024) proposes that AI can utilize our weaknesses to

enhance our virtues and create more effective institutions. Wood (2024) proposes that wisdom

development over time can be supported by AI-driven reflection and deliberation on decision-making,

rather than focusing solely on efficiency.

According to Graves (2021), Phronesis or practical wisdom is the most promising philosophical model

for developing wisdom in AI systems. Phronesis differs from theoretical knowledge and technical skill

because it needs sensitivity to context and moral reasoning to achieve the common good in uncertain

situations. Graves proposes that while machines may never achieve moral experience, they can be

trained to recognize ethically relevant features of a situation and to reason through moral trade-offs

using embedded virtue-oriented principles. The model promotes the development of AI systems that

produce answers while also performing goal-based deliberation across multiple stakeholder interests

and social consequences.



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Building on Graves’ philosophical framework, Lake’s research also highlights the inherent limitations

of current AI models in achieving such practical wisdom. Lake et al. (2017) identify the main weakness

of current AI models as their inability to learn through flexible, structured, causal, and coherent

methods, which humans use. The authors argue that AI must integrate intuitive psychology with

compositional reasoning and causal inference to achieve human-like real-world adaptability and

judgment. The features are essential for systems that operate in unpredictable or changing

environments, as they surpass the limitations of strict rule-based systems and narrow optimization

approaches. The research of Lake creates a connection between statistical methods and the

decision-making approaches required for socially grounded context-aware choices.

Simon, Rieder, and Branford (2024) study how AI systems obtain implicit values through their design

elements, data sources, and usage environments. The authors demonstrate that all AI systems carry

inherent values even though these values remain unexpressed. According to their approach, the

development of artificial wisdom requires building systems that can interpret ethics, rather than

providing machines with explicit ethical rules. The development of AI requires designers to create

systems that can examine their underlying assumptions while learning new moral principles and

maintaining beneficial relationships with diverse human viewpoints. The authors propose mechanisms

that enable AI systems to reveal and challenge their internal values while adjusting them throughout

time.

Nguyen (2025) develops this perspective by stating that artificial wisdom needs an integrative method

that unites learning architectures with ethical self-monitoring and social responsiveness. Nguyen

suggests that we should design systems that prioritize social impact alongside long-term effects and

human dignity, rather than solely focusing on task completion. AI development would transition from

measuring intelligence through task performance to measuring intelligence through contributions to a

shared moral world.

Jeste et al. (2020) indicate that people are becoming increasingly aware that wisdom encompasses more

than just mathematical reasoning and data processing. The development of artificial wisdom should

remain linked to human values, interpretive flexibility, and ethical oversight based on these

perspectives. AI design requires immediate integration of these frameworks because AI will continue to

affect significant social domains.

4. Philosophical Approaches to AI Wisdom

According to the National Institutes of Health (NIH), humans are necessary in generating AI wisdom,

as “only humans can have consciousness, autonomy, will, and a theory of mind” (Jeste et al., 2020, p.

24). This special characteristic of human wisdom is closely associated with the Theory of Mind, which

is the ability to attribute mental states to oneself and others (Ruhl, 2023). For AI to develop a theory of

mind, it would need "to have the capability to understand and remember other entities' emotions and

needs and adjust their behavior based on these. This capability is like humans in social interaction"



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(Arya, 2023, para. 12). If AI were to achieve self-awareness, it could reach human-like intelligence

(Arya, 2023).

4.1 Philosophical Perspectives on AI Wisdom

Philosophers have diverse views on how to incorporate wisdom into AI. Barkol (2025a) considers the

views of thinkers such as Socrates, Plato, and Aristotle, as well as those of AI. Barkol (2025b, para. 48)

suggests reflecting on agentic AI as “Allport lands in the digital age to participate in lively intellectual

discussions, taking their towering perspectives with them.” 1). By attributing personalities to agentic AI

and constructing rich prompts, AI can supply problem-solving capacities in selected philosophical

systems that entertain specific propositions.

Building on this, Kim and Mejia (2024) argue that AI development should progress from mitigating

harm to utilizing what is often referred to as Socratic wisdom. This method makes possible the

acknowledgment of ignorance and the perpetual questioning of our hypothesis of human flourishing.

They argue that AI systems should promote critical thinking among users and stimulate self-reflection,

rather than merely mimicking existing behavior or upholding existing biases.

Building on this, Polizzi and Harrison (2022) suggest that “cyber-wisdom” is the new “organizing

framework” to facilitate users negotiating the risks and opportunities that come with digitally mediated

connectivity. This “cultivation of cyber wisdom” will be informed by Neo-Aristotelian ideas and moral

theories, such as those that center on human nature, virtue ethics, and practical wisdom. Polizzi and

Harrison (2022, para. 1) identify four components of cyber-wisdom: “cyber-wisdom literacy,

cyber-wisdom reasoning, cyber-wisdom self-reflection, [and] cyber-wisdom motivation.”

Taking a more critical stance, Simon, Rieder, and Branford (2024) argue that discussions of AI wisdom

must acknowledge that AI systems are always value-laden. Research in philosophy and empirical

science reveals that AI systems operate through human judgment because they incorporate conceptual

assumptions derived from design decisions, data origins, and their social and political contexts.

