The role of artificial intelligence in promoting kindness in society: A qualitative study to advance well-being,equity,and positive social change

Abstract

Background and Objective: Artificial Intelligence (AI) has the potential to influence social behavior and public well-being. This study investigates AI’s role in promoting kindness and identifies ethical strategies to foster empathy, support, and compassionate communities. Materials and Methods: This qualitative content analysis involved purposive sampling of 30 participants, including AI experts, social scientists, religious leaders, developers, and users. Semi-structured interviews were conducted until theoretical saturation was reached. Data were analyzed using Granheim and Lundman’s method. Trustworthiness was ensured using Lincoln and Guba’s criteria: credibility (via member checking), transferability (detailed contextual descriptions), dependability (audit trail), and confirmability (researcher reflexivity and documentation). Results: Five key themes emerged: emotional well-being, social justice, behavioral change, ethical responsibility, and education. AI fosters prosocial behavior, enhances social inclusion, and promotes sustainable relationships. Conclusion: When ethically and thoughtfully applied, AI can support and complement, but not replace, human relationships, particularly in promoting kindness and prosocial behaviors. This approach can strengthen social cohesion, enhance public health, and improve community well-being. Explicitly acknowledging AI’s supportive role reinforces the manuscript’s ethical positioning and conceptual clarity.

Keywords

artificial intelligence kindness public health social cohesion equity

Introduction and objective

Spreading kindness within society benefits not only others but also individuals and communities themselves. A growing body of research links the promotion of kindness to reduced stress, improved immune function, enhanced happiness, and better mental health (Boulter et al., 2023; Otake et al., 2006; Shoxrux, 2023). Kind actions foster belonging, strengthen social bonds, and reduce isolation through volunteering, empathy, and prosocial behavior (Rowland, 2018; Yusuf, 2020). Observing acts of kindness also inspires imitation, generating a positive ripple effect that contributes to more compassionate and cohesive communities (Hui et al., 2020, 2024; Wei, 2023).

Before exploring how Artificial Intelligence (AI) may contribute to promoting kindness, it is necessary to clarify the study’s key concepts. Kindness refers to intentional behaviors and attitudes that enhance empathy, social connection, and collective well-being through prosocial and ethical engagement (Layous et al., 2013; Post, 2014). Artificial Intelligence (AI), in turn, denotes systems capable of performing cognitive tasks such as learning, reasoning, and problem-solving (Le et al., 2022; Shalev-Shwartz and Ben-David, 2014). In this research, AI is conceptualized not merely as a technical tool but as a sociotechnical system that can support and scale kindness-promoting practices across diverse cultural and organizational contexts.

Recent literature underscores the centrality of kindness across multiple sectors, with Greco et al. (2025) highlighting its importance in healthcare (Greco et al., 2025), while additional studies demonstrate its critical role in education (Flanagan et al., 2025; Macfarlane and Carson, 2023; O'Connor et al., 2025) and community health (Helliwell et al., 2025; Shillington et al., 2023; Slavich et al., 2022; Sun et al., 2021), where compassionate interactions contribute to improved outcomes, engagement, and resilience (Hui et al., 2020).

Global and local initiatives—such as the Random Acts of Kindness Foundation, Kindness.org, and the International Kindness Movement—seek to institutionalize kindness through education, digital engagement, and policy advocacy (Lawrence, 2021; Nawawi et al., 2022). Such initiatives underscore that kindness can be cultivated systematically through learning, practice, and collective commitment.

Parallel to these human-centered efforts, emerging evidence suggests that AI technologies can amplify the reach and impact of kindness-based initiatives. AI-driven platforms generate educational and motivational content, support digital coaching on empathy, and facilitate compassionate communication in workplaces (Buttol, 2023; Grant and Pittaway, 2024; Lettieri, 2025; Munn and Matthews, 2024). Integrating kindness-oriented design principles in AI can enhance human–machine relationships and foster ethical and empathetic digital environments (Buttol, 2023; Ray, 2018; Svenson, 2022; Unwin, 2018).

Despite growing scholarly interest, the potential of AI as a catalyst for kindness, equity, and prosocial transformation remains underexplored. Existing research has largely focused on technical or economic applications, with limited qualitative inquiry into its social and ethical dimensions.

This study aims to fill this gap by qualitatively exploring the multifaceted ways in which Artificial Intelligence can foster kindness within society—advancing well-being, equity, and social cohesion across individual, organizational, and community levels.

Materials and methods

This qualitative study was rigorously designed and conducted in Tehran using a content analysis approach, following ethical approval from Iran University of Medical Sciences. To address the complex and multifaceted nature of the research question, a qualitative design was chosen because it allows for deep exploration of participants’ perspectives and meanings around the role of Artificial Intelligence (AI) in promoting kindness in society, which quantitative methods alone may not capture.

