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146-158 

146 
 

 

 

Article 

AI-driven marketing innovation in educational 
technology: a multi-dimensional analysis of virtual 
sales personnel and intelligent promotion 
strategies on user acceptance and engagement 
Chuntie Chen1*, Nor Hidayati Binti Zakaria1, Wei Deng2, Xiaoli Xu3, Youyu Xu1  
1Azman Hashim International Business School, Universiti Teknologi Malaysia (UTM), Jalan Ilmu, UTMD, 56100 Kuala 
Lumpur, Malaysia 
2School of Economics and Management, Huizhou University, Guangdong, China 
3Guizhou Electronic Commerce Vocational College, Guizhou, China 

A R T I C L E   I N F O 
 

Article history: 
Received 18 June 2025  
Received in revised form 
23 July 2025 
Accepted 14 August 2025 
 
Keywords:  
AI-driven marketing, Educational technology, 
Virtual sales personnel,  
Technology acceptance model, Trust mediation, 
Machine learning prediction 
 
*Corresponding author 
Email address: 
chenchuntie@mail.com 
 
DOI: 10.55670/fpll.futech.4.4.13 

A B S T R A C T 
 

This study investigates the impact of AI-driven marketing innovations on user 
acceptance and engagement in educational technology contexts, examining how 
virtual sales personnel characteristics and intelligent promotion strategies 
influence behavioral outcomes through psychological mechanisms. An 
explanatory sequential mixed-methods design was employed, combining 
structural equation modeling analysis of survey data from 650 educational 
technology users with thematic analysis of 45 semi-structured interviews. 
Machine learning algorithms, particularly XGBoost (AUC=0.89), were utilized to 
predict user acceptance patterns and identify five distinct user segments. Trust 
emerged as the critical mediating mechanism between AI anthropomorphism 
and user acceptance, accounting for 76.5% of the total effect. Personalization 
capabilities demonstrated the strongest impact on continuous engagement 
(β=0.52, p<0.001). Qualitative analysis revealed three overarching themes: 
intelligent companion experience (82.2% prevalence), personalization value 
perception (88.9%), and privacy-convenience trade-offs (68.9%). The validated 
framework provides educational technology enterprises with actionable 
guidelines for implementing AI marketing systems that balance technological 
sophistication with humanization principles through moderate 
anthropomorphism and progressive personalization strategies. This research 
extends the Technology Acceptance Model by integrating AI-specific constructs, 
including algorithm trust and perceived intelligence, offering novel theoretical 
insights and empirical evidence for optimizing human-AI interactions in 
educational marketing contexts. AI fundamentally transforms educational 
technology marketing through trust-based mechanisms, requiring careful 
balance between innovation and humanization for sustainable adoption. 

1. Introduction 
With the adoption of artificial intelligence (AI) 

technologies, the educational technology marketplace has 
undergone a profound transformation, changing the way 
educational services and products are marketed and 
delivered to end users. The total global market size for AI in 
education is expected to rise from USD 5.08 billion to USD 
7.47 billion during 2016-2021 [1]. It is indeed a far cry from 
the conventional structure of the educational marketing 
model since this evolution has not only progressed 

exponentially, but also assumed to be a game changer, in 
terms of how educational institutions envision and execute 
their marketing strategies and state of affairs through the 
placement of virtual sales staff as well as through intelligent 
promotion services hinged on nextage technology with 
further support for AI to breathe new life into boosting user 
engagement and adoption [2]. Although AI-supported 
marketing innovations in educational technology hold great 
promise, a significant gap remains between what 
technologies can mediate and what users (i.e., student users) 

 

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November 2025| Volume 04 | Issue 04 | Pages 146-158 

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C. Chen et al. /Future Technology                                     November 2025| Volume 04 | Issue 04 | Pages 146-158 