Therefore, any effort to develop "wise" AI needs to consider not simply how values are encoded, but

how they are read and negotiated in the system's outcomes. Wisdom is more than making sense of

values, the authors argue: it includes the capacity to reason normatively in a flexibly transparent

manner, accountable to democratic standards.

Taddeo and Floridi (2018) add value by framing AI as a locus for ethical advancement, when guided by

what they call “design for values”. They call for this kind of moral prudence to be built into AI,

including the ability to consider possible future threats, long-term social consequences, and the dignity

of human beings. In this sense, AI wisdom is less about mimicking human cognition and more about

supporting technological agency with the ethical and epistemic responsibilities that characterize wise

action. Their intent illustrates the need for institutional and philosophical infrastructure that aligns in

such a way that AI no longer computes efficiently, but does so in ways that consider ethical

consequences.

These philosophical methodologies cumulatively broaden the theoretical foundation for researching



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artificial wisdom. They claim that such frameworks assume an AI system that acts wisely is one that

can reflect and use reason when dealing with ethical issues and collaborate with human beings towards

human flourishing, rather than one that merely reproduces human mental processes or intuitions. This

new perspective will transform AI from a mere tool into a moral agent, bound within a shared

ecosystem. Not only truthfully, but also scrutinizing the ethics.

4.2 AI as a Support System for Human Wisdom

However, considering AI's ability to promote wisdom also requires emphasizing a human psychological

quality that predates the advent of AI. Classical psychological research has consistently shown that

under conditions of information overload, human decision-making can become impaired in systematic

and predictable ways. Early Gestalt theorists described such environments as producing a high degree

of uncertainty and “felt discomfort,” which often leads to rigid thinking patterns, a phenomenon they

termed functional fixedness or Einstellung. Individuals confronted with too much information tend to

simplify their cognitive processing by narrowing their attentional field, reducing flexibility, and

defaulting to habitual or previously learned responses. Broadbent (1971) incorporated concepts from

information theory to argue that cognitive overload leads to psychological stress, which in turn affects

the overall functioning of the cognitive system.

From a purely psychological perspective, particularly in ethical or other complex scenarios, an

individual’s attention or moral judgment may diminish. Therefore, AI can serve as a support system to

enable humans to meaningfully apply wisdom in ethical and other contexts, allowing them to function

more productively. AI has the potential to enhance ethical reasoning—not by replacing human

judgment, but by clearing the cognitive space necessary for deliberation, perspective-taking, and moral

reflection. AI can help transform the context of decision-making from being overwhelming and reactive

to one that is reflective and informed.

5. Theoretical Models of AI Wisdom

Various theoretical models have been suggested on which imitation or emulation and culture like

effects might be based, and can be considered important pointers to how such systems should be

modelled and designedExamining these prevalent theories, it becomes possible to speculate about what

aspects are essential to wisdom, and how these aspects can inform the design of more advanced and

ethical forms of AI.

5.1 Models Emphasizing Environmental and Experiential Learning

4E Cognition Framework: The 4E Cognition Model (Embodied, Embedded, Enacted, Extended)

posits that AI agents can acquire “cyber wisdom” through real-time interaction with their environment.

(Alexander, 2025; Peschi et al., 2025; Santos, 2023). The model emphasizes that the cultural context,

social interaction, and ongoing exchange of real-world information are crucial for enabling AI to

acquire context-sensitive understanding, a fundamental component of wisdom.

Material Engagement Theory (MET): MET posits that cognition is not limited to the mind but is



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instead embodied and environmentally embedded (Iliopoulos, 2019). For AI, MET proposes that

wisdom is cultivated through dynamic interaction with its social and physical environment, facilitating

experiential and contextual learning. Similar to human beings, AI systems need to acquire knowledge

from their environment to comprehend and apply it effectively, thereby increasing their understanding

of both AI and its applications (Malafouris, 2019).

5.2 Models Focused on Human Cognitive Processes and Ethics

Cognitive Psychology Integration: To instill wisdom, the Cognitive Psychology Integration method

advocates for AI to mimic human mental operations, such as memory, learning, and decision-making,

particularly by integrating empathy and ethical thinking into its simulated processes (Jeste et al., 2020;

Marsh et al., 2016; Nelson et al., 2025; Taylor & Taylor, 2021).

Common Model of Wisdom: This model posits that wisdom consists of moral aspirations and

reputational concerns, alongside perspectival meta-cognition (PMC) (Grossmann et al., 2020, p. 103).

For AI, this model suggests that systems ought not only to detect climate-related knowledge gaps but

also to engage with different worldviews and coordinate with moral principles, such as seeking the

truth, to determine how to act. It emphasizes the contextual and cultural aspects in constructing wisdom,

focusing on striving for “the common good” (Weststrate et al., 2016; Grossmann et al., 2020b).

The Dreyfus Model of Skill Acquisition: The Dreyfus model comprises five levels of proficiency,

ranging from Novice to Expert, culminating in Wisdom (Dreyfus & Dreyfus, 1986; Tsai, 2020).

