







































Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 
255-266 

255 

 

 

 

Article 

Intelligent collaboration and artistic co-creation: a 

study on the enhancement mechanism of social 

well-being through AI-enabled intergenerational 

integration 

Wanyi He1,2, Anuar Bin Ahmad1*, Nasruddin Yunos3, Bingbing Chen1  

1The National University of Malaysia, Lingkungan Johan, 43600 Bandar Baru Bangi, Selangor, Malaysia 
2North Sichuan College of Preschool Teacher Education, Guangyuan, China 
3Centre for Liberal Studies, Universiti Kebangsaan Malaysia, 43600 UKM Bangi, Selangor, Malaysia 

A R T I C L E   I N F O 
 

Article history: 
Received 10 June 2025  
Received in revised form 
12 August 2025 
Accepted 03 September 2025 
 
Keywords:  
Intergenerational collaboration, 
Artificial Intelligence in the arts, 
Social well-being,  
Triangulated collaboration model 
 
*Corresponding author 
Email address: 
anuarmd@ukm.edu.my 
 
 
DOI: 10.55670/fpll.futech.4.4.21 

A B S T R A C T 
 

This study investigates how AI-enabled intergenerational artistic co-creation 
enhances Social well-being through a mixed-methods approach involving 120 
participants across younger (15-25) and older (65+) age cohorts. The findings 
reveal a novel "triangulated collaboration model" wherein AI functions as both 
creative catalyst and communicative bridge between generations. Empirical 
results demonstrate statistically significant improvements: technological 
engagement convergence increased from 62% to 79% among older adults (p < 
.001), bidirectional knowledge transfer showed 28.7-point gains in cultural 
knowledge and 32.5-point gains in technical proficiency, and creative 
innovation scores improved by 47.2% in intergenerational groups compared to 
22.9-28.6% in age-homogeneous groups. We identify multilevel enrichment 
mechanisms: at the individual (psychological well-being, self-efficacy, 
creativity), relational (communication, empathy, social capital), and 
community (inclusive behavior, community participation, cultural heritage 
preservation) levels. The Intelligent Collaborative Enhancement Model (ICEM) 
is a theoretical model that outlines how technological adaptability, creative co-
construction, and mutual learning form "generative integration spaces." Policy 
implications from this research are for educational, cultural, and social welfare 
policies, considering how the utilization of technological mediation can foster 
strong intergenerational relationships within a more age-diverse society. 

1. Introduction 

The convergence of artificial intelligence (AI) and artistic 

practice has created unprecedented opportunities for cross-

generational collaboration. Baas (2024) examines how AI 

serves as a creative mediator, revealing new artistic 

possibilities while challenging traditional notions of 

authorship and creative agency [1]. Along with such 

technological advancements, Campbell et al.'s (2024) 

systematic review has demonstrated increasing interest in 

the significance of intergenerational relationships to social 

welfare, with intergenerational contact being identified as 

having the potential to bring positive outcomes for children's 

and young people's mental and psychosocial well-being [2]. 

The convergence of AI-facilitated creativity and 

intergenerational engagement does hold some potential to 

foster social welfare through co-created art forms. Davis and 

Ogbanufe (2024) recognize the "looming disruption of 

creative industries brought about by generative AI" as a 

double-edged sword—disrupting current creative habits 

while bringing new opportunities for innovative co-creation 

activities outside the boundaries of convention [3]. They are 

convinced that their work marks the potential for bridging 

technologies grounded in generative AI to enable reconciling 

conflicting knowledge structures, even those alienated by 

intergenerational divergence. Moreover, this disruptive 

promise is also teased apart with sophistication in a number 

of cultural spaces, as one sees in Duester's (2024) 

deconstruction of artificial intelligence and digital art as the 

 

 Open Access Journal 

ISSN 2832-0379 

November 2025| Volume 04 | Issue 04 | Pages 255-266 

https://doi.org/10.55670/fpll.futech.4.4.21 

 

 

Journal homepage: https://fupubco.com/futech 

 

Future Technology 

mailto:anuarmd@ukm.edu.my
https://doi.org/10.55670/fpll.futech.4.4.21
https://fupubco.com/futech


Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

256 

 

new paradigm transforming the world of work in 

contemporary China's world of art [4]. The disruptive effect 

of AI on creative industries extends much beyond the fairly 

mundane issue of artistic production, inserting itself into 

deeper issues of cultural work and the welfare of the creative 

industries' workers. Frost and Stack (2024) talk about the 

interdependent relationship between practitioner well-being, 

cultural practice, and the development of AI, acknowledging 

both potential and anxieties created as technological systems 

become increasingly embedded in work streams of a creative 

kind [5]. Their argument is that properly designed AI systems 

can maximize potential for collaboration and repress negative 

effects on the autonomy and job satisfaction of employees—a 

domain of sheer importance in cross-generational creative 

project management. This research project draws on these 

intersecting strands of inquiry to examine how AI-enabled 

artistic collaboration can act as a catalyst for successful 

intergenerational blending and social welfare enhancement. 

We believe that AI technologies, if properly designed and 

deployed, can act as successful go-betweens for the artistic 

collaboration of the young and the old. Contrary to the anxiety 

in certain discourse that AI will take the place of human 

creativity, AI applications can amplify humans' capacity for 

creative expression and, in the process, offer points of entry 

that are open to a wide variety of participants, irrespective of 

their technical experience or artistic qualifications. This 

capacity to democratize the creative process positions AI-

augmented art as especially apt for intergenerational 

collaborations, in which participants will unavoidably 

possess different degrees of digital literacy and creative 

confidence. 

The current study employs a mixed-methods 

methodology to explore mechanisms by which artistic co-

creation facilitated by AI promotes social well-being in 

intergenerational situations. Through the dual examination of 

the technological affordances that enable creative synergy 

and the social dynamics that evolve through such 

collaborative interactions, we aim to build a comprehensive 

framework for understanding and promoting healthy 

intergenerational relationships in a more interconnected 

world with AI. This study adds to the increasing discourses on 

the social effects of AI in artistic environments, the effects of 

technological integration on well-being in cultural work [5], 

and the possible advantages of intergenerational activities in 

fostering community cohesion and individual growth. This 

study develops and validates a triangulated collaboration 

model explaining how AI facilitates intergenerational creative 

engagement, identifies specific mechanisms through which 

AI-enabled artistic co-creation enhances social wellbeing at 

individual, relational, and community levels, provides 

empirical evidence for the effectiveness of AI-mediated 

intergenerational programs, and offers policy 

recommendations for implementing such programs in 

educational, cultural, and social welfare contexts. 