Participants were purposefully selected from a diverse group to ensure comprehensive representation of views and experiences. This group included specialists and researchers in artificial intelligence, social science consultants, religious leaders and clergy, AI developers and engineers, social activists and NGO representatives, technology users, and members of online communities. This maximum variation sampling strategy ensured diversity across age, gender, education level, and occupation, thereby enhancing the richness and transferability of the findings.

Inclusion criteria were clearly defined: participants had to be over 18 years old, willing to participate, and able to respond meaningfully to interview questions. Sampling continued until data saturation was reached, which was carefully assessed through ongoing iterative analysis. Specifically, saturation was considered achieved when no new themes or codes emerged in three consecutive interviews, and this decision was made collaboratively by the research team after extensive discussion and review of emerging data.

To ensure comprehensive and rigorous data saturation, a systematic approach was employed throughout the data collection and analysis phases. Data saturation was assessed iteratively by the research team through regular team meetings, where preliminary findings were reviewed, and new themes were identified or validated. Interim analyses were conducted at multiple stages of data collection, allowing the research team to assess whether additional interviews were yielding novel insights or if themes were becoming repetitive. In addition, the researchers maintained detailed records of these interim analyses to document the ongoing assessment of saturation. This iterative process, combined with member checking and peer debriefing, helped confirm that saturation was reached and that the data analysis was sufficiently robust and comprehensive. Consequently, the judgment of saturation was supported by a transparent, methodologically rigorous approach, enhancing the credibility and reliability of the study findings.

Interviews were conducted face-to-face and were semi-structured (rather than unstructured) to balance consistency and flexibility. The interview guide, developed based on relevant literature and pilot testing, consisted of open-ended questions designed to elicit detailed and reflective responses. To ensure transparency and reproducibility, the interview guide and coding framework are provided in the supplemental materials. This enables other researchers to replicate or build upon this study.

Before starting each interview, the interviewer provided a brief demographic survey collecting data on age, gender, occupation, education level, and marital status. All interviews were audio-recorded with the participants’ permission to allow for accurate transcription and analysis. Follow-up prompts such as “Could you elaborate?” or “Can you give an example?” were used to deepen the conversation and obtain richer insights.

To minimize researcher bias, questions were formulated using simple, neutral language to avoid leading participants toward predetermined answers. The central guiding question was: “In what ways can artificial intelligence contribute to the promotion of kindness in society?” This focus was maintained throughout the interviews while allowing flexibility to explore unexpected but relevant themes.

After data collection, the audio recordings were transcribed verbatim by the interviewer, who reviewed them multiple times at slower speeds when necessary to ensure accuracy. The data were then analyzed using the content analysis approach described by (Graneheim and Lundman, 2004), which allows for systematic interpretation of both explicit and latent content in textual data. This method was chosen due to its proven rigor and suitability for identifying nuanced patterns and themes in qualitative data. The interview guide and coding framework used in this study are provided in the supplemental materials for transparency and reproducibility of the research process.

To ensure trustworthiness and rigor, the following strategies were applied:

Credibility: Semi-structured interviews captured diverse perspectives; data were reviewed meticulously to include only relevant information closely aligned with research aims, ensuring internal consistency.

Dependability: A two-stage coding process was used—initial codes were derived independently from the data, followed by collaborative review and refinement within the research team. This inter-coder agreement process mitigated individual biases and enhanced reliability (Lincoln and Guba, 1985).

Transferability: Detailed documentation of sampling, data collection, and analysis procedures was maintained to enable replication and application of findings in other contexts.

Confirmability: Member checking was performed by returning analyzed transcripts to participants for validation of interpretations. Ongoing communication was maintained to resolve ambiguities, strengthening the objectivity of findings (Lincoln and Guba, 1988).

Authenticity: Researchers ensured that all reported data reflected participants’ genuine experiences without distortion. Irrelevant or potentially misleading information was excluded to maintain clarity and integrity.

Lastly, this study adhered to ethical principles rigorously. Written informed consent was obtained from all participants before data collection. They were fully informed both verbally and in writing about the study’s purpose, procedures, confidentiality, and their rights, including voluntary participation and withdrawal without consequences.

Results

This study examined AI’s role in promoting kindness within society, involving 30 participants from fields such as AI, social sciences, technology, and social activism. Participants were 60% male and 40% female, ranging in age from 22 to 65 years, with the majority holding advanced degrees. The sample included professionals, social activists, and technology developers. This diversity contributed to a holistic understanding of AI’s potential in fostering kindness and social cohesion. Table 1 presents the participants’ demographic characteristics.

Table 1.

Demographic characteristics of interview participants.

Demographic characteristic	Description	Percentage (%)
Age	22–30 years: 5 participants	16.67
	31–40 years: 9 participants	30
	41–50 years: 7 participants	23.33
	51–65 years: 9 participants	30
Gender	Males: 18 participants	60
Gender	Females: 12 participants	40
Education	Master’s or Ph.D. degrees in relevant fields (AI, social sciences, etc.): 20 participants	66.67
Occupation	AI experts: 6 participants	20
	Social scientists: 5 participants	16.67
	Religious leaders: 4 participants	13.33
	AI developers: 5 participants	16.67
	Social activists: 6 participants	20
	Technology users: 4 participants	13.33

Data saturation was achieved after 30 interviews. The analysis identified five main themes related to AI’s role in promoting kindness, each representing a distinct dimension of how AI can influence empathy, ethical conduct, and supportive social interactions. Table 2 summarizes these themes along with their associated concepts and sub-concepts.