According to the study’s author, a key aspect of the decision model is the ability of AI to transition

from rule-based to intuitive, context-driven decision-making. This progression enables AI to make

informed judgments in complex situations, potentially achieving a level of wisdom comparable to that

of humans.

Ethical AI Decision-Making Model: This model sets out principles for developing AI systems that are

fair, transparent, and accountable. It seeks to integrate human ethical values throughout the AI

development process so that systems consider cultural, legal, and stakeholder factors, deal with biases,

and ultimately contribute to the broader good (Nguyen, 2025; Prem, 2023; Sinha & Lakhanpal, 2024;

Trotta et al., 2023; Tsai, 2020).

Integrative Ethical AI Model: Prioritizing the idea that for AI to be wise, it needs to gain experience,

understand how to deal with new situations, and be able to understand the consequences of its actions

(Dabis & Csaki, 2024; Nguyen, 2025). Such a model has the advantage of preparing students to strike

an appropriate balance between efficiency and effectiveness, and to integrate both practice, knowledge

and ethical reasoning in every area of their legal workIt believes that sage AI systems are not only

capable of managing enormous datasets but also of utilizing that intellect effectively and making

informed ethical decisions while adapting to changing conditions (Dabis & Csaki, 2024; Nguyen,

2025).

Metacognition: Ardelt’s (2004) three-component wisdom model, grounded in moral psychology,

encompasses cognitive, reflective, and affective dimensions. Applied to AI, Metacognition presumes



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that genuine AI wisdom necessitates systems having the capacity to detect biases, identify logical

fallacies, develop contextual methods, gain self-knowledge, and commit to ongoing growth (Johnson,

2022; Crowder, 2011; Johnson et al., 2024; Rodriquez & Kannan, 2024). Self-knowledge is necessary

for AI to evolve from specialist knowledge to comprehensive wisdom, facilitating ethical action and

transcending subjectivity (Johnson et al., 2024).

Neurobiological Model of Wisdom: This model examines the cognitive and neural processes

underlying human wise decision-making (Bhuyan et al., 2023). It explores how AI can replicate

neurobiological properties, including emotion regulation, empathy, and self-reflection. The objective is

to create AI that exhibits wise and empathetic actions and human-like moral deliberation, emphasizing

the blend of biological and cognitive science into intelligent AI system design to facilitate sound and

moral decision-making (Lee & Jeste, 2019; Nguyen et al., 2020).

Phronesis (Practical Wisdom): A neo-Aristotelian virtue ethics framework proposed by Darnell et al.

(2019), Phronesis integrates moral reasoning with action through a coordinated interplay of cognitive,

meta-cognitive, affective, and motivational processes. For AI, Phronesis recommends ideal systems

capable of making good, context-dependent choices regarding values and outcomes, ultimately leading

to AI that makes morally sound decisions beneficial for individual flourishing (Graves, 2024; Jeste et

al., 2020; Maher, 2016; Tsai & Ku, 2024).

5.2 Models for Knowledge Organization and Collaboration

Hierarchical Knowledge Representation Model: This model suggests that AI wisdom can be

inductively learned by structuring knowledge hierarchically, moving from specific to general (Lee,

2024). This is consistent with the notion that the structure and connection of information—not the

quantity of information—are crucial in generating creative solutions. This model also enables AI to

recognize its knowledge limits, meaning it can request more data or human creativity if needed—a

clever endeavor requiring higher-order reasoning (Bhuyan et al., 2025; Lee, 2024).

Human-AI Co-Creation Model: This model demonstrates how human-AI collaboration can augment

intelligence and creativity. By collecting information from various sources, AI enables new possibilities

at every step of the creative process, thereby making creativity more inclusive and accessible

(Ismayilzada et al., 2024; Lockhart, 2024; Wu et al., 2024). Ideal AI, according to this model, should be

able to sort information, identify patterns, produce new work, and make incisive connections (Lockhart,

2024).

To provide a structured overview of these foundational theories and their implications for AI wisdom,

Table 1 presents a comparative analysis of the theoretical approaches to AI wisdom. This table extends

beyond simple summaries, organizing each approach by its Core Concept of Wisdom or Key

Contribution, its Primary AI Relevance (i.e., how it informs AI wisdom), and its specific Ethical and

Contextual Implications for AI. This comparative framework enables the identification of what is

unique about each theory's conception of wisdom and its distinctive relationship to the challenge and

potential for creating AI that supports wise decisions.