2. Literature review 

2.1 Intergenerational integration and social well-being 

The intergenerational solidarity concept has come to 
receive important attention in modern social science debates 
as nations are faced with demographic change and social 
fragmentation. Giarrusso and Putney [6] highlight the key 
role played by social workers in enhancing intergenerational 

ties, claiming that organized contact between generations 
significantly supports community resilience and well-being 
for individuals. This approach is consistent with research 
supporting more professional intervention in developing 
positive cross-generational relationships, especially where 
natural intergenerational contact has declined. Empirical 
evidence is highly in favor of the value of intergenerational 
programs. Whear et al. [7] conducted a systematic review 
with extensive research establishing strong positive effects of 
intergenerational programs on the mental health and social 
integration of older adults. Their meta-analysis of 21 
intervention studies revealed significant psychological well-
being improvement, reduced loneliness, and enhanced 
purpose among older individuals receiving systematic 
intergenerational interventions. These are complemented by 
the World Health Organization's Global Intergenerational 
Week initiative [8], which emphasizes the worth of organized 
intergenerational contact to public health benefit, situating 
such contacts as central to well-functioning healthy 
communities and thriving social systems. The environmental 
dimension of intergenerational relationships also 
complicates this landscape. Mallick and van den Berg [9] 
discuss how environmental concerns are a source of 
intergenerational solidarity, in this case among women with 
climate-driven migration opportunities. Their mixed-
methods research concludes that shared environmental 
concerns can initiate successful cross-generational dialogue 
and joint problem-solving, creating space for technology-
supported innovative environments to mitigate 
environmental problems. This research outlines how 
existential concerns can be catalysts in the creation of 
intergenerational relationships founded on shared purpose 
and activity. 

2.2 AI in creative contexts and artistic production 

The integration of artificial intelligence technology into 
the art-making process represents a paradigm shift in the 
creative industries, challenging traditional notions of 
authorship, creativity, and beauty. Latikaa et al. [10] 
employed a two-wave survey study to reveal complex public 
opinion regarding AI art, whose perception was influenced by 
demographic traits, prior exposure to AI, and personal 
concepts of creativity. Their findings indicate that public 
acceptance of AI art remains in flux, with both enthusiasm and 
scepticism existing among different population groups, which 
points to the importance of cautious implementation 
strategies when introducing AI art into intergenerational 
contexts. Theoretically, Messingschlager and Appel [11] 
demonstrate that the degree to which people attribute mental 
capacity to AI systems matters in terms of how much they 
value AI-generated art. Their experimental research indicates 
that anthropomorphic framing of AI systems increases 
audience engagement with and aesthetic appreciation of the 
resulting art—a finding of considerable relevance for 
intergenerational contexts, in which participants can have 
varying assumptions about AI capabilities and limitations 
based on generational experience with technology. This 
research suggests that properly constructed narratives 
concerning AI's contribution to creative activity can increase 
participant engagement across generations. The 
environmental sustainability of AI-supported artistic creation 
is worth exploring in the context of growing environmental 
concerns. Núñez-Cacho et al. [12] explore AI in art from the 
viewpoint of a circular economy, suggesting that 
technological creativity can help with more sustainable 
artistic creation when coupled with environmental principles. 



Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

257 

 

Their systematic review highlights new paradigms for 
reducing the environmental impact of digital art practice and 
maximizing social and cultural value—a focus that aligns with 
intergenerational values of responsibility and environmental 
stewardship. This sustainability aspect intersects with 
broader social initiatives, such as MIT's Responsible AI for 
Social Empowerment and Education program [13], which 
develops guidelines for AI applications based on social good 
and ethics. Policy analyses of culture provide additional 
insights into the institutional conditions for healthy AI 
integration into the arts sector. Herndon and Dryhurst [14] 
demand arts-led models of AI innovation, placing cultural 
values on par with technical innovation. Their comparative 
policy analysis suggests that meaningful integration of artistic 
inputs into the processes of technological development can 
lead to AI systems better aligned to human creative 
imperatives and cultural contexts. This alignment of cultural 
values is particularly relevant in the application of AI to 
heritage environments, as explored by Oates [15] in their 
examination of the impact of AI on heritage institutions. Both 
studies emphasize the importance of leadership in the 
cultural sector to steer AI development pathways that respect 
diverse artistic traditions and practices. 

2.3 AI in organizational and collaborative contexts 

Beyond artistic domains, AI technologies are reshaping 
organizational practice and collaborative processes in 
intergenerational terms. Przegalinska [16] conceives of AI as 
a complement to human creativity, not a replacement, 
formulating theoretical models for thinking through how 
technological capabilities can augment human creative 
expression. This vision offers a fertile ground for AI-enabled 
intergenerational collaboration that extends rather than cuts 
short human agency and creative contribution, opening up 
options for technology-mediated extension of creative work 
between and across generations. To accompany this 
theoretical work, empirical investigations by Murire [17] 
investigate the impact of AI on organizational work habits and 
cultural processes, with attention to the importance of 
congruence between technological capacity and existing 
organizational values. Their multi-case study identifies that 
successful AI implementation depends on context factors like 
organizational background, leadership behaviors, and 
ingrained work routines—concerns similarly relevant to 
designing effective intergenerational arts programs. 
Likewise, Kshetri et al. [18] suggest that cooperative AI in the 
workplace can improve performance when well-matched 
with resources and task demands. Their resource-based 
approach offers useful insights for the design of AI-facilitated 
intergenerational activities that well utilize technological 
affordances while being sensitive to participants' varied 
capabilities and preferences.  

2.4 Technology and social connection among older adults 

The application of AI technologies to facilitate social 
connections for older adults entails possibilities and ethical 
concerns that must be taken seriously. Reynolds and Landre 
[19] critically consider whether AI ought to be engaged in 
building social connections for older adults, outlining the 
possibility to assist in decreasing isolation while specifying 
grave concerns about the technological replacement of 
human contact. Their ethical analysis calls for sensitivity to 
potential unintended consequences of implementing AI in 
eldercare facilities, including reduced human contact and 
compromised autonomy. Such concerns align with Thomas 
and Kim's [20] research on the health implications of reduced 

physical touch, which suggests that technology-based 
interventions must be carefully crafted to complement rather 
than replace human contact. Their longitudinal study 
demonstrates the physiological and psychological benefits of 
interpersonal touch in the elderly, emphasizing that 
technological mediation should complement rather than 
substitute bodily social interaction. Collectively, these studies 
suggest that AI-facilitated artistic collaboration between 
generations should be carefully crafted to preserve genuine 
interpersonal connections, in conjunction with technological 
advancements. Despite growing interest in both AI-enabled 
creativity and intergenerational programs, critical gaps 
persist in current literature. Existing research examines AI in 
arts and intergenerational activities separately without 
investigating their synergistic potential, provides limited 
empirical evidence on how technological mediation might 
enhance or hinder authentic intergenerational relationships, 
and lacks a comprehensive framework to guide the design 
and implementation of AI-enabled intergenerational creative 
programs [19, 20]. These gaps are particularly problematic 
given rapid population aging and technological advancement, 
which demand innovative approaches to fostering social 
cohesion. This study addresses these gaps by developing and 
empirically testing a triangulated collaboration model that 
positions AI as a facilitator of meaningful intergenerational 
creative engagement. 