Table 2.

Main concepts and sub-concepts on the role of artificial intelligence in promoting kindness in society.

Code	Sub-themes	Connection to kindness	Risks
1. AI for emotional and psychological well-being	• Promoting empathy and emotional support • Facilitating peer support networks • AI-powered mental health chatbots • Reducing loneliness and social isolation • Telemedicine and remote psychological services • Emotional awareness in Human-AI interaction • Personalized mental health recommendations • Enhancing psychological resilience through AI • Potential risks of AI in mental health: Privacy concerns and dependence	This theme emphasizes AI’s role in fostering empathy, emotional support, and combating loneliness. These aspects directly promote kindness by enhancing emotional well-being and creating a compassionate environment for individuals	Emotional dependency on AI systems, reducing reliance on human support
			Privacy concerns regarding sensitive mental health data
			Inaccurate or inappropriate recommendations by AI, potentially harming users
			Reduced human empathy if AI interactions replace interpersonal contact
			Overconfidence in AI solutions leading to neglect of professional care
			Potential bias in AI algorithms affecting certain demographic or cultural groups
2. AI for social Inclusion and equity building	• Building inclusive digital communities • AI tools for supporting refugees and migrants • Enhancing accessibility for people with disabilities • Bridging the digital divide in underserved regions • Promoting language equity through AI translation • Gender and cultural inclusivity in AI systems • Fair representation of marginalized voices in AI design • AI for supporting disadvantaged social groups in crisis situations • AI systems’ impact on social stratification: Risks and biases	By promoting social inclusion, fairness, and equity, this theme directly contributes to kindness by ensuring that marginalized groups are treated with dignity and respect, fostering a more just and compassionate society	Bias in algorithms leading to unfair treatment
			Underrepresentation of marginalized groups in data
			Reinforcement of social inequalities through flawed AI design
			Exclusion of vulnerable populations due to digital divides
			Potential misuse of AI that deepens social stratification
			Lack of cultural sensitivity and inclusivity in AI systems
3. AI for behavioral and Normative Transformation	• Promoting kindness and positive online interaction • Encouraging altruism and compassionate action • AI-Driven nudges for ethical behavior • Gamification of kindness and positive habits • AI moderation to reduce toxicity and harassment • Reinforcement of socially desirable norms • Supporting moral development through AI • AI for social norm shaping: balancing ethical influence and autonomy • Risks of reinforcing harmful norms through AI algorithms	This theme directly promotes kindness by shaping positive social behaviors, reducing toxicity, and encouraging altruistic actions, creating a culture where kindness and compassion are prioritized	Manipulation of user behavior undermining personal autonomy
			Reinforcement of harmful or biased social norms
			Ethical concerns about nudging and behavioral control
			Risk of reducing genuine human empathy and spontaneity
			Potential for AI-driven social conformity suppressing diversity
			Unintended consequences of algorithmic moderation and censorship
4. AI for ethical Responsibility and sustainability	• Promoting environmental awareness through AI • encouraging sustainable consumption and travel • Ethical decision-making in AI algorithms • AI for reducing discrimination in institutions • Transparency and accountability in AI systems • AI for monitoring and managing environmental impact • Empowering communities to act sustainably through AI • Ethical and transparent use of AI in environmental management • AI for sustainable development: balancing technological advancements and ethical concerns	By promoting sustainability, ethical decision-making, and transparency, this theme contributes to kindness by encouraging responsible practices that benefit both people and the planet, fostering global solidarity and compassion	Greenwashing and superficial claims of sustainability
			Lack of transparency in AI decision-making processes
			Ethical dilemmas in balancing technological progress and environmental impact
			Potential reinforcement of existing inequalities through biased AI governance
			Insufficient accountability for AI’s environmental footprint
			Risk of neglecting marginalized communities in sustainability initiatives
5. AI for education and Human Capacity building	• Personalized learning platforms • AI for supporting neurodiverse learners • AI-Enhanced language learning and translation • Career guidance and skill development systems • Gamified educational environments • Expanding educational access in remote areas • Adaptive learning for lifelong education • AI for expanding access to higher education for underserved populations • Ethical challenges in AI education tools: Privacy and data security	This theme promotes kindness by enhancing educational opportunities for all, particularly marginalized and underserved populations, and by fostering inclusive learning environments that respect individual needs and development	Marginalization of certain learner groups, especially neurodiverse and underserved populations
			Privacy and data security concerns in AI-driven educational tools
			Potential biases in AI algorithms affecting fair access and evaluation
			Over-reliance on AI reducing human interaction and personalized mentorship
			Unequal access to AI educational resources, exacerbating digital divides
			Ethical challenges related to transparency and consent in adaptive learning systems

Drawing on diverse perspectives from AI development, education, psychology, social activism, and religious leadership, the findings illustrate how participants perceive AI as a supportive tool capable of enhancing compassion, empathy, and prosocial values. While each theme reflects a unique aspect of AI’s influence on human flourishing, together they highlight AI’s potential to strengthen social inclusion, encourage ethical behavior, and promote meaningful interpersonal connections.