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Table 1. Theoretical Approaches to AI Wisdom

Theoretical

Approach

Core Concept of

Wisdom / Key

Contribution

Primary AI

Relevance

Ethical / Contextual

Implications

4E Cognition

Framework

Embodied,

embedded, enacted,

extended cognition

Context-sensitive AI

through real-world

interaction

Requires cultural and

environmental integration

Cognitive

Psychology

Integration

Human cognitive

processes: memory,

learning, empathy,

ethics

Modeling empathy

and ethical reasoning

Wise AI requires emotional

understanding

Common Model

of Wisdom

Moral aspirations +

perspectival

metacognition

Helps AI integrate

perspectives

ethically

AI must align with common good

aims

Dreyfus’s Skill

Acquisition

Progression from

novice to expert

judgment

Shift from rule-based

to intuitive reasoning

Contextual experience strengthens

AI judgment

Ethical AI

Decision-Making

Model

Fairness,

transparency,

accountability

Ensures AI aligns

with social norms

Ethical safeguards required

throughout lifecycle

Hierarchical

Knowledge

Representation

Knowledge

structured from

specific to general

Allows AI to

generalize and

recognize limits

Recognizing limitations is key to

safety

Human–AI

Co-Creation

Model

Collaborative

creativity and

problem-solving

AI enhances human

creativity

Must include ethical awareness in

outputs

Integrative

Ethical AI

Model

Experiential learning

balancing efficiency

and ethics

Supports adaptive

ethical reasoning

Moral integrity cannot be

secondary to performance

Material

Engagement

Theory

Cognition through

interaction with

environment

Supports embodied

contextual learning

Requires situated application to

avoid misalignment

Metacognition Self-reflection and

bias awareness

AI monitors and

adjusts reasoning

Bias recognition is critical for

ethical use

Neurobiological

Model of

Emotion regulation

+ empathy

Supports

compassionate AI

Emotion-informed reasoning aids

moral judgment



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Wisdom decision-making

Phronesis

(Practical

Wisdom)

Ethical reasoning +

greater good

orientation

Applies knowledge

ethically in context

AI must integrate emotion,

cognition, and ethics

Note. Table content generated with assistance from Google Gemini (7/21/2025). Formatting assistance

provided by ChatGPT.

6. Human Wisdom: Guiding AI Through Its Challenges

The path toward developing AI that's truly wise depends to a great extent on the insights and wisdom of

human beings. The National Institutes of Health (NIH) agrees that human judgment and intelligence

should be integrated with artificial intelligence. This type of collaboration is essential, since—as the

NIH points out—qualities like consciousness, autonomy, free will, and an understanding of others’

thoughts and feelings are uniquely human (Jeste et al., 2020, para. 24). Because of that, true wisdom,

grounded in these human traits, can only come from people themselves.

Human wisdom enables us to instruct AI systems to operate based on specific principles to inform

decision-making. AI excels at sorting through large amounts of information and assisting people in

navigating complex ethical and moral decisions (Graves, 2024). However, it does not have a

conscience or a real sense of right and wrong—and it often has a hard time dealing with gray areas

(Celluci, 2023)The NIH highlights AI's benefits in information provision and extensive health dataset

analysis, predicting that AI systems will utilize accumulated knowledge, gained experience, diverse

perspectives, and multiple scenarios to produce informed decisions.

Gabayan (2024) documents how human beings’ know-how and experience need to be augmented in AI.

Celluci (2023) also documents that people and AI need to be partnered so that they can set clear ethical

limits, gain a deeper understanding of the broader impacts of AI, and avoid bias. The path to actual “AI

wisdom” is not straightforward, however. Every phase of progress, from trying to mimic human

common sense to forming sound moral decisions, is filled with practical challenges and tough open

questions. Human oversight remains essential to establish ethical benchmarks, monitor AI decisions for

prejudice, and guide technology so that it remains compatible with our values. In its absence, the

potential of AI can be undermined by failures such as mistrust, opacity, or built-in bias. Table 2

discusses issues that need to be addressed while building AI wisdom.

Table 2. Challenges in the Creation of AI Wisdom

Theoretical Approach Challenges or Constraints

4E Cognitive Framework Simulating embodied, embedded, extended, and

enactive cognition in AI systems requires

modeling complex interactions between mind,



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body, and environment (Lake, 2017; Peschi et al.,

2025; Santos, 2023).

Cognitive Psychology Integration Bridging human cognitive processes and AI

algorithms requires robust modeling of

higher-level psychological functions and attention

to ethical issues (Kapoor, 2025).

Common Model of Wisdom Incorporating moral aspirations and perspectival

meta-cognition into AI while maintaining

reliability and ethical decision-making is

challenging (Grossman et al., 2020b).

Dreyfus’s Model of Skill Acquisition Replicating the progression from novice to expert

and capturing intuitive, experiential

decision-making in AI systems is difficult (Tsai,

2020).

Ethical AI Decision-Making Model Avoiding algorithmic bias, ensuring transparency

and accountability, and accommodating

multicultural ethical perspectives pose

implementation challenges (Wood, 2024).

Hierarchical Knowledge Representation Model Managing complex, multi-layered knowledge

structures and ensuring accurate contextual

retrieval are ongoing challenges (Chen, 2024).

Human-AI Co-Creation Model Maintaining human creativity, establishing

effective collaboration, and addressing ethical and

bias concerns in co-creative work are key

challenges (Wu et al., 2021).

Integrative Ethical AI Model Integrating multiple ethical systems while

ensuring transparency, accountability, and fairness

remains difficult (Webb, 2025).

Material Engagement Theory Simulating dynamic interaction between cognition

and material culture in AI systems is difficult

(Microsoft, 2025; Peschl et al., 2025).

Metacognition Replicating human reflective and affective

dimensions and achieving depth of wisdom in AI

systems presents obstacles (Rodriguez & Kannan,

2024).