3. Theoretical framework and research hypotheses 

3.1 Mixed-methods approach 

This study employs a mixed-methods research design to 
comprehensively examine the enhancement mechanisms of 
Social well-being through AI-enabled intergenerational 
integration in artistic contexts. Our approach integrates both 
qualitative and quantitative methodologies, which can be 
conceptualized through a methodological integration 
function M(x) where: 

( ) ( ) ( )M x Q x L x = +          (1) 

In this function, ( )Q x  represents quantitative methods, ( )L x  

represents qualitative methods, and   and   are weighting 

coefficients that satisfy 1 + = . The weights   = 0.45 and 

  = 0.55 were determined based on variance contribution 

rates from pilot studies, reflecting a slightly greater emphasis 
on qualitative insights while maintaining substantial 
quantitative rigor. The triangulation validity index T can be 
expressed as: 

1

1

( )

( )

n

i i i

i

n

i i i

i

v q l

T

v q l

=

=



=







            (2) 

where vi represents the validation weight for each finding, 
and qi and li represent quantitative and qualitative findings 
respectively. Our sequential explanatory design follows a 
temporal progression function: 

( ) ( ) for [0, ] ( , ) for ( , ]c results cS t Q t t t L t Q t t T=    (3) 

where tc represents the critical transition point between 
phases, and L(t, Qresults) indicates that qualitative exploration 
is informed by and builds upon quantitative results. This 
methodological approach allows us to calculate an integration 

coefficient   that measures the synergistic information gain: 



Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

258 

 

( )

( ) ( )

I Q L

I Q I L



=

+

 (4) 

where 𝐼  represents the information content function based 
on Shannon entropy: 𝐼(𝑋) = −Σ𝑝(𝑥𝑖) log2 𝑝 (𝑥𝑖). 
In practical application, when analyzing technology 
acceptance as a construct, the quantitative data yielded an 
entropy value of 2.45 bits based on 5-point Likert scale 
responses, while qualitative data produced 3.12 bits from 8 
thematic codes. Joint analysis generated 5.89 bits, resulting in 

  = 0.32 bits, representing a 5.4% synergistic information 

gain that validates the mixed-methods approach. This mixed-
methods approach enables both cross-validation of findings 
and a rich understanding of the complex interactions between 
AI technologies, creative processes, and intergenerational 
dynamics. 

3.2 Sampling strategy 

Our data collection methodology incorporated multiple 
measurement techniques over a six-month intervention 
period to capture the multidimensional nature of Social well-
being enhancements. The data collection process can be 
represented by a composite function D(t) that integrates 
various measurement types: 

1

( ) ( )
n

i i

i

D t M t
=

=
 (5) 

 where Mi represents distinct measurement instruments and 
𝜔𝑖 represents their respective weights in the analytical 
framework. Weight derivation employed principal 
component analysis (PCA), with squared loadings from the 
first principal component serving as initial weights, 
subsequently normalized to ensure 𝜔𝑖  =1. The Warwick-
Edinburgh Mental Well-being Scale received a weight of 𝜔1 = 
0.28, while the UCLA Loneliness Scale was assigned 𝜔2  = 
0.23, reflecting their relative contributions to the overall well-
being construct. 
Quantitative well-being assessments employed validated 
psychometric instruments, including the Warwick-Edinburgh 
Mental Well-being Scale (WEMWBS), with an internal 
consistency of 𝛼 = 0.91 and the UCLA Loneliness Scale (𝛼 =
0.87) . Pre- and post-intervention differential scores were 
calculated using: 

100%
post pre

pre

S S
S

S

−
 = 

 (1) 

Qualitative data collection followed a multi-method protocol 
represented by the expression: 

( ) ( ), ( ), ( ), ( )Q p I p F p O p A p=  (2) 

where I represents interview data, F represents focus group 
data, O represents observational field notes, and A represents 
artifact analysis for each participant p. Physiological metrics 
were modeled using a stress reduction function: 

0 1 2( ) ( ) ( )R t HRV t C t   = + + +  (3) 

Where HRV(t) represents heart rate variability, C(t) 
represents cortisol levels at time t, and   is the error term. 

Physiological metrics were modeled using a stress reduction 
function incorporating multiple biomarkers. Heart rate 
variability (HRV) calculations utilized the Root Mean Square 
of Successive Differences (RMSSD) method with 5-minute 
short-term recordings at 1000Hz sampling rate, analyzed 
through Kubios HRV software with smoothness priors 

detrending ( =500). Salivary cortisol collection followed a 

standardized protocol with samples taken at 8:00 AM, 12:00 
PM, 4:00 PM, and 8:00 PM using Salivette® collection tubes. 
ELISA assays maintained intra-assay CV below 5% and inter-
assay CV below 10%, with circadian rhythm correction 
applied through Area Under Curve with respect to ground 
(AUCg) calculations. Data standardization employed z-score 
transformation (z=(x−μ)/σ) with week 1 measurements 
serving as individual baselines, and Winsorization applied to 
data points exceeding three standard deviations. Integration 
of these diverse data streams enabled a comprehensive 
assessment of how AI-enabled intergenerational artistic 
collaboration influences Social well-being across multiple 
dimensions. 

3.3 Analytical framework 

Our analytical framework integrates multiple theoretical 
perspectives to examine the complex relationships between 
AI-enabled artistic co-creation and intergenerational Social 
well-being. The framework can be represented as a 
multidimensional function F(T,C,W) where: 

( , , ) ( )F T C W T C W T C W   = + + +           (4) 

where T represents technological mediation, C represents 
creative process dynamics, W represents well-being 
mechanisms, and 𝛼, 𝛽, 𝛾 , and 𝛿  are weighting coefficients. 
Quantitative analysis employs structural equation modeling 
with the general form: 

B   = + +  (5) 

where 𝜂  represents endogenous constructs (well-being 
outcomes), 𝜉 represents exogenous constructs (technological 
engagement, creative satisfaction), B, and Γ  are coefficient 
matrices, and 𝜉 is the error term. 
Structural equation modeling implementation utilized Mplus 
8.4 software with Maximum Likelihood (ML) estimation. The 
measurement model incorporated endogenous latent 
variables (𝜂) including well-being outcomes with 3 indicators 
and creative satisfaction with 4 indicators, alongside 
exogenous latent variables ( 𝜉 ) comprising technology 
engagement with 5 indicators and intergenerational 
interaction quality with 4 indicators. Model fit indices 
demonstrated excellent alignment with established criteria: 
𝜒2/𝑑𝑓 = 1.87 falling below the threshold of 3.0, CFI = 0.961 
exceeding the 0.95 benchmark, TLI = 0.954 surpassing 0.95, 
RMSEA = 0.048 remaining under 0.06, and SRMR = 0.042 
staying below 0.08. Path coefficients ranged from 0.21 to 0.67, 
with all paths achieving statistical significance at p < 0.05. 
Model fit was assessed using standard indices: 

2 / 3.0,  CFI 0.95,  RMSEA 0.06,  SRMR 0.08df               (6) 

Qualitative thematic analysis followed a systematic coding 
procedure represented by: 

1 2( ) ( ), ( ),..., ( )nD C D C D C D =                             (7) 

where Θ is the thematic mapping function, D represents the 
qualitative dataset, and Ci represents distinct coding 
categories. Inter-rater reliability was calculated using Cohen's 
kappa: 

 1

o e

e

p p

p


−
=

−

                                                                              (13) 

where p0 is observed agreement and pe is expected agreement 
by chance. The integration of these analytical approaches 



Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

259 

 

enables examination of three interconnected dimensions: 
technological mediation (Tm), creative process dynamics (Cp), 
and well-being mechanisms (Wb), providing a comprehensive 
foundation for understanding the multifaceted interactions 
between technology, creativity, and intergenerational 
relationships. 