Participants also noted several ethical considerations that accompany AI’s growing presence in emotionally sensitive and socially influential contexts. These concerns—such as privacy vulnerabilities in mental-health applications, bias and underrepresentation in data and design, risks to user autonomy in behavior-shaping systems, superficial sustainability claims, and unequal access in AI-enhanced learning—underscore the importance of thoughtful and responsible implementation. These issues are discussed within the analysis of each theme in the following sections.

Overall, the themes presented in this section demonstrate the multifaceted ways AI can contribute to empathy, social cohesion, and equitable well-being when developed and applied ethically. By foregrounding human values and prioritizing transparent, inclusive design processes, AI can complement—rather than replace—human capacities for kindness and compassion, supporting healthier and more resilient communities.

AI for emotional and psychological well-being

Participants emphasized the growing potential of AI to support mental health, emotional resilience, and psychological care. From mental health chatbots to emotionally intelligent interfaces, AI was seen as a tool that could foster empathy, reduce loneliness, and provide timely support—especially in settings where human resources are scarce.

One AI developer in his 30 s noted,

“What surprised me was how emotionally responsive the AI system felt. It didn’t just give generic answers—it reflected my mood. That alone made me feel heard.”

Others highlighted the role of AI in enabling peer support and forming digital communities of care. A young female activist explained,

“The app connected me with others going through similar challenges. It’s not just about the AI—it’s how it creates human connections through smart matching.”

AI-powered mental health tools were frequently cited as reliable, non-judgmental companions. A participant in his 40 s shared:

“The chatbot became my go-to during sleepless nights. It helped calm me down more than I expected.”

In several cases, AI was seen as an effective tool for reducing social isolation, especially among the elderly. A woman in her 30 s explained how her father regularly interacted with an AI voice assistant,

“He even jokes with it. It may seem trivial, but for him, it really helps.”

Participants also valued the emotional sensitivity of AI systems. A female AI specialist noted,

“The AI could detect emotional cues from my tone and adapt its responses—it felt far more human.”

Finally, personalized mental health suggestions were appreciated by users. One young man shared,

“After a few sessions, the system started recommending exercises that really matched my needs. It felt like it understood me.”

In summary, participants saw AI not only as a support system for mental well-being, but as a facilitator of emotional connection, inclusion, and psychological care.

AI for social inclusion and equity building

This theme highlights AI’s potential to empower marginalized communities, foster accessibility, and promote equitable participation across social and digital platforms—when designed with inclusivity in mind.

Participants emphasized the importance of inclusive digital spaces. A female social activist in her 20 s remarked,

“Digital spaces claim to be open to all, but without intentional design, many are still excluded. AI can help build platforms where everyone feels seen and heard.”

AI’s practical role in humanitarian settings was also discussed. A male social worker said,

“The translation feature helped me communicate with refugees in their native languages, making them feel welcomed and understood.”

Assistive AI technologies were noted for enhancing independence among people with disabilities. A tech worker in her 30 s shared,

“Voice navigation and visual recognition tools changed how my students with disabilities engage with the world.”

Many participants highlighted AI’s ability to bridge the digital divide. A developer in his 50 s said,

“In regions lacking teachers or materials, AI-powered platforms can democratize education and opportunity.”

Language equity was another key point. A multicultural educator explained,

“AI translation allows students to speak in their native language and still fully participate. That’s powerful inclusion.”

Some interviewees also raised the importance of cultural and gender diversity in AI design. A religious leader warned,

“Technology should reflect the values we aim for—not replicate the biases we’re trying to overcome.”

Concerns about representation in data and design were shared. A social scientist noted,

“If AI is trained only on dominant perspectives, it erases everyone else. Inclusion starts with who builds the system.”

In sum, participants agreed that AI must go beyond neutrality and act as a force for inclusion—ensuring that its development and application reflect the diverse realities of all communities.

AI for behavioral and normative transformation

Participants viewed AI as a potential catalyst for promoting ethical behavior, prosocial norms, and kindness in both online and offline contexts. The use of AI to influence individual and collective behavior was seen as a transformative opportunity—provided it is deployed ethically.

A recurring theme was the role of AI in encouraging positive digital interactions. A male AI expert in his 30 s explained,

“AI can create spaces that reward kindness, where users feel encouraged to be positive. It helps break the cycle of negativity we often see online.”

Many participants also emphasized AI’s capacity to promote compassion and altruism by sharing inspiring content or nudging users toward prosocial actions. A female social scientist in her 40 s remarked,

“When AI systems highlight acts of kindness, they help normalize caring for others—it becomes expected, not exceptional.”