The Neurobiological Model of Wisdom Replicating neural processes, emotional



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regulation, and ethical reasoning in AI systems

poses complexity (Lee & Jeste, 2019).

Phronesis Aligning human and AI moral reasoning, defining

the “Good”, and scaling ethical decisions in

real-world contexts is challenging (Sullins, 2025;

Tsai & Ku, 2024).

Note. Formatting assistance provided by ChatGPT. Table content generated with assistance from

Google Gemini (7/21/2025)

6.1 Discussion of Challenges

AI wisdom engineering encompasses several key areas of challenge, ideally structured to capture the

intellectual and moral dimensions of human life within a simulation environment. To attain

superintelligence, it is not enough to process information or have access to millions of pages of data;

the system must also understand context, engage in moral deliberation, and navigate the complexities

of human life. This section discusses the specific theoretical and practical barriers that have to be

overcome so that AI can be directed towards true wisdom.

6.2 Replicating Human-Like Cognition

The precision and subtlety of human mental powers are something that most AI models cannot

approximate to the necessary degree of accuracy required for robustly mimicking human cognition.

 4E Cognitive Framework: It stresses embodied, embedded, extended, and enactive cognition

with pervasive interactions among body, mind, and world. AI systems cannot easily replicate such

effects because they lack physical bodies and therefore cannot comprehend the physical influence on

cognition (Peschl et al., 2025; Santos, 2023).

 Dreyfus's Model of Skill Acquisition: Replicating the human journey from novice to expert,

involving intuitive decision-making and learning from experience, is a significant hurdle. AI models

currently struggle to acquire knowledge through lived experience in the same way humans do,

particularly in developing intuitive skills (Tsai, 2020).

 Metacognition: Ardelt's model emphasizes the combination of cognitive, reflective, and

affective features. AI systems struggle to mimic this due to the high degree of self-awareness, reflection,

and emotional control—characteristics uniquely human (Rodriguez & Kannan, 2024).

6.2 Ethical and Moral Integration

The biggest challenge lies in integrating ethics and morality into AI decision-making, which forms the

foundation for developing responsible AI.

 Ethical AI Decision-Making Model: To ensure that AI systems can act morally, we need to

do more than address algorithmic bias; we must also incorporate transparency and diversity of ethical



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norms. This is, however, challenging because the ethical norms vary significantly between cultures and

contexts, making it difficult to identify a universally ethical AI. (Wood, 2024).

 Integrating moral ideals and perspectival meta-cognition into AI systems is a challenging task.

It requires AI to understand and apply moral reasoning in a manner that is ethically consistent with

human ethics and societal norms (Grossman et al., 2020b).

 Phronesis: This approach requires practical wisdom and ethical decision-making. AI

technologies fall short in operationalizing a socially-conceived "Good," aligning human and AI moral

understanding, and scaling ethical decisions for everyday use. More detailed information is necessary

to implement concepts such as Zubiri’s “sentient intelligence” and establish assessment criteria for

moral AI (Sullins, 2025; Tsai & Ku, 2024).

6.3 Complex Knowledge Management

Effectively managing and retrieving multi-layered knowledge structures poses a distinct challenge for

AI systems.

 Hierarchical Knowledge Representation Model: Although useful in AI wisdom research,

this model struggles with complex and multi-level knowledge systems. Accurate, context-dependent

retrieval and integrating various types of knowledge, especially in connecting particular facts to broad

ideas, still prove challenging (Chen, 2024).

Human-AI Interaction

Balancing human creativity with AI capabilities and ensuring meaningful collaboration is essential for

co-creation models.

 Human-AI Co-Creation Model: This model needs to overcome challenges such as

preserving human imagination alongside AI-generated output and addressing ethical and bias issues in

co-creative processes. Disparate human goals, personalities, and AI procedural logic may interfere with

fruitful cooperation (Wu et al., 2021).

Neurobiological Replication

Replicating the intricate neurocircuitry and integrating emotional regulation found in the human brain

is a formidable task for AI.

 Neurobiological Model of Wisdom: Developing AI systems that incorporate emotional

regulation alongside the replication of complex neurocircuitry presents significant challenges. The

result of this would be contingent upon a deep understanding of brain function that would allow AI to

detect and account for people's emotional state, much like humans do as part of their decision-making

process (Lee & Jeste, 2019; Google DeepMind, 2025).

6.4 Cultural and Legal Diversity

One of the significant issues in deploying AI globally is negotiating conflicting ethical duties in various

legal and cultural traditions.



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 Integrative Ethical AI Model: This model integrates multiple ethical models to maintain

transparency, accountability, and reduce bias in AI. The extensive ethical and legal variations across

regions present challenges to creating a globally authentic ethical AI system (Webb, 2025).

The path to achieving true AI wisdom faces many theoretical and practical challenges. Key obstacles

include replicating complex human cognitive processes, such as embodied cognition, intuitive

decision-making, and metacognition; integrating ethical and moral reasoning aligned with diverse

human values; effectively managing intricate knowledge structures; establishing genuine human-AI

partnerships; replicating neurobiological emotional control mechanisms; and implementing ethical

standards across various cultural and legal frameworks. These multi-faceted problems need to be

resolved when and if we develop wise AI systems.