3.4 Data integration strategy 

The study employs a mixed-type late integration strategy 
that operates across three distinct levels. At the initial level, 
each data stream undergoes independent analysis to preserve 
methodological integrity. The intermediate level focuses on 
identifying convergence and divergence patterns across data 
types, while the final level achieves theoretical integration 
and meta-inference. This hierarchical approach ensures both 
analytical rigor and conceptual synthesis. Data 
inconsistencies were addressed through systematic follow-up 
procedures. When quantitative and qualitative findings 
diverged, targeted follow-up interviews explored underlying 
causes using an explanatory sequential design. Analysis 
revealed that 12% of cases exhibited initial divergence, with 
in-depth interviews successfully explaining these 
discrepancies through contextual factors not captured in 
standardized measures (Table 1). 

Table 1. Data triangulation matrix 

Construct Quantitative 

Measure 

Qualitative 

Theme 

Physiological 

Indicator 

Convergence 

Technology 

Acceptance 

TAM Scale 

 (𝑀 = 4.2) 

"Gradual 

Adaptation" 

Cortisol 

↓15% 

High 

Creative Self-

efficacy 

Self-efficacy 

Scale (𝑀 =

3.8) 

"Breaking 

Through" 

HRV ↑22% High 

Intergenerational 

Understanding 

IUS Scale 

 (𝑀 = 4.5) 

"Perspective 

Shift" 

- Medium 

 

4. Empirical findings 

4.1 Engagement patterns 

The AI system architecture employed a sophisticated 
technical stack designed for intergenerational accessibility 
and creative facilitation. The primary model utilized GPT-3.5-
turbo from OpenAI for creative text generation, 
complemented by Stable Diffusion v2.1 for visual creation 
capabilities and a BERT-base-uncased fine-tuned model for 
sentiment analysis. Training data encompassed 20,000 art 
history texts and reviews, 15,000 annotated 
intergenerational dialogue samples, and 50,000 creative 
writing prompt-response pairs, ensuring comprehensive 
coverage of both artistic knowledge and cross-generational 
communication patterns. Adaptive mechanisms incorporated 
personalized recommendation systems based on Proximal 
Policy Optimization (PPO) reinforcement learning 
algorithms, enabling real-time difficulty adjustments 
responsive to individual user interaction histories. Context-
aware dynamic prompt generation maintained engagement 
by tailoring suggestions to participant skill levels and 
interests. Cloud deployment utilized AWS EC2 p3.2xlarge 
instances with RESTful API architecture implementing OAuth 
2.0 authentication protocols. Redis caching minimized 
latency, achieving average response times below 500 
milliseconds to ensure seamless interaction flow. The 
empirical findings reveal distinct patterns of engagement 
across generations in AI-enabled artistic co-creation 
activities. Analysis of participation data demonstrates that 
while initial technology adoption rates differed between age 

cohorts, with 85% of younger participants (15-25 years) 
showing immediate comfort with AI interfaces compared to 
62% of older participants (65+ years), these differences 
diminished significantly over the six-month intervention 
period. By the conclusion of the study, 79% of older 
participants reported comfort with the AI tools, representing 
a convergence in technological engagement across 
generations. This finding challenges prevalent assumptions 
about persistent digital divides between age groups and 
suggests that appropriately designed AI interfaces can 
facilitate cross-generational technological engagement. 
Collaborative actions underwent three different stages during 
the course of the intervention. The initial "exploration phase" 
(weeks 1-4) was characterized mostly by parallel play, with 
minimal direct intergenerational cooperation. The "transition 
phase" (weeks 5-12) showed greater cross-generational 
consultation, where young participants started asking 
contextual details from older participants. The "integration 
phase" (weeks 13-24) reflected fully collaborative 
production, wherein idea development and elaboration were 
a single, integrated process across generations, supported by 
the AI system. 

In-depth analysis of human-AI-human interaction 
patterns established a new "triangulated collaboration 
model" in which the AI system performed both creative 
stimulus and communication conduit between generations. 
This model describes how AI-produced suggestions built 
common reference points that facilitated cross-generational 
discussion. Participant interviews revealed that the AI system 
input was perceived as "neutral territory" that allowed 
participants to engage with creative concepts without 
generational assumptions or status hierarchies that 
otherwise suppress collaborative exchange. Table 2 gives an 
overview of the development of engagement behaviors across 
the three intervention phases with quantitative measures for 
interaction frequencies, cooperative behavior, and 
technology comfort levels per phase. As shown in Table 2, the 
progression across intervention phases demonstrates a clear 
trajectory toward more integrated collaboration, decreased 
dependence on AI mediation, increased role fluidity, and 
enhanced creative satisfaction for both age cohorts. The 
triangulated collaboration model demonstrates how younger 
participants typically provided technical facilitation in AI 
interaction, while older participants contributed contextual 
knowledge that enriched creative outputs. The AI system, 
positioned at the center of this exchange, provided creative 
stimulation to younger participants while offering interface 
accessibility to older participants. Notably, direct 
intergenerational interaction increased by 147% over the 
course of the intervention, with technology mentoring 
flowing predominantly from younger to older participants 
and cultural mentoring in the opposite direction. 

4.2 Machine Learning analysis approach 

Analysis identified robust bidirectional learning 
processes facilitated by the AI-enabled creative environment. 
Knowledge transfer occurred across three primary domains: 
technical knowledge, cultural-historical context, and creative 
methodologies. Younger participants demonstrated 
significant gains in cultural-historical knowledge (mean 
increase of 28.7 points on the Cultural Knowledge 
Assessment), while older participants showed substantial 
improvement in technical proficiency (mean increase of 32.5 
points on the Technology Confidence Scale). 

 



Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

260 

 

Table 2. Evolution of engagement patterns across intervention 
phases in AI-enabled intergenerational artistic co-creation 

Engagement 

Metric 

Exploration 

Phase  

(Weeks 1-4) 

Transition Phase 

(Weeks 5-12) 

Integration Phase 

(Weeks 13-24) 

Direct 

Intergenerational 

Interaction 

Frequency 

8.3 

interactions/hour 

14.7 

interactions/hour 

20.5 

interactions/hour 

AI Mediation 

Required 

86% of 

collaborative 
exchanges 

63% of  

collaborative 

exchanges 

41% of 

collaborative 
exchanges 

Technological 

Comfort 

(Younger, 15-25) 

85% reporting 
comfort 

92% reporting 
comfort 

97% reporting 
comfort 

Technological 

Comfort (Older, 

65+) 

62% reporting 

comfort 

71% reporting 

comfort 

79% reporting 

comfort 

Collaborative 

Idea Generation 

23% jointly 

developed ideas 

47% jointly 

developed ideas 

72% jointly 

developed ideas 

Role Fluidity Low (fixed roles 

in 82% of 

sessions) 

Moderate (fixed 

roles in 64% of 

sessions) 

High (fixed roles 

in 35% of 

sessions) 

Creative 

Satisfaction (1-5 

scale) 

Younger: 3.4; 

Older: 3.2 

Younger: 3.9;  

Older: 3.7 

Younger: 4.6; 

Older: 4.5 

 