The idea of behavioral nudges was also widely discussed. A senior AI developer shared,

“AI can subtly guide people toward more ethical decisions, like encouraging long-term thinking over short-term gratification.”

Several participants highlighted the effectiveness of gamification in encouraging positive behavior. A female activist noted,

“Rewarding people for small acts of kindness—through points or badges—motivates ongoing engagement in doing good.”

AI-powered moderation tools were also seen as essential in reducing toxicity and harassment online. A tech-savvy participant in his 20 s said,

“AI moderation that detects and addresses harmful behavior before it escalates is crucial for creating safe spaces.”

Furthermore, AI was seen as a tool to reinforce moral and social norms. A female religious leader explained,

“When AI reflects values like respect and dignity, it can serve as a moral compass in digital interactions.”

Lastly, the role of AI in supporting ethical and moral development was viewed as a long-term benefit. An older participant noted,

“AI can teach ethical reasoning and support people in becoming more empathetic and fair—not just smarter.”

Overall, this theme highlights AI’s potential to help shape a more respectful, compassionate, and ethically grounded society—so long as it does not cross into manipulation or undermine human autonomy.

AI for ethical responsibility and sustainability

This theme focuses on how AI can promote ethical governance, environmental sustainability, and social justice. Participants acknowledged AI’s potential to support systemic change, but stressed the need for transparency, fairness, and accountability.

A key area was AI’s role in environmental awareness. A developer in his 40 s said,

“AI helps us analyze environmental data and identify trends. With better insights, we can make real changes to reduce our carbon footprint.”

Participants also highlighted AI’s ability to promote sustainable consumption and travel. A female activist noted,

“AI platforms can recommend eco-friendly choices and raise awareness about personal environmental impact. It’s about empowering conscious decisions.”

However, concerns about the ethics of AI algorithms were also expressed. A male social scientist stated,

“Algorithms reflect the biases of their creators. If we don’t build fairness into the system, we risk reproducing inequality at scale.”

Several participants emphasized AI’s potential to reduce discrimination in institutions. A religious leader explained,

“If used wisely, AI can eliminate human bias in hiring or legal decisions. It can ensure fairness where people might fall short.”

Transparency and accountability were seen as critical. A participant in his 60 s with expertise in AI ethics shared,

“People need to know how AI systems work and how decisions are made. Without transparency, there’s no trust.”

Others focused on AI’s ability to support real-time environmental monitoring. A tech expert noted,

“AI can track deforestation or air pollution as it happens. That level of insight helps us act before it’s too late.”

Finally, the democratizing power of AI was emphasized. A social activist said,

“AI isn’t just for governments or corporations. Local communities can use it to monitor resources or make sustainable choices themselves.”

In conclusion, participants saw AI as a powerful tool for ethical transformation and sustainability—if it is developed with inclusivity, transparency, and justice at its core.

AI for education and human capacity building

Participants recognized AI’s potential to revolutionize education by offering personalized learning, expanding access to underserved populations, and building lifelong skills relevant to the 21st century.

Personalized learning was widely praised. A female AI expert in her 30 s said,

“AI can adapt content to each student’s pace and style. That level of personalization is a game-changer.”

Several participants focused on supporting neurodiverse learners. A male educator shared,

“AI tools help students with ADHD or autism stay engaged. That can level the playing field for them.”

Language learning was another area where AI showed great promise. A young teacher noted,

“Students can practice with AI in real-time. It’s like having a language partner available 24/7.”

Career guidance and skills development systems were also highlighted. A developer remarked,

“AI can analyze job market trends and guide students toward in-demand careers. That’s real-world value.”

Participants appreciated the use of gamification in education. A social activist said,

“When learning is fun and interactive, students are more motivated—and they retain knowledge better.”

In underserved and remote areas, AI was seen as a major equalizer. A participant in international development explained,

“AI brings quality education to places with few teachers. It’s how we bridge global educational gaps.”

Finally, the potential of AI for lifelong learning was emphasized. A social scientist said,

“AI helps adults continue learning, upskill for new jobs, or explore personal growth—even long after formal education ends.”

Overall, participants saw AI as a tool for not just delivering content, but for empowering learners, reducing barriers, and enhancing human potential—as long as issues of bias, data privacy, and inclusion are carefully addressed.

Conceptual model of artificial Intelligence’s role in fostering compassion and social cohesion

This conceptual framework demonstrates how Artificial Intelligence (AI) can serve as a transformative catalyst for advancing social well-being, equity, and sustainable development—key pillars in nurturing compassion and kindness within society. The model comprises five interconnected domains, each highlighting significant opportunities as well as potential risks identified by study participants:

AI for emotional and psychological well-being

This domain focuses on emotional support, mental health, and enhancing social connectedness. AI has the capacity to foster empathy, reduce loneliness, and offer personalized mental health assistance. However, risks include emotional dependence on AI, privacy issues, potential inaccuracies in recommendations, diminished human empathy, over-reliance on technology, and biases affecting specific demographic groups.