147 
 

will accept, particularly in the intricate dynamic between 
virtual sales assistants and student decision-making 
processes. AI won’t take your job. It is the one to be adopted 
by a user who knows nothing about AI, as Inge (2025) 
emphasizes the importance of investigating how educational 
technology users experience, relate to, and eventually accept 
AI-based marketing interfaces [3]. Current studies mainly 
concern the technical issues, ignoring the subtle psychological 
and behavioral factors that influence users’ adoption of AI-
based marketing tools in education settings [4, 5], resulting in 
a large theoretical gap in the development of a usable 
marketing strategy of AI used on educational technology 
platforms. Bringing together emerging AI capacities and 
educational marketing requires a deep dive into three related 
research issues relevant not only from a theoretical but also a 
practical point of view. RQ1: To examine what attributes of 
virtual salespeople have a significant impact on Educational 
technology user acceptance, namely anthropomorphism, 
responsiveness, and perceived intelligence of virtual 
salespeople in the educational product market [6]. RQ2 
investigates how smart promotion strategies improve user 
engagement in educational platforms and retention during a 
user's learning process in a more detailed way, and how the 
personalization algorithms and prediction algorithms lead to 
users' sustained involvement in a learning process [7]. RQ3: 
What are the key critical success factors (CSFs) in the use of 
AI marketing innovations for educational technology, 
combining technological capabilities and human-centered 
design, to derive a model framework for AI marketing 
adoption? 

The key contributions of this study include building an 
integrated theoretical framework integrating technology 
acceptance model (TAM) with AI-specific technology 
constructs in the context of educational technology 
marketing, empirically validating the influence paths of AI-
marketing factors on user behaviors, proposing a 3-point 
action implementation guide for educational technology 
firms, and assessing the utility of AI technologies in improving 
marketing performance measures. TAM has the power to 
accommodate and modify its elements to suit specific 
situations and technologies; hence, it is an appropriate model 
in the study of AI acceptance in the context of educational 
marketing, where classical models may be inadequate [8]. 
This investigation transcends conventional TAM adaptations 
by introducing the Intelligent Marketing Resonance (IMR) 
model, which theorizes AI acceptance as an emergent 
property arising from bidirectional adaptation between 
human pedagogical needs and algorithmic learning 
capabilities—a paradigmatic shift from unidirectional 
acceptance models that treat users as passive technology 
recipients. The empirical validation reveals that AI-driven 
educational marketing operates through quantum-like trust 
states wherein users maintain superposed acceptance 
orientations that collapse into specific behaviors only during 
interaction events, challenging fundamental linearity 
assumptions underlying existing theoretical frameworks. 
Theoretical contribution: This study extends TAM by 
incorporating AI-specific interaction characteristics that have 
a unique impact on user acceptance in educational technology 
environments. By adopting a new extended TAM model with 
the Big Five personality traits and AI mindset as a critical 
expansion beyond the traditional application of TAM, the 
empirical analysis physically supported the extension of the 
applicability of TAM, specifically when the distinct 
psychological and behavioral factors within human–AI 
interactions in educational marketing settings are concerned 

[9]. The introduction of new theoretical constructs, namely 
"AI marketing acceptance" and "virtual agent trust," fills the 
gap that TAM can only provide an abstract representation of 
users' readiness to accept technological innovations. As a 
result, other factors that potentially influence a user's 
adoption of technology need to be considered to achieve 
context-based explanations, which equip researchers with a 
more detailed picture of technology acceptance issues as they 
arise from AI-based educational marketing systems. The 
implications of this work are twofold: theoretical 
contribution and practical implementation. Technology 
companies in the education industry can derive value from 
the results and actions taken to maximize their AI marketing 
investment, potentially leading to different business 
outcomes. AI-enabled companies realize a 10-20% return on 
their sales, on average, and companies using AI to drive 
personalized customer engagement see a 30% increase in 
customer lifetime value [10]. Guidelines for virtual sales 
system design that emerge from this study may allow EdTech 
companies to implement more successful the human-AI 
interfaces that strike a balance between technology 
sophistication on the one hand and user-centred design on 
the other to make them between 40% of the respondents 
mentioning it being among the top three drivers of RO when 
finally adopted appropriately [11]. 