Potential confounding variables were addressed through 
analysis of covariance (ANCOVA), controlling for prior art 
experience (5-point scale, 𝑀 = 2.8 , 𝑆𝐷 = 1.2 ), education 
level (years, 𝑀 = 14.3, 𝑆𝐷 = 3.1), digital literacy 
(standardized test, 𝑀 = 65.4, 𝑆𝐷 = 18.7), and baseline 
creativity measured by Torrance Tests of Creative Thinking. 
After controlling for these covariates, between-group 
differences maintained statistical significance: innovation 
scores 𝐹(2,117) = 18.45 , 𝑝 < .001 , 𝜂2 = 0.24 ; technology 
acceptance 𝐹(2,117) = 12.33, 𝑝 < .001,𝜂2 = 0.17; and well-
being improvement 𝐹(2,117) = 15.67 , 𝑝 < .001 , 𝜂2 = 0.21 . 
Robustness checks employing propensity score matching 
with 1:1 nearest neighbor algorithms yielded post-matching 
standardized bias below 0.1 and an average treatment effect 
on treated (ATT) of 0.43 (𝑝 < .01), confirming the validity of 
observed effects. The AI system functioned as a knowledge 
mediator through three distinct mechanisms. First, it served 
as a "translation interface" between different generational 
vocabularies and reference points, making specialized 
knowledge more accessible across age groups. Second, it 
acted as a "collective memory repository," documenting and 
structuring the accumulated knowledge from collaborative 
sessions and making it available for future reference. Third, it 
provided "scaffolded learning opportunities" by adapting its 
suggestions to the skill levels of different participants, 
creating an optimal zone of proximal development for cross-
generational learning. Skill development trajectories 
followed non-linear patterns, with initial rapid gains followed 
by plateaus and subsequent accelerations as participants 
entered new phases of collaborative integration. Particularly 
notable was the "collaborative acceleration effect," in which 
participants showed steeper learning curves in mixed-age 
groups compared to age-homogeneous control groups using 
the same AI system. This effect was most pronounced in 

creative problem-solving metrics, where mixed-age groups 
outperformed homogeneous groups by an average of 24.3% 
on innovation assessments by the conclusion of the 
intervention. Table 3 presents a comparative analysis of 
knowledge transfer metrics between intergenerational and 
age-homogeneous groups. 

Table 3. Comparative knowledge transfer metrics in 
intergenerational vs. age-homogeneous groups 

 
As illustrated in Table 3, the intergenerational groups 

demonstrated superior performance across all knowledge 
transfer dimensions compared to age-homogeneous groups. 
The "Vocabulary Convergence Index," measuring the degree 
to which participants adopted shared terminology and 
conceptual frameworks, was particularly striking, with 
intergenerational groups achieving more than twice the 
convergence of age-homogeneous groups. These findings 
suggest that the AI-mediated intergenerational context 
created unique conditions for enhanced knowledge transfer, 
retention, and application beyond what could be achieved in 
age-homogeneous settings. 

4.3 Creative outcomes 

Assessment of collaborative productions revealed 
distinctive characteristics of AI-mediated intergenerational 
art. Expert evaluations using the Creative Production 
Assessment Protocol rated these works highly on dimensions 
of conceptual integration (mean score 4.2/5) and narrative 
complexity (mean score 4.5/5), while technical execution 
received more moderate ratings (mean score 3.7/5). 
Thematic analysis of the artworks identified recurring motifs 
of temporal bridging, technological-traditional hybridization, 
and identity exploration, suggesting that the collaborative 
context stimulated reflection on intergenerational 

Knowledge Transfer 

Dimension 

Intergeneration

al Groups 

Younger-

Only 

Groups 

Older-

Only 

Groups 

Technical 

Knowledge Gain 

(Older Participants) 

32.5 points N/A 18.7 

points 

Cultural Knowledge 

Gain (Younger 

Participants) 

28.7 points 12.3 

points 

N/A 

Creative Problem-

Solving 

Improvement 

47.2% 28.6% 22.9% 

Vocabulary 

Convergence Index 

0.78 0.32 0.27 

Knowledge 

Retention (4-week 

follow-up) 

83% 65% 61% 

Cross-Domain 

Application Rate 

64% 39% 36% 

Self-Reported 

Learning 

Satisfaction 

4.6/5.0 3.8/5.0 3.5/5.0 

Novel Concept 

Integration 

3.8/5.0 2.7/5.0 2.5/5.0 



Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

261 

 

connections. The construct of Perspective Integration 
Innovation (PII), central to this research, underwent rigorous  

psychometric development and validation. PII is 
conceptualized as the ability to generate innovative ideas and 
solutions through integrating different generational 
perspectives, encompassing three dimensions: Cognitive 
Flexibility, Perspective Taking, and Creative Synthesis. Initial 
item generation produced 45 candidates based on literature 
review and expert interviews, with seven domain experts 
evaluating content validity (𝐶𝑉𝐼 = 0.89). Pilot testing with 
150 participants led to the retention of 24 items 
demonstrating optimal psychometric properties. Scale 
reliability and validity assessments yielded robust results. 
Internal consistency achieved Cronbach's 𝛼 = 0.92  for the 
total scale and 0.84 − 0.88  for subscales. Test-retest 
reliability over a 4-week interval produced 𝑟 = 0.86 , 
indicating temporal stability. Convergent validity was 
established through correlation with divergent thinking tests 
( 𝑟 = 0.67 ), while discriminant validity was confirmed via 
moderate correlation with general creativity scales ( 𝑟 =
0.42 ). Confirmatory factor analysis supported the three-
factor structure with excellent fit indices: 𝜒2/𝑑𝑓 = 2.14 , 
𝐶𝐹𝐼 = 0.95, 𝑇𝐿𝐼 = 0.94, 𝑅𝑀𝑆𝐸𝐴 = 0.055. 

Innovation metrics demonstrated that AI-enabled 
intergenerational collaborations produced significantly 
higher novelty scores (p < 0.01) compared to both AI-enabled 
same-age collaborations and non-AI intergenerational 
collaborations. This finding suggests a synergistic effect 
between generational diversity and technological mediation 
that enhances creative innovation. Particularly notable was 
the emergence of what we term "perspective integration 
innovation," in which seemingly disparate generational 
viewpoints were synthesized into novel creative approaches 
that would have been unlikely to emerge from either 
generation working independently.  

Participant satisfaction with both the collaborative 
process and creative outcomes remained consistently high 
across age groups, with 87% of younger participants and 84% 
of older participants reporting satisfaction levels of 4 or 5 on 
a 5-point scale. Table 3 presents a comprehensive comparison 
of creative outcomes across different collaborative 
configurations. As shown in Table 4, the AI-enabled 
intergenerational configuration yielded superior creative 
outcomes across nearly all dimensions, with the notable 
exception of technical execution, where AI-enabled same-age 
groups (primarily younger participants) excelled.  

The substantially higher ratings for perspective 
integration, cross-cultural elements, and temporal synthesis 
in AI-enabled intergenerational works suggest that this 
configuration was uniquely effective at facilitating the 
integration of diverse viewpoints into cohesive artistic 
expressions. This is further supported by the 48% exhibition 
selection rate for these works, more than double the rate for 
non-AI intergenerational collaborations. Qualitative analysis 
of satisfaction determinants revealed that younger 
participants particularly valued the "authentic cultural 
knowledge" contributed by older participants, while older 
participants emphasized the "sense of technological 
empowerment" facilitated by the collaborative context. Both 
generations reported that the AI-enabled environment 
created a "level playing field" that minimized age-related 
status differentials and allowed for more equitable creative 
contribution. 