AI for social inclusion and equity

AI contributes to bridging societal divides, promoting fairness, and ensuring equitable access to opportunities across different socioeconomic and marginalized groups. This supports social cohesion by empowering disenfranchised populations. Nevertheless, algorithmic biases, underrepresentation of some communities, and inadvertent reinforcement of social inequalities remain key challenges.

AI for behavioral and normative transformation

By encouraging prosocial behaviors and ethical norms, AI can reduce online toxicity, incentivize kindness, and foster positive interactions. Risks here involve manipulation of individual autonomy, reinforcement of harmful social norms, and ethical concerns surrounding behavior-modifying algorithms.

AI for ethical responsibility and sustainability

This dimension situates AI within broader societal and environmental frameworks, promoting responsible innovation, sustainable consumption, and environmental stewardship. Risks include superficial sustainability claims (greenwashing), ethical oversights in AI application, and a lack of transparency or accountability mechanisms.

AI for education and human capacity building

AI enhances access to knowledge and skills, supporting diverse learners including neurodiverse individuals and underserved populations. Potential risks involve marginalization of specific learner groups, privacy and data security concerns in educational technologies, and unequal access that may exacerbate existing disparities.

Together, these interrelated domains underscore AI’s potential to enhance empathy, inclusion, ethical conduct, and human development, while simultaneously acknowledging and addressing associated risks. By balancing opportunities with challenges, AI can act as a complementary tool to human kindness, fostering compassionate, equitable, and sustainable communities.

Figure 1 depicts this conceptual model, based on insights from the qualitative study “The Role of Artificial Intelligence in Promoting Kindness in Society.”

Figure 1.

Conceptual model illustrating the interconnected domains of artificial intelligence in promoting kindness in society as a complementary tool to human kindness and empathy.

This conceptual model presents an integrated framework illustrating how Artificial Intelligence (AI) can be effectively utilized to foster kindness, empathy, inclusivity, and social cohesion in society. Central to this framework is the conceptualization of AI as a catalyst for compassionate and prosocial transformation. The model highlights five interconnected domains where AI contributes significantly to individual and collective well-being:

AI for emotional and psychological well-being

Focusing on enhancing mental health, emotional resilience, and social connectedness through personalized support and empathetic AI interactions.

AI for social inclusion and equity

Addressing systemic barriers by promoting equitable access, amplifying marginalized voices, and bridging digital divides.

AI for behavioral and normative transformation

Shaping prosocial behaviors and ethical norms via behavioral nudges, gamification, and content moderation.

AI for ethical responsibility and sustainability

Promoting transparent, accountable, and ethically sound AI practices aligned with social justice and environmental stewardship.

AI for education and human capacity building

Expanding access to personalized learning, supporting neurodiverse and underserved learners, and fostering lifelong empowerment.

While the model emphasizes these opportunities, it acknowledges the presence of complex risks and challenges associated with AI applications, such as privacy concerns, algorithmic biases, potential over-reliance on technology, exclusion of vulnerable groups, and ethical dilemmas. These risks are comprehensively detailed in the accompanying table (Table 1) and must be carefully managed to ensure AI’s positive social impact.

Together, these dimensions position AI not merely as a set of technical tools, but as a catalyst for transformative social impact—fostering empathy, equity, and global responsibility while explicitly recognizing potential risks.

An alternative framing highlights four overarching, interdependent processes, incorporating both opportunities and associated risks:

Enhancing Empathy and Social Support (with attention to dependency and privacy concerns)

Supporting Fairness and Social Inclusivity (mitigating bias and exclusion risks)

Promoting Prosocial Behavior and Kindness (while safeguarding autonomy and ethical integrity)

Encouraging Responsibility and Global Sustainability (ensuring transparency, accountability, and avoidance of greenwashing)

These processes form a continuous, mutually reinforcing cycle: empathy fosters kindness, which promotes fairness and inclusion, ultimately driving global ethical responsibility. Each domain strengthens the others, reflecting a holistic, human-centered view of AI’s potential for societal transformation. Figure 2 illustrates this enhanced conceptual model derived from the study.

Figure 2.

A cyclical conceptual model illustrating the dynamic interplay between empathy, prosocial behavior, inclusivity, and global responsibility in fostering compassionate and sustainable societies.

Discussion

The findings of this study reveal that artificial intelligence (AI) plays a multifaceted and transformative role in fostering kindness and prosocial behavior across five key thematic domains: AI for Emotional and Psychological Well-being, AI for Social Inclusion and Equity Building, AI for Behavioral and Normative Transformation, AI for Ethical Responsibility and Sustainability, and AI for Education and Human Capacity Building. Together, these domains highlight how AI can act as a sociotechnical catalyst, supporting both individual well-being and collective social progress.