7. The Synergistic Enhancement of Human Capabilities by AI

7.1 The AI-Human Synergy Wisdom Model (AHSWM)

The AHSWM model proposes that humans and AI can coexist in harmony, complementing each other's

strengths. Humans gain superior abilities, undergo personal development, exhibit creativity, and

develop strength, while AI benefits from human wisdom in ethics and morality. This model integrates

human judgment—rooted in consciousness, values, and contextual understanding—with the analytical

and pattern-recognition strengths of AI. The AHSWM's core premise is that this synergy leads to a

richer, contextually informed, and morally grounded form of “wisdom” that neither humans nor

machines could achieve in isolation. Ultimately, AHSWM describes how practical, ethical, and

customized consequences are realized through human-AI cooperation.

7.2 AHSWM Framework: Layers and Components

As shown in Figure 1, the AI-Human Synergy Wisdom Model (AHSWM) is composed of three

interconnected layers.



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Figure 1. AI-Human Synergy Wisdom Model (AHSWM)

Note. This model was generated with the assistance of Microsoft Copilot (2025)

1. Basic Capacities: This foundational layer encompasses the distinct skills of human beings and

AI.

 Human strengths encompass cognitive and ethical abilities, including reasoning, contextual

judgments, empathy, moral and ethical reasoning, contextual awareness, consciousness, values, and

social sensitivity (Zhang et al., 2023). Human wisdom, particularly its ability to navigate complex

contexts and consider future impacts, is vital for ethical decision-making (Lees & Young, 2020) and

social sensitivity (Zhang et al., 2023).

 AI Strengths: AI contributes through advanced data analysis, pattern recognition, and efficient

task execution, enhancing creativity by identifying new relationships and yielding innovative solutions

(Bozkurt & Aharma; Microsoft, 2025).

 Convergence: The interplay of these strengths leads to Human Wisdom Integration and

Augmented Human Potential.

2. Mediating Variables: The second level focuses on variables that decide the extent and kind of

human-AI convergence. Mediators have a significant impact on how humans learn, adopt, and engage

with AI technology, and its implications for establishing genuine relationships are profound.

 AI Interplay Dynamics: Human trust and perception of AI (as a tool, threat, or conscious entity)

directly determine the willingness to use and depend on the technology. High trust fosters greater

adoption, while perceived bias or unreliability reduces it (Guingrich & Graziano, 2024).

 Knowledge Management Processes (KMPs): Knowledge Management Processes (KMPs):

Organizational knowledge acquisition, retention, and dissemination capabilities are greatly facilitated

by AI's processing and analyzing capacity, allowing them to work better and react in a timely fashion

(Leoni et al., 2022).



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 Attitudes towards AI: An organization's or individual's perspective on AI, along with their

assessment of its usefulness and ease of use, as well as their concerns about job loss and ethical issues,

serves as a direct mediator for AI adoption and integration. Individuals with a positive disposition

towards AI are more likely to implement it at higher levels, while institutions with a negative

disposition will face greater implementation barriers. (Emon & Khan, 2025).

 Ethical Governance Frameworks: This dimension ensures that technological advancements

evolve in accordance with human values. This dimension usually includes five main elements: ethical

standards and norms, which dictate respectful AI practice by fairness, transparency, and user data

privacy; compliance with laws, that AI rules and global and industry-specific laws, such as GDPR and

the EU AI Act, are adhered to; accountability structures, assigning clear roles and responsibilities for

developing and releasing AI; transparency and explainability mechanisms, that provide transparent,

understandable, and traceable AI output; and ongoing monitoring and auditing, that entails ongoing AI

systems testing for performance, regulation, and bias elimination and detection. By integrating these

values in design, organizations can create ethical AI that preserves human values and helps to tackle

social disparity. This is true for bias, transparency, accountability as well as privacy resulting in a

positive social impact (Emon & Khan, 2025; Waehlisch, 2025).

 Communication Effectiveness: The pace, effectiveness, and simplicity of communication terms

used in human-AI and human-AI-assisted tool interactions are vital for effective communication. AI

improves communication by removing barriers and delivering precise information swiftly (Ateeq,

2024).

3. Intended Outcomes: The final layer represents the desired results of the synergistic

collaboration. The last step of the AI-Human Synergy Wisdom Model is the ensuing impacts of the

fruits of collaboration between humans and AI. Aside from being efficient, they are supposed to have a

profound positive effect on the individual and society:

 Facilitating Ethically and Contextually Oriented Decision-Making: This cooperation

facilitates decisions not only based on information, but also deeply informed by moral principles,

human ethics, and a profound understanding of the interconnected complexities of social and

environmental issues. Through the intersection of AI's analytical capacity and human wisdom,

decisions become more sophisticated, ethical, and attuned to long-term well-being.

 Building Resilient and Adaptive Individuals and Organizations: Through greater exposure to

adaptive information and advanced problem-solving capability, individuals and organizations acquire

the ability to anticipate, adapt to, and flourish in adversity. This partnership fosters a robust

environment for learning from information, adapting plans, and being productive in dynamic

circumstances.