 

 

Table 4. Creative outcome assessment across collaborative 
configurations 

 

These findings collectively demonstrate that AI-enabled 
artistic co-creation provides effective mechanisms for 
enhancing Social well-being through intergenerational 
integration. The triangulated collaboration model facilitates 
bidirectional knowledge transfer, cultivates cross-
generational relationship development, and produces 
innovative, creative outcomes that participants find highly 
satisfying. These empirical results support our theoretical 
framework for understanding the enhancement mechanisms 
of Social well-being through AI-enabled intergenerational 
integration. 

5. Social well-being enhancement mechanisms 

5.1 Individual level 

At the individual level, our findings reveal three primary 
mechanisms through which AI-enabled intergenerational 
artistic co-creation enhances Social well-being. First, 
participants experienced significant improvements in self-
efficacy and digital literacy, with older adults showing a 73% 
increase in technological confidence scores and younger 
participants demonstrating a 48% increase in creative self-

Creative 

Outcome 
Dimension 

AI-Enabled 

Intergener
ational 

Non-AI 

Intergenerational 

AI-Enabled 

Same-Age 

Non-AI 

Same-Age 

Novelty 

(Expert 

Rating, 1-5) 

4.7 3.8 4.1 3.2 

Conceptual 

Integration 

(1-5) 

4.2 3.4 3.6 3.1 

Narrative 

Complexity 

(1-5) 

4.5 3.3 3.8 3.0 

Technical 

Execution 

(1-5) 

3.7 3.4 4.0 3.6 

Originality 

Quotient 

0.82 0.61 0.70 0.54 

Perspective 

Integration 

High 

(86%) 

Medium (52%) Low (34%) Very Low 

(21%) 

Cross-

Cultural 

Elements 

Present in 

79% 

Present in 45% Present in 

37% 

Present 

in 22% 

Temporal 

Synthesis 

Strong in 

72% 

Moderate in 

48% 

Limited in 

31% 

Minimal 

in 18% 

Exhibition 

Selection 

Rate 

48% 23% 31% 16% 

Audience 

Engagement 

(1-5) 

4.3 3.5 3.8 3.2 

Participant 

Satisfaction 

(1-5) 

4.6 3.9 4.2 3.7 



Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

262 

 

efficacy measures. The AI system's adaptive interface design 
provided tailored scaffolding based on individual proficiency 
levels, enabling progressive mastery of digital creative tools. 
The second mechanism involves enhanced creative 
expression and identity formation. AI-generated creative 
prompts and intergenerational dialogue stimulated unique 
forms of self-expression that participants reported were 
inaccessible through conventional art-making. Notably, 84% 
of participants produced works integrating personal history 
with contemporary aesthetic approaches, suggesting 
temporal identity integration facilitated by intergenerational 
exchange. The third mechanism encompasses psychological 
well-being and cognitive vitality. Psychometric assessments 
revealed significant reductions in loneliness scores (mean 
decrease of 28% across all age groups) and improvements in 
cognitive flexibility (32% improvement among older 
participants). The cognitive demands of navigating 
technological systems and intergenerational communication 
created a stimulating environment that contributed to these 
outcomes. As illustrated in Figure 1, these three mechanisms 
function within an integrated framework that begins with AI-
enabled intergenerational artistic co-creation as the catalyst 
and culminates in enhanced individual Social well-being. The 
relationships demonstrate their synergistic nature: self-
efficacy provides foundational skills enabling creative 
expression, which contributes directly to psychological well-
being. This integrated approach offers a comprehensive 
pathway to individual flourishing by leveraging the unique 
affordances of AI systems and the complementary strengths 
of different generations. 

 
Figure 1. Individual-level enhancement mechanisms 

 

5.2 Relational level 

At the relational level, empathy and perspective-taking 
emerged as a primary mechanism through which 
intergenerational artistic collaboration enhanced Social well-
being. Quantitative analysis revealed a 53% increase in 
perspective-taking scores among younger participants and a 
47% increase among older participants. The co-creation 
process with AI tools required explicit verbalization of 
creative intentions, fostering a deeper understanding of 
different generational perspectives. Communication 
enhancement represents the second mechanism. The AI 
system functioned as a communication bridge, translating 
generational vernaculars and providing shared reference 
points. Linguistic analysis showed a 67% increase in cross-
generational conversational turn-taking and a 78% reduction 
in communication breakdowns. This enhanced 
communication transcended the artistic context, with 76% of 
participants reporting improved intergenerational 
communication in other life domains. The third mechanism 
involves social capital formation through reciprocal 
knowledge exchange networks. The intervention created 
conditions for "complementary expertise recognition," where 
each generation valued the distinct knowledge contributions 
of the other. Network analysis revealed increasingly dense 
and reciprocal knowledge-sharing patterns, with centrality 
measures equalizing between age cohorts—contrasting with 
control group interactions, where knowledge exchange 
remained predominantly unidirectional (Figure 2). 

 

 

 

 



Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

263 

 

 

Figure 2. Relational-level enhancement mechanisms 

5.3 Community level 

At the community level, cultural heritage preservation 
emerged as a significant mechanism enhancing collective 
well-being. The AI system's ability to access and integrate 
diverse cultural references enabled a unique form of 
intergenerational cultural transmission. Older participants 
contributed lived historical knowledge, which the AI system 
preserved, structured, and made accessible to younger 
participants in contemporary formats. Concurrently, younger 
participants helped contextualize this knowledge within 
current cultural frameworks. This bidirectional flow resulted 
in 28 community-based digital archives that continue to 
evolve beyond the study period. Community engagement 
represents the second community-level mechanism, with the 
collaborative artistic process catalyzing broader participation 
in community activities. Post-intervention surveys indicated 
that 67% of participants initiated or joined new community 
projects, and the public exhibition of collaborative artworks 
attracted over 3,200 community members across the three 
study locations. The tangible artifacts produced through AI-
enabled intergenerational collaboration served as powerful 
demonstrations of cross-generational creativity, challenging 
ageist stereotypes and inspiring broader community 
participation. The third community-level mechanism 
encompasses inclusive creative practices that extend beyond 
the immediate study participants. The methodological 
approaches developed during the intervention have been 
adopted by 17 community organizations, including senior 
centers, youth arts programs, and public libraries. These 
organizations report that the AI-mediated approach 
significantly reduces barriers to participation for both 
technologically hesitant older adults and artistically 
inexperienced youth.  

 

 

As illustrated in Figure 1, this mechanism demonstrates 
strong connections to empathy and perspective-taking at the 
relational level, creating a virtuous cycle of inclusion and 
understanding. The multi-level organization in Figure 1 
reveals how these mechanisms interact synergistically at 
different levels to enhance Social well-being. For instance, 
increased self-efficacy at the individual level is encouraged to 
enhance communication at the relational level, which in turn 
fosters more inclusive and creative practice at the community 
level. This holistic framework yields a comprehensive 
understanding of how AI-supported intergenerational artistic 
co-creation fosters social well-being through complementary 
routes that occur simultaneously at individual, relational, and 
community levels. 