AI for emotional and psychological well-being

A central contribution of AI, as identified in this study, lies in its capacity to support emotional and psychological resilience — an essential foundation for kindness. AI-driven conversational agents and chatbots can provide empathetic, non-judgmental support, helping users feel heard and validated. For instance, some users rated AI-generated empathic responses more favorably than human ones (Ovsyannikova et al., 2025), and studies show measurable reductions in loneliness and anxiety among students using AI-based support systems (Kim et al., 2025).

Nevertheless, this capacity raises complex ethical questions. While AI can convincingly simulate empathy, it lacks genuine emotional experience or consciousness, which means its role should remain complementary—not substitutive—to human interaction. Overreliance on AI for emotional comfort could risk emotional detachment or weakened human empathy if safeguards are not in place. Future AI development must therefore prioritize transparency, human oversight, and protection of users’ emotional autonomy (Kasula, 2023; Shen et al., 2024).

AI for behavioral and normative transformation

AI also demonstrates the ability to shape social norms and ethical behaviors. In this study, evidence suggested that AI agents designed to model or reinforce prosocial actions can reduce in-group bias and foster intergroup cooperation (Comes, 2024). For example, in gaming environments, AI participation increased reciprocity and cooperative kindness among human players (Vistorte et al., 2024).

However, the phenomenon of “AI-induced indifference”—where exposure to unfair or biased AI decisions reduces people’s willingness to act kindly—highlights potential risks (Bankhwal et al., 2024). This underscores the need for ethically grounded AI design that upholds justice and avoids unintended erosion of trust. Moreover, the fine line between encouraging kindness and manipulating moral behavior through AI nudges requires critical ethical reflection. AI-driven nudges must operate with transparency and consent to preserve individual autonomy and respect for moral diversity (Engelen and Schmidt, 2020).

AI for social inclusion and equity building

This domain underscores AI’s capacity to address systemic inequities and enhance social justice. When designed with fairness in mind, AI systems can mitigate biases in healthcare, finance, and education (Kerry et al., 2021; Renieris et al., 2022). Fairness-aware algorithms can rebuild trust in institutional decision-making and promote social cohesion.

Yet, persistent challenges remain. Biases embedded in training data, institutional norms, or algorithmic processes can perpetuate discrimination if not actively monitored (Tano and Okolo, 2024). In education, inclusive AI models can enhance access for marginalized learners, but they must also be culturally adaptive to avoid reinforcing exclusionary practices (Bostrom and Yudkowsky, 2018). AI’s contribution to equity therefore depends on ongoing ethical vigilance, interdisciplinary collaboration, and transparent governance.

AI for education and human capacity building

The study findings highlight AI’s role in nurturing kindness through emotionally responsive education. AI systems using affective computing can recognize and adapt to students’ emotional states, fostering motivation, empathy, and social connectedness (Ojedeji & Adejuwon, 2024). Such emotionally attuned learning environments can strengthen not only cognitive growth but also social-emotional intelligence, a key driver of kindness.

Beyond classrooms, AI-human collaboration in workplaces has been associated with improved teamwork, creativity, and well-being (Altinay et al., 2024; Sey and Mudongo, 2021). This synergy shows how AI, when integrated thoughtfully, can amplify human capacities for empathy, cooperation, and innovation.

However, ethical safeguards regarding data privacy, surveillance, and bias are essential. Educational AI must respect cultural diversity, learner autonomy, and social equity, ensuring that technological tools promote inclusion rather than replicate existing inequalities.

AI for ethical responsibility and sustainability

AI’s contribution to ethical responsibility and sustainability highlights its broader societal relevance. From environmental monitoring and smart resource management to corporate social responsibility initiatives, AI-driven systems can support the UN Sustainable Development Goals (Camilleri, 2024). International frameworks such as the EU Green Deal and G7 initiatives illustrate how AI can be aligned with human rights and democratic governance (Commission, 2019; Floridi and Cowls, 2022; Maremonti, 2024).

Corporations integrating AI into Environmental, Social, and Governance (ESG) strategies — such as H&M’s sustainability programs — demonstrate AI’s capacity to balance profit with ethical impact (Carlsson, 2023; Kornberger et al., 2025; Singh et al., 2024). UNESCO’s advocacy for global AI ethics frameworks further emphasizes the need to minimize bias, ensure transparency, and protect vulnerable communities (Ramos, 2023; Unesco, 2022).

Yet, these benefits depend on accountable implementation. Without clear ethical oversight, AI may enable “greenwashing” or deepen global inequalities. Sustainable AI therefore requires responsibility, inclusivity, and equity at every level of design and deployment.

Overall, this study highlights the remarkable potential of artificial intelligence (AI) to foster empathy, social support, and kindness across multiple domains, including emotional and psychological well-being, education and human capacity building, ethical responsibility, and social inclusion. When designed and implemented with human-centered ethical principles, AI can strengthen social cohesion, promote prosocial norms, and encourage compassionate innovation within communities. The study demonstrates that AI is not merely a technological tool but a transformative social agent capable of supporting individual well-being, enhancing social relationships, and advancing ethical and sustainable practices.