 Enabling Self-Determination and Innovation: AI's emergent relationship perception capacity

and content tailoring enable humans by providing them with customized resources that are best suited

to their desires and interests. Human curiosity, insight, and creativity are supported with individualized



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teaching and AI’s capacity to perceive emerging relations, promoting more personal growth and more

inventive productivity.

 Designing Socially Responsible AI Systems: Continuous human monitoring and ethical

monitoring are integrated into the model, ensuring that AI systems are designed and implemented in a

manner that serves the common good of society. This includes designing AI that is equitable,

transparent, accountable, and purpose-built to preserve human values and actively counteract biases

while preventing the aggravation of social injustices.

7.3 Alignment with Contemporary Research

This model also aligns with contemporary research, which has emphasized that AI integration should

not only be technical but also socially engaged, psychology-based, philosophically grounded, and

ethically informed. Human and artificial reasoning rely on metacognition, moral reasoning, and theory

of mind in solving complex social dilemmas, as proposed by Grossmann et al. (2020). Likewise,

Abrams (2024) and Brey and Danow (2024) hold the view that psychological and ethical theories

should be incorporated when designing AI systems that are moral, fair, and inclusive. By incorporating

interdisciplinary understandings into human-AI collaboration and AI design, the AHSWM provides a

direction toward wiser, context-sensitive, and more ethical decision-making.

8. Empirical Testing of Wisdom in AI Systems

Wisdom is complicated to measure, even in humans. The ability to make ethical decisions is not a static

quality or a single outcome, but a developing capacity to judge ethically while understanding the

context and controlling emotions. These qualities develop through time as they form from the

interaction of uncertainty with conflicting values and social complexities. Measuring wisdom in

artificial systems involves a shift away from traditional performance measurements towards

context-sensitive and integrative evaluation approaches (Google DeepMind, 2025).

Cognitive-reflective benchmarking is also an emergent approach through scenario simulation. Drawing

on the Berlin Wisdom Paradigm (Baltes & Staudinger, 2000), the performance of AI agents can be

assessed in handling simulated moral, emotional, and interpersonal conflict scenarios. These

simulations would force AI to weigh numerous points of view, adopt long horizons, and balance

opposing values or compromises (e.g., justice versus compassion). The activities need organized

thresholds to assess the depth of moral thinking, decisional complexity, and context relevance

(Microsoft, 2025). The system needs to demonstrate ethical deliberation and social awareness by

reasoning through ambiguous situations, rather than focusing solely on correctness.

The second method utilizes entropy as a fundamental principle of both physical and information theory,

which measures the uncertainty, variability, and disorder of a system. Entropy functions as a universal

principle that controls thermodynamics, communication systems, biological evolution, and artificial

intelligence systems. Entropic measurement of the system responses to inputs indicates their predictive

stability and flexibility. The system has rule-based, strict responses under low entropy, but becomes



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more random with higher amounts of entropy (Microsoft, 2025). A system needs to maintain a balanced

state between adaptability through variability and structural coherence, as well as principled behavior,

to demonstrate wisdom. Entropy serves as an effective tool for evaluating AI systems' ability to handle

complex situations, balancing disorder and rigid dogmatic behavior. Recent studies have shown that

preserving entropy in training enhances generalization and reasoning capacity, which is an elementary

design principle for intelligent behavior rather than a diagnostic (Cui et al., 2025).

The third approach involves emotional calibration through inverse-U modulation. Human wisdom

depends on proper emotional regulation and arousal management systems. An AI's decision quality can

be evaluated under differential cognitive load or emulated emotional intensity, such as noise,

contradictory goals, or ambiguous stakes. If the system functions well at intermediate levels of stress

and fails in extremes, as per the Yerkes-Dodson curve, this would be a sign of wisdom-like regulatory

patterns. The ability of AI to act under stress is a test of emotional strength, though AI is not emotional.

Wisdom measurement must be conducted as a collaborative effort between human beings and AI. The

most significant technological weakness of AI systems is their inability to exhibit emotions, despite

their technological advancements.

The quest for wisdom by artificial intelligence is faced with one overriding challenge: the fact that

wisdom operates beyond the domain of processing and analyzing data. Wisdom involves understanding

emotions, as well as the power of exercising moral judgment and the capacity to comprehend intricate

situations. These elements form the essential foundation that enables wisdom to develop (Google

DeepMind, 2025). Moreover, far from being a bug in human thinking, emotion is a cornerstone of our

evolution. Designing wise AI, therefore, is not about replicating human affect, but rather about enabling

systems to scaffold human moral insight and emotional reasoning.

9. Foundations: Why AI’s Path to Wisdom Requires More Than Data

The pursuit of artificial wisdom faces the unavoidable challenge: wisdom is not just about information

handling, but also about emotional understanding, ethical decision-making, and contextual knowledge

(Microsoft, 2025). They are not niche capabilities; they are exactly central to what makes wisdom

possible. Emotion is not a defect in human reasoning—it is an evolutionary success. The human brain

evolved in layers: from the instinctual brainstem, to the emotion-centered limbic system, to the

reflective neocortex. Wisdom emerges from the integration of these systems, where emotion tempers

reason and reason gives structure to feeling.