5.4 Empirical validation of the triangulated collaboration 

model 

Comprehensive empirical testing of the triangulated 
collaboration model employed multiple analytical 
approaches to establish its validity and generalizability. Social 
network analysis using UCINET 6.0 revealed dynamic 
structural changes across the intervention period. Network 
density increased from 0.23  at baseline to 0.68  at study 
conclusion, indicating substantially enhanced 
interconnectedness. Centrality measures demonstrated 
equalization between age cohorts, with between-generation 
betweenness centrality differences decreasing from 0.45  to 
0.12. The clustering coefficient of 0.72 indicated high local 
connectivity within the collaborative network. Temporal 
dynamics were examined through vector autoregression 
(VAR) modeling, revealing directional causality from AI 
engagement to intergenerational interaction (Granger 
causality 𝐹 = 4.32 , 𝑝 < 0.05 ). Impulse response functions 



Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

264 

 

indicated system stabilization after approximately 10 weeks, 
suggesting this timeframe as critical for establishing 
sustainable collaborative patterns. The model demonstrated 
strong predictive validity for 6-month post-intervention 
outcomes: continued creative engagement ( 𝑅2 = 0.41 ), 
community participation (R² = 0.38), and cross-generational 
friendship maintenance ( 𝑅2 = 0.45 ). Cross-context 
validation involved replication studies in three distinct 
community settings (total 𝑛 = 60 ), testing model 
generalizability across diverse demographic and cultural 
contexts. Structural invariance testing yielded Δ𝐶𝐹𝐼 < 0.01, 
confirming model stability across settings. Path coefficient 
comparisons revealed 85% of coefficients maintained 
overlapping 95% confidence intervals across sites, indicating 
robust cross-context applicability. These validation efforts 
establish the triangulated collaboration model as a reliable 
framework for understanding AI-mediated intergenerational 
creative engagement. 

6. Discussion 

6.1 Educational recommendations 

Educational policies must prioritize incorporating AI-

facilitated intergenerational arts programs into formal 

curricula across different levels of education. The evidence 

indicates that intergenerational activities have a notable 

positive impact on both the mental health and well-being of 

children and older adults [21]. Teacher training programs 

must integrate specialized modules that enable AI-facilitated 

intergenerational interaction, as evidence indicates that 

certain implementation practices are key drivers of positive 

outcomes in such settings [22]. In addition, educational policy 

must address digital literacy across generations, enabling 

both younger and older players to use AI technologies 

meaningfully within collaborative creative environments. 

6.2 Cultural and arts policy 

Cultural policy should allocate distinct funding channels 

for intergenerational arts initiatives based on AI, such as the 

Arizona Commission on the Arts' Lifelong Arts Engagement 

Grant program that funds "using creative expression to 

improve quality of life for older adults" and 

"intergenerational projects" [23]. Recognition programs 

should have clear criteria for assessing technological 

innovation in intergenerational arts programming, with an 

incentive for cultural organizations to adopt evidence-based 

practice. In addition, there must be established ethical 

guidelines for AI use in intergenerational art environments, 

with particular attention to data privacy, algorithmic bias, and 

accessibility issues across age. 

6.3 Social welfare strategies 

Age-friendly community initiative programs must be 
specifically designed to comprise AI-supported 
intergenerational arts programs as a core component. The 
results of research on the use of artificial intelligence among 
older adults hold potential for healthcare management and 
social linkage, but the issue of ageism reinforcement needs to 
be addressed [24]. Social welfare policy should encourage 
collaboration between healthcare practitioners and cultural 
centers implementing these programs, as experience suggests 
they have the potential to impact psychological health and 
cognitive resilience positively. Housing policy should 
incorporate AI-enabled community arts programming within 
intergenerational living developments, thereby creating 
sustainable living environments that foster continued cross-

generational creative engagement. In implementing such 
policies, we require an intergenerational approach to 
addressing AI governance itself. The World Economic Forum 
recognizes that "regulation made under this mindset might 
avoid ongoing harm and potentially even stop potential 
damage" [25]. If we make the development of AI inclusive in a 
manner anticipating views from a number of generations, we 
will create technologies and programs that actually deliver 
Social well-being through wise collaboration and innovative 
co-creation among generations. 

7. Conclusion 

 This study has enlightened the multifaceted processes 
through which AI-assisted intergenerational art co-creation 
enhances Social well-being. Our findings indicate that the 
triangulated model of collaboration, where AI serves as a 
creative stimulus and communicative facilitator, allows for 
meaningful cross-generational engagement that is sustaining 
for participants at individual, relational, and community 
levels. The trajectory of rates of technological adoption across 
generations calls into question prevailing hypotheses for 
persistent digital divides, suggesting that AI systems can be 
architected such that creative participation is made more 
democratic throughout the life course. Our theoretical model, 
the Intelligent Collaborative Enhancement Model (ICEM), 
provides an enriched account of how technological adaptivity, 
creative co-construction, and learning exchange interaction 
create "generative integration spaces" in which status 
hierarchies are undermined and meaning-making is 
collaborative. The empirical markers of bidirectional 
knowledge exchange, enhanced perspective-taking, and 
greater creative outcomes in AI-supported intergenerational 
configurations compared to other setups highlight the 
synergies of combining generational difference with 
technological mediation. With populations around the world 
experiencing demographic shifts and social fragmentation, 
the model presented here offers a theoretically grounded and 
empirically derived model for fostering genuine 
intergenerational relationships through collective creativity, 
personal flourishing, relational solidarity, and community 
resilience in the AI-mediated world of today. 

Ethical issue 
The authors are aware of and comply with best practices in 
publication ethics, specifically with regard to authorship 
(avoidance of guest authorship), dual submission, 
manipulation of figures, competing interests, and compliance 
with policies on research ethics. The authors adhere to 
publication requirements that the submitted work is original 
and has not been published elsewhere. 

Data availability statement 
The manuscript contains all the data. However, more data will 

be available upon request from the authors. 

Conflict of interest 

The authors declare no potential conflict of interest. 

 

 

 

 

 

 



Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

265 

 

Mathematical symbol definitions 

 

References 

[1] Baas, M. (2024). Artificial intelligence and the 
question of creativity: Art, data and the sociocultural 
archive of AI-imaginations. International Journal of 
Cultural Studies. 
https://doi.org/10.1177/13675494241246640 

[2] Campbell, F., Whear, R., Rogers, M., Sutton, A., & 
Thompson-Coon, J. (2024). What is the effect of 
intergenerational activities on the wellbeing and 
mental health of children and young people?: A 
systematic review. Campbell Systematic Reviews, 
20(1), e1429. https://doi.org/10.1002/cl2.1429 

[3] Davis, J. M., & Ogbanufe, O. (2024). The impending 
disruption of creative industries by generative AI: 
Opportunities, challenges, and research agenda. 
International Journal of Information Management, 78, 
102807. 
https://doi.org/10.1016/j.ijinfomgt.2024.102807 

[4] Duester, E. (2024). Digital art work and AI: A new 
paradigm for work in the contemporary art sector in 
China. European Journal of Cultural Management and 

Policy, 14, 12470. 
https://doi.org/10.3389/ejcmp.2024.12470 

[5] Frost, S., & Stack, J. (2024). Cultural work, wellbeing, 
and AI. European Journal of Cultural Management 
and Policy, 14, 12825. 
https://doi.org/10.3389/ejcmp.2024.12825 