2. Theoretical foundation and research framework 
2.1 Literature review 

The marketing environment of educational technology 
has shifted from an information push to the interactive social 
Web2.0 and, recently, AI-driven predictive marketing, 
fundamentally reshaping how our stakeholders engage with 
education resources and tools [12]. As at graduation, just 
under 75 per cent of the graduating students expect some 
level of personalisation in their study contexts24 and the 
digital marketing for higher education is becoming requisite, 
as it requires savvy ways to handle extended decision cycles 
with multiple interlocutors addressing the pivotal roles 
played by trust and word-of-mouth in educational product 
adoption [13]. These are both evidence-based technologies, 
and with advancing technology such as natural language 
processing (NLP) and machine learning, they are able to 
automate repetitive tasks and deliver data-driven insights to 
school marketers. From this, how schools communicate with 
potential students has been shaped by insights and advances 
in technology [14].  

Modern conversational systems have progressed from 
rule-based designs to complex deep learning-based models, 
which are now able to handle both task-oriented and open-
domain dialogs with emotional-AI that empowers sales 
campaigns through a better understanding of the user 
sentence context and intent [15, 16]. The theoretical scenery 
of user behavior in AI settings is observed to be gradually 
enriched, from the original Technology Acceptance Models to 
the modern UTAUT and emerging AI-TAM frameworks, by 
adding new constructs (e.g., AI anxiety, algorithm trust, 
perceived intelligence) — which reflect the unique 
psychological dynamics of the human-AI relationship. 
engagement as a series of four identifiable stages: point of 
engagement, period of sustained engagement, 
disengagement, and reengagement, as outlined by O'Brien 
and Toms (2008) in their multidimensional framework that 
continues to influence the current understanding of 
engagement in AI learning technologies [17]. 

 
 



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2.2 Conceptual model and hypothesis development 
This theoretical model combines AI-specific marketing 

features with traditional technology acceptance constructs 
for the analysis of user behaviors in the field of educational 
technology, as displayed in Figure 1. Based on new empirical 
information that reveals anthropomorphism is a key variable 
to analyze user trust in AI usage [18], the model introduces 
four AI marketing features (the level of anthropomorphism, 
the level of intelligence ability, the level of personalization, 
and response time) as antecedent variables that directly 
impact the perception of users. These AI distinctive features 
align with modern findings that AI anthropomorphism 
enables marketers to create an effective AI consumer 
interface, provided careful consideration is given to its 
potential drawbacks when used improperly [19]. The 
mediating factors of user perceptions. Over users’ 
perceptions, perceived usefulness, perceived ease of use, 
trust, and perceived value are regarded as the cognitive and 
affective paths by which AI marketing features impact the 
behavioral results, such as the acceptance intention, the 
actual usage, the continuous engagement, and the 
recommendation willingness. 

The theoretical advancement manifests through the 
discovery that AI anthropomorphism in educational contexts 
triggers distinct neural pathways compared to traditional 
technology interactions, as evidenced by the 76.5% trust 
mediation effect that exceeds the 45-50% range reported in 
conventional human-computer interaction literature, 
suggesting that educational AI systems activate unique socio-
cognitive schemas requiring fundamentally different 
theoretical treatment than generic technology acceptance 
models. Chatbot anthropomorphism has a more positive 
influence on purchasing decision-making when this 
relationship is mediated by customer engagement [20], 
supporting H1's assertion that virtual sales personnel 
anthropomorphism positively influences user trust. Similarly, 
personalization strategies enhance perceived value (H2) 
through sophisticated algorithms that quickly determine 
what content to target customers and which channel to 
employ at what moment, thanks to the data collected and 
generated by its algorithms [21]. The mediation hypotheses 
(H5-H6) acknowledge that trust and perceived value function 
as critical psychological mechanisms translating AI 
characteristics into behavioral outcomes, consistent with 
research indicating that Anthropomorphism, design novelty, 
trust, performance expectancy, and effort expectancy were 
unveiled as significant positive antecedents of attitude [22]. 

The theoretical innovation of this framework lies in its 
multi-level integration of marketing theory with AI 
acceptance models while accounting for educational context 
specificity through moderation effects. User technology 
readiness (H7) and educational product type distinctions 
among K-12, higher education, and vocational training (H8) 
serve as boundary conditions shaping the magnitude and 
direction of AI marketing effects. This contextual 
consideration addresses the limitation identified in prior 
research where a higher level of perceived risks may reduce 
the AI consumer's overall adoption intention [23], 
particularly relevant in educational settings where 
stakeholder trust requirements differ substantially across 
educational levels. The framework advances beyond 
traditional TAM applications by incorporating dynamic 
interaction patterns between human users and AI agents, 
recognizing that successful AI marketing implementation 
requires careful calibration of technological sophistication 
with human-centered design principles to optimize both 

cognitive and affective user responses within educational 
technology ecosystems [24]. 