AI systems, however, lack the capacity for emotional experience. They do not feel grief, joy, or moral

conflict. The systems generate affective responses through pattern recognition and probabilistic

modeling; however, they lack the embodied, subjective awareness that defines human empathy and

ethical courage. The absence represents a fundamental conceptual limit. Human emotional competence

develops through personal experiences combined with cultural values and moral growth. Wisdom in

eldercare, trauma recovery, and conflict mediation needs professionals to create spaces for ambiguity

while showing compassion and handling multiple values without simplifying into binary choices. AI



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systems focus on achieving clarity, efficiency, and resolution, which can work against the deliberative

process needed for wisdom.

The ability to regulate emotions functions as a cognitive resource that goes beyond being a

psychological trait. Through emotional regulation, humans develop the ability to handle uncertain

situations by postponing judgment and evaluating different viewpoints. The capabilities that AI systems

find challenging to replicate are exactly these. AI systems tend to make inflexible decisions when they

lack emotional calibration, often becoming overly confident in their outputs or becoming paralyzed by

ambiguous situations.

Actual emotional competence in AI does not mean synthetic feeling. The design of such systems

requires them to be aware of their context and sensitive to relationships while enabling emotionally

intelligent outcomes. Wisdom is often relational. The primary value of an AI system lies in its ability to

support humans in reflective processes that lead to deliberate actions with increased care and

consideration. The goal is not synthetic wisdom, but supportive augmentation—amplifying what is

most meaningful in human judgment, rather than attempting to replicate it.

10. Limitations

This study is conceptual and does not include empirical testing of the AI-Human Synergy Wisdom

Model. The research incorporates multiple theoretical frameworks from philosophy, psychology, and

cognitive science; however, it does not provide operational definitions of what “wisdom” means. The

absence of consensus regarding definitions of wisdom, measurement techniques, and instantiation

methods for both humans and machines makes any wisdom modeling approach difficult to apply.

The paper examines the emotional and ethical aspects of wisdom primarily through Western

philosophical perspectives. The analysis lacks a comprehensive cultural understanding of wisdom

because it fails to incorporate cross-cultural perspectives. The paper fails to address how wisdom stands

as a desirable objective for artificial intelligence systems.

The above is a shortcoming of the paper's current focus. Although it addresses philosophical and ethical

issues, it does not sufficiently address the practical matters of wisdom achievement in AI, especially in

the context of current economic and institutional incentives. Today's trend is that economic and

institutional incentives tend to favor rapid operations and profits over ethics (OpenAI, 2025). Artificial

wisdom has yet to be attained because fundamental structural modifications to AI research and

governance have not been made.

11. Future Research

Future research should focus on two key areas. First, empirical studies are needed to test whether AI

systems can enhance human moral reasoning in real-world contexts. For example, can AI support wiser

decisions in eldercare, climate policy, or restorative justice? Such studies would help determine

whether AI functions as a scaffold for human wisdom or a substitute for it, and what the consequences



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are.

Second, design research should explore how to build emotionally aware, context-sensitive AI systems

that do not simulate emotion but respect its role in moral life. This includes developing architectures

that can recognize when a situation requires ethical deliberation rather than optimization, and that can

defer to human judgment when appropriate—participatory design methods, value-sensitive design, and

human-computer interaction research offer rich resources for this work.

Second, future research should examine the political and institutional factors that influence the

emphasis on wisdom as a design principle. What kinds of governance, funding, and accountability

structures are needed to support the development of AI systems that prioritize ethical reflection over

efficiency? (OpenAI, 2025).

12. Conclusion

This paper argues that the pursuit of artificial wisdom is not a question of whether machines can

become wise, but instead of how they can support wiser human action. Wisdom is not reducible to

intellect or the product of size. It is an ethical and affective ability grounded in human experience,

situational awareness, and moral regard.

AI systems, as they currently exist, lack the richness of emotions, the sensitivity of morals, and the

perspectival quickness that wisdom entails. However, it does not render them useless in our quest for

wisdom.

AI systems, as they currently exist, lack the emotional depth, moral awareness, and perspectival

flexibility that wisdom requires. However, this does not mean they are irrelevant to the pursuit of

wisdom. AI systems designed with care enable humans to handle complex situations while considering

their values and making decisions among multiple priorities. The technology enables moral reasoning

by freeing up mental space, revealing concealed assumptions, and providing fresh viewpoints.

Future AI wisdom requires systems that augment human ethical capacities, not machines that imitate

human consciousness. This will require a reframing of the way we develop, test, and certify AI, not as

efficiency tools, but as partners in ethical inquiry. If AI is to aid in wisdom, it will not be because it

becomes more human, but rather through the assistance it provides in making humans wiser.

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	Wisdom’s New Frontier: AI and the Quest for Unders
	Katryna M. Johnson1* & Jack Lesser1
	1 Metropolitan State University, Department of Mar
	Abstract
	Keywords
	6.1 Discussion of Challenges
	7.3 Alignment with Contemporary Research