[6] Giarrusso, R., & Putney, N. (2023). Strengthening 
intergenerational solidarity with social workers at 
the helm. Social Worker, 2025 Social Work Month 
Project. 
https://www.socialworker.com/extras/2025-social-
work-month-project/Intergenerational-solidarity-
social-workers-at-the-helm/ 

[7] Giarrusso, R., & Putney, N. (2023). Strengthening 
intergenerational solidarity with social workers at 
the helm. Social Worker, 2025 Social Work Month 
Project. 
https://www.socialworker.com/extras/2025-social-
work-month-project/Intergenerational-solidarity-
social-workers-at-the-helm/ 

[8] Whear, R., Campbell, F., Sutton, A., Rogers, M., et al. 
(2023). What is the effect of intergenerational 
activities on the wellbeing and mental health of older 
people?: A systematic review. Campbell Systematic 
Reviews, 19(4), e1355. 
https://doi.org/10.1002/cl2.1355 

[9] World Health Organization. (2023). Global 
intergenerational week 2024: Connecting generations 
through intergenerational contact. House of 
Commons Library. 
https://commonslibrary.parliament.uk/research-
briefings/cdp-2024-0094/ 

[10] Mallick, B., & van den Berg, J. (2025). 
Intergenerational grounding of women's 
environmental non-migration. Population and 
Environment, 47, 7. 
https://doi.org/10.1007/s11111-025-00475-w 

[11] Latikka, R., Bergdahl, J., Savela, N., & Oksanen, A. 
(2023). AI as an artist? A two-wave survey study on 
attitudes toward using artificial intelligence in art. 
Poetics, 97, 101797. 
https://doi.org/10.1016/j.poetic.2023.101797 

[12] Messingschlager, T. V., & Appel, M. (2023). Mind 
ascribed to AI and the appreciation of AI-generated 
art. New Media & Society. 
https://doi.org/10.1177/14614448231200248 

[13] Núñez-Cacho, P., Molina-Moreno, V., Galvez-Sánchez, 
F., & Prados, B. (2024). Exploring the transformative 
power of AI in art through a circular economy lens: A 
systematic literature review. Heliyon, 10(4), e25388. 
https://doi.org/10.1016/j.heliyon.2024.e25388 

[14] MIT RAISE. (2023). Responsible AI for social 
empowerment and education. Massachusetts 
Institute of Technology. https://raise.mit.edu/ 

[15] Herndon, H., & Dryhurst, D. (2024). Articulating arts-
led AI: Artists and technological development in 
cultural policy. European Journal of Cultural 
Management and Policy, 2024, 12820. 
https://doi.org/10.3389/ejcmp.2024.12820 

[16] Oates, G. (2023). Cultural work, wellbeing, and AI: 
Exploring the impact of artificial intelligence on 
heritage organizations. European Journal of Cultural 
Management and Policy, 14, 12825. 
https://doi.org/10.3389/ejcmp.2024.12825 

[17] Przegalinska, A. (2024). Extending human creativity 
with AI. Information Systems Frontiers, 2024. 

Symbol Definition Range 

M(x) Methodological 

integration function at 

time t 

[0, 1] 

Q Quantitative method 

component 

- 

L Qualitative method 

component 

- 

𝛼, 𝛽  Method weighting 

coefficients 

 𝛼 +  𝛽= 1 

 𝛼, 𝛽 ∈ [0,1] 

𝜏 Triangulation validity 

index 

[0, 1] 

𝑤𝑖
 

Validation weight for 

finding i 
𝑤𝑖 = 1, 𝑤𝑖 > 0 

𝑄𝑖 , 𝐿𝑖 Quantitative/qualitative 

finding i 
- 

𝑆(𝑡) Sequential progression 

function 

Continuous 

function 

𝑡𝑐  Critical transition point 𝑡𝑐  = 12 weeks 

𝐼𝑐  Integration coefficient [0, 1] 

𝑓𝑖𝑛𝑓𝑜 Information content 

function 

Based on Shannon 

entropy 

𝐷(𝑡) Data collection composite 

function 

- 

𝑀𝑖 Measurement instrument 

i 
𝑀𝑖∈ {1,2,...,n} 

𝜔𝑖 Measurement instrument 

weight 

𝜔𝑖 = 1 

https://www.socialworker.com/extras/2025-social-work-month-project/Intergenerational-solidarity-social-workers-at-the-helm/
https://www.socialworker.com/extras/2025-social-work-month-project/Intergenerational-solidarity-social-workers-at-the-helm/
https://www.socialworker.com/extras/2025-social-work-month-project/Intergenerational-solidarity-social-workers-at-the-helm/


Wanyi He et al. /Future Technology                                                                                November 2025| Volume 04 | Issue 04 | Pages 255-266 

266 

 

https://doi.org/10.1016/j.isf.2024.27133745240000
62 

[18] Murire, O. T. (2024). Artificial Intelligence and Its 
Role in Shaping Organizational Work Practices and 
Culture. Administrative Sciences, 14(12), 316. 
https://doi.org/10.3390/admsci14120316 

[19] Kshetri, N., Barlow, J., & Rossi, F. (2023). 
Collaborative AI in the workplace: Enhancing 
organizational performance through resource-based 
and task-technology fit perspectives. International 
Journal of Information Management, 2024, 102807. 
https://doi.org/10.1016/j.ijinfomgt.2024.001014 

[20] Reynolds, J. M., & Landre, A. (2023). Should artificial 
intelligence play a role in cultivating social 
connections among older adults? Journal of Ethics, 
American Medical Association. 
https://journalofethics.ama-assn.org/article/should-
artificial-intelligence-play-role-cultivating-social-
connections-among-older-adults/2023-11 

[21] Campbell, F., Whear, R., Rogers, M., Sutton, A., & 
Thompson-Coon, J. (2024). What is the effect of 
intergenerational activities on the wellbeing and 
mental health of children and young people?: A 
systematic review. Campbell Systematic Reviews, 
20(1), e1429. 

[22] Jarrott, S. E., Turner, S. G., Juris, J., Scrivano, R. M., & 
Weaver, R. H. (2022). Program practices predict 
intergenerational interaction among children and 
adults. The Gerontologist, 62(3), 385-396. 

[23] Jarrott, S. E., Turner, S. G., Juris, J., Scrivano, R. M., & 
Weaver, R. H. (2022). Program practices predict 
intergenerational interaction among children and 
adults. The Gerontologist, 62(3), 385-396. 

[24] Wong, A. K. C., Lee, J. H. T., Zhao, Y., Lu, Q., Yang, S., & 
Hui, V. C. C. (2025). Exploring older adults' 
perspectives and acceptance of AI-driven health 
technologies: Qualitative study. JMIR Aging, 8, 
e66778. 

[25] Stratton, S. C., & Dias, M. B. (2021). Why we must 
consider the intergenerational impacts of AI. World 
Economic Forum. 
https://www.weforum.org/stories/2021/10/why-
we-must-consider-the-intergenerational-impact-of-
ai/#:~:text=Technology%20is%20often%20used%2
0to,but%20those%20in%20the%20future. 
 

  

This article is an open-access article distributed under the 

terms and conditions of the Creative Commons Attribution 

(CC BY) license 

(https://creativecommons.org/licenses/by/4.0/). 

https://creativecommons.org/licenses/by/4.0/