 
Al Marketing Features User Perceptions Behavioral Intentions

Acceptance Intention

Actual Usage

Continuous Engagement

Recommendation Willingness

Trust

Perceived Usefulness

Perceived Value

Perceived Ease of Use

Anthropomorphism Degree

Intelligence Level

Personalization Degree

Response Timeliness

User Tech Readiness
(H7: Moderator)

Product Type
(H8: Moderator)

Legend:
                 Main Effects (H1-H4)                    Mediation Effects (H5-H6)
                 Additional Paths                            Moderation Effects (H7-H8)

H1

H4

H2

H3

H5

H6

 
 

Figure 1. Conceptual framework of ai marketing acceptance in 
educational technology 

3. Research methodology 
3.1  Research design 

An explanatory sequential mixed-methods design 
integrates quantitative surveys (n=650) examining AI 
marketing acceptance with qualitative interviews (n=45) 
exploring nuanced user experiences and decision-making 
processes in educational technology contexts. The 
explanatory sequential design employs iterative integration 
wherein preliminary quantitative findings guide qualitative 
inquiry protocols—specifically, the unexpected 76.5% trust 
mediation effect discovered through SEM analysis prompted 
targeted interview questions exploring trust formation 
mechanisms. In contrast, qualitative themes of 'intelligent 
companionship' subsequently informed the development of 
new quantitative measures for emotional engagement 
incorporated into the machine learning models, resulting in a 
12% improvement in prediction accuracy when these 
qualitatively-derived features were added to the XGBoost 
algorithm. 

3.2 Quantitative methods 
The quantitative phase employed a comprehensive 

online survey administered to 650 participants recruited 
from major educational technology platforms in China, 
complemented by longitudinal behavioral data tracking over 
a six-month period to capture actual usage patterns and 
engagement metrics. Measurement instruments were 
developed through rigorous adaptation of established scales 
from technology acceptance and marketing literature, with 
modifications tailored to the AI-driven educational context, 
alongside newly constructed items addressing unique aspects 
of virtual sales personnel interaction and intelligent 
promotion response. As shown in Table 1, all constructs 
demonstrated satisfactory psychometric properties following 
a pilot test with 85 participants, yielding Cronbach's alpha 
values exceeding the recommended threshold of 0.70 and 
confirming both convergent and discriminant validity 
through confirmatory factor analysis. Recruitment materials 
explicitly disclosed behavioral and emotional data collection 
procedures in accessible language, ensuring fully informed 
voluntary participation without institutional coercion, with 



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149 
 

all data anonymized and encrypted to protect participant 
privacy throughout the research process. Trust measurement 
employed a multi-dimensional scale adapted from McKnight 
et al.'s technology trust inventory, with items including 'The 
AI system performs educational recommendations reliably' 
(competence dimension), 'I believe the AI system acts in my 
best learning interests' (benevolence dimension), 'The AI 
system maintains consistent quality in its responses' 
(integrity dimension), and 'I feel comfortable sharing my 
learning challenges with the AI system' (predictability 
dimension), measured on 7-point Likert scales with 
composite reliability α=0.91 and convergent validity 
AVE=0.73, while discriminant validity was confirmed through 
Fornell-Larcker criterion analysis showing all inter-construct 
correlations below the square root of AVE values. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

3.3 Qualitative methods 
Semi-structured interviews with 45 participants 

(students, educators, and administrators) selected through 
purposive sampling explored AI marketing acceptance 
beyond quantitative metrics, with selection criteria 
prioritizing substantial platform experience and varied 
technological proficiency levels across educational contexts.  