Nevertheless, several limitations must be acknowledged. First, this research relied on qualitative insights from a limited sample of 30 participants, which may constrain the generalizability of the findings, particularly across diverse cultural, regional, and demographic contexts. Although purposive and diverse sampling was employed, future studies should incorporate larger, cross-cultural samples to better assess AI’s global applicability in promoting kindness.

Second, the study’s findings depend on the researchers’ interpretations during data analysis, which, despite the use of collaborative coding, member checking, and triangulation, may introduce interpretive bias inherent to qualitative research.

Third, the rapid evolution of AI technologies means that the ethical, emotional, and social implications of human–AI interactions are dynamic and require longitudinal examination to capture their evolving impact over time.

Finally, while this study emphasizes the positive contributions of AI to kindness and social cohesion, it did not fully explore potential negative consequences, such as the reinforcement of biases, inequities, or the risk of diminishing genuine human empathy. Future research should adopt a balanced approach, integrating both the opportunities and challenges of AI, and employ participatory and culturally sensitive design strategies to ensure AI-driven kindness aligns with diverse human values and lived experiences.

In sum, while this study provides significant insights into AI’s capacity to promote prosocial behavior and compassionate social structures, its conclusions should be interpreted with these methodological and contextual limitations in mind.

Conclusion

The findings of this qualitative study demonstrate that artificial intelligence (AI) holds considerable potential to foster kindness, empathy, and social cohesion within society. The results highlight AI’s capacity to enhance emotional support, promote prosocial behavior, and contribute to the development of more compassionate and inclusive communities. In particular, AI’s role in supporting mental health outcomes and facilitating empathetic interactions emerges as a key contribution, while it should be emphasized that AI is intended to complement, not replace, human relationships.

When designed and deployed responsibly, AI can strengthen positive social behaviors and norms, but potential risks—such as reduced human empathy arising from biased or unjust systems—must be carefully managed. This underscores the necessity of ethical design, fairness, transparency, and cultural sensitivity to prevent reinforcement of harmful biases or social inequities.

The study identifies five core domains through which AI can support these goals: AI for Emotional and Psychological Well-being, AI for Social Inclusion and Equity Building, AI for Behavioral and Normative Transformation, AI for Ethical Responsibility and Sustainability, and AI for Education and Human Capacity Building.

Collectively, these domains illustrate how AI can enhance individual well-being, strengthen social relationships, promote ethical norms, and foster inclusive and sustainable communities.

Furthermore, the findings emphasize AI’s critical role in addressing systemic biases, expanding equitable access to opportunities, and supporting global initiatives in justice, sustainability, and international cooperation. This global perspective ensures that AI not only promotes kindness but also avoids inadvertently reinforcing inequalities or cultural tensions.

In conclusion, ethically and responsibly developed AI can serve as a powerful enabler of kindness, empathy, justice, and social well-being—both at local and global scales. To realize this potential, developers, policymakers, and stakeholders must collaborate to design AI systems that are effective, fair, inclusive, and aligned with universal ethical principles, while explicitly acknowledging AI’s supportive, complementary role in human relationships.

Supplemental material

Supplemental material - The role of artificial intelligence in promoting kindness in society: A qualitative study to advance well-being, equity, and positive social change

Supplemental material for The role of artificial intelligence in promoting kindness in society: A qualitative study to advance well-being, equity, and positive social change by Ali Ebrahimyan, Morteza Mansourian, Sheida Vahidi, Fatemehsadat Alavi in Health Psychology Open.

Footnotes

Acknowledgments

The authors would like to express their sincere gratitude to all the participants who generously shared their time and insights during this study. We also thank the faculty and staff of the School of Public Health at Iran University of Medical Sciences for their valuable support throughout the research process.

ORCID iD

Fatemehsadat Alavi

Ethical considerations

This research project was approved by the Research Committee for Health Promotion Studies at the School of Public Health and received ethical approval from the Ethics Committee of Iran University of Medical Sciences with the ethics code IR.IUMS.REC.1402.709. The project tracking code is 26030, and the approval was granted on 14 November 2023 (23/08/1402 in the Iranian calendar).

Consent to participate

Informed consent was obtained from all participants involved in this study. Participants were fully informed about the purpose, procedure, potential risks, and benefits of the study, and they voluntarily agreed to participate. The study adhered to ethical principles and respected the rights of all participants.

Consent for publication

All participants provided informed consent for the publication of the results of this study. They were assured that their identities would remain anonymous and that the data would only be used for the purposes of this research.

Funding

The authors received no financial support for the research, authorship, and/or publication of this article.

Declaration of conflicting interests

The authors declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Data Availability Statement

This was a qualitative study, and due to ethical considerations, the raw data (including interview transcripts) are not publicly available. However, data may be made available from the corresponding author upon reasonable request. The key findings and results derived from the interviews have been included in the published article.*

Supplemental material

Supplemental material for this article is available online.

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