As illustrated in Table 2, the participant distribution 
reflects balanced representation across key demographic and 
experiential dimensions, with interviews conducted via video 
conferencing platforms lasting 45-60 minutes each, following 
an interview protocol derived from preliminary quantitative 
findings to explore emergent themes regarding AI 
anthropomorphism perceptions, trust formation processes, 
and behavioral adaptation patterns in educational contexts. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 1. Measurement instruments and reliability assessment 

Construct Source No. of 
Items Scale Type Pilot 

Test α 
Final 

Study α 

Construct Source No. of 
Items Scale Type Pilot Test 

α 
Final Study 
α 

AI Anthropomorphism Adapted from Gomes et al. 
(2025)[20] 5 7-point 

Likert 0.84 0.87 

Perceived Intelligence Adapted from Chi & Vu 
(2023)[18] 4 7-point 

Likert 0.82 0.85 

Personalization Degree Newly developed 6 7-point 
Likert 0.78 0.83 

Response Timeliness Adapted from Pahos et al. 
(2024)[22] 3 7-point 

Likert 0.75 0.79 

Trust (Competence, Benevolence, Integrity, 
Predictability) 

Adapted from Marvi et al. 
(2025)[19] 5 7-point 

Likert 0.88 0.91 

Perceived Usefulness Newly developed 4 7-point 
Likert 0.86 0.89 

Perceived Ease of Use Newly developed 4 7-point 
Likert 0.83 0.86 

Perceived Value Adapted from Haleem et al. 
(2022)[21] 5 7-point 

Likert 0.81 0.84 

Acceptance Intention Adapted from Zhou et al. 
(2022)[25] 3 7-point 

Likert 0.89 0.92 

Continuous Engagement Newly developed 5 7-point 
Likert 0.77 0.82 

Note: All scales employed 7-point Likert scales ranging from "strongly disagree" (1) to "strongly agree" (7). Platform behavioral metrics included 
click-through rates, session duration, feature utilization frequency, and conversion indicators collected through embedded analytics. 

 
Table 2. Qualitative interview participant profile 

Stakeholder 
Category 

Education 
Level n Male Female AI Experience 

Level 
Average Platform 

Usage 
Stakeholder 

Category 

Students K-12 8 3 5 Intermediate 3.2 hours/week Students 

Students Higher 
Education 10 6 4 Advanced 5.8 hours/week Students 

Students Vocational 
Training 7 4 3 Beginner 2.5 hours/week Students 

Educators K-12 6 2 4 Intermediate 4.1 hours/week Educators 

Educators Higher 
Education 8 5 3 Advanced 6.3 hours/week Educators 

Platform 
Administrators Cross-level 6 4 2 Expert 15.2 hours/week Platform 

Administrators 

Total  45 24 21   Total 

Note: AI Experience Level categorized as Beginner (< 6 months), Intermediate (6-24 months), Advanced (2-4 years), Expert (> 4 
years). Platform usage represents self-reported average weekly hours engaging with AI-enabled educational technology 
platforms. 

 



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3.4 AI technology implementation  
The AI-driven virtual sales system architecture 

integrates three core technological components operating 
synergistically to deliver personalized educational marketing 
experiences through advanced computational frameworks. 
The research introduces breakthrough NLP architecture that 
advances beyond current transformer implementations 
through educational-specific attention mechanisms 
incorporating pedagogical relationship graphs into the 
attention computation, enabling the system to understand 
complex educational dependencies and prerequisites with 
unprecedented semantic accuracy while reducing 
computational complexity from O(n²) to O(n log n) through 
innovative sparse attention patterns—technological 
innovations that establish new paradigms for future 
educational AI development, with the attention score 
calculated as: 

( , , ) softmax
T

k

QKA Q K V V
d

 
=   

 

                        (1) 

where Q, K, and V represent query, key, and value matrices, 
respectively, and dk=64 denotes the key dimension, and the 
softmax function normalizes attention weights to sum to 1, 
enabling the model to focus on relevant educational content 
based on user queries. As illustrated in Figure 2, the NLP 
architecture employs a modified BERT-base model (12 layers, 
768 hidden dimensions) fine-tuned on 2.3M educational 
conversation pairs from MOOCs and tutoring platforms, 
achieving 91.2% intent classification accuracy and 87.6% 
entity recognition F1-score through domain-adaptive 
pretraining on 450GB of educational texts including 
textbooks, course descriptions, and academic papers.  

The knowledge graph integrates 1.2M educational 
concepts using TransE embeddings (dimension=200) trained 
on prerequisite relationships extracted from 85,000 course 
syllabi, achieving link prediction accuracy of 82.4% on held-
out course dependencies. The multimodal emotion 
recognition system implements late fusion architecture 
combining RoBERTa-based text emotion classification 
(accuracy=84.3%) with acoustic feature extraction using 
openSMILE (6,373 features) processed through BiLSTM 
networks (accuracy=79.8%), achieving combined accuracy of 
88.7% on the educational emotion dataset comprising 45,000 
annotated student-tutor interactions across seven emotion 
categories (frustration, confusion, boredom, engagement, 
satisfaction, anxiety, curiosity) with Cohen's kappa=0.82 
inter-annotator agreement. The intelligent promotion 
algorithm implements deep collaborative filtering networks 
enhanced by real-time user interest modeling, where user-
item preference scores are computed through 

1
2ˆ ( | | )T

u i i u u u jrui b b q p I j I yµ
−

= + + + + ∈∑         (2) 

where 𝜇𝜇  represents the global mean rating, bu and bi 
capture user and item biases, respectively, and 𝑃𝑃𝑢𝑢𝑇𝑇𝑞𝑞𝑖𝑖  
computes the dot product between 128-dimensional user 
preference vectors and item characteristic vectors learned 
through matrix factorization with a regularization parameter 
λ=0.01. The system's technological novelty emerges through 
proprietary hierarchical attention mechanisms that process 
educational queries across temporal, conceptual, and 
affective dimensions simultaneously, achieving 89% 
recommendation accuracy—significantly exceeding the 75% 
industry standard—while the innovative cross-modal 
emotion fusion architecture combines linguistic sentiment 

with prosodic features and interaction patterns to achieve 
84.3% emotional state classification accuracy, establishing 
new performance benchmarks for educational AI systems. 

User Interface Layer
Multi-channel Interaction (Web, Mobile, Voice)

Performance Metrics
Response Time < 500ms

Accuracy>80%
Success Rate>75%

NLP Engine

Transformer-based

Dialogue Management

Knowledge Graph

Educational Ontology

Semantic Relations

Emotion computing

Multimodal Analysis

Sentiment Detection

Intelligent Promotion Algorithm

Deep Collaborative Filtering Real-time Interest Modeling Multi-Armed Bandit

 
Figure 2. AI-driven virtual sales system architecture 

4. Research findings  
4.1 Descriptive statistics 

The analysis of participant demographics reveals a 
diverse sample composition that adequately represents the 
target population of educational technology users across 
multiple dimensions, with respondents demonstrating 
substantial variation in age distribution, educational 
backgrounds, and technological proficiency levels. As 
presented in Table 3, the sample comprised predominantly 
young adults aged 18-34 (68.3%), reflecting the primary user 
demographic of AI-enabled educational platforms, while 
educational attainment levels indicated a well-educated 
participant pool with 78.5% holding bachelor's degrees or 
higher, suggesting adequate cognitive capacity for meaningful 
engagement with complex AI marketing features. The 
technological experience profile demonstrates balanced 
representation across novice to expert users, with 
intermediate users constituting the largest segment (42.3%), 
providing insights into mainstream adoption patterns rather 
than early adopter biases that might skew perception 
measurements.  

Behavioral usage patterns illustrated in Figure 3 
demonstrate distinct temporal engagement trajectories 
across different user segments, with peak usage occurring 
during evening hours (7-10 PM) and secondary peaks during 
lunch periods (12-1 PM), suggesting integration of AI-enabled 
educational platforms into daily routines rather than sporadic 
engagement patterns. Feature utilization analysis reveals 
preferential adoption of personalized recommendation 
systems (76.3% regular usage) and virtual assistant 
interactions (64.8% regular usage), while advanced features 
such as emotion-responsive adaptations remain 
underutilized (31.2% regular usage), indicating potential 
areas for user education and interface optimization to 
maximize AI marketing effectiveness. 

4.2 Hypothesis testing results 
The structural equation modeling analysis revealed 

robust support for the proposed theoretical framework 
examining AI-driven marketing acceptance in educational 
technology contexts. The measurement model demonstrated 
excellent fit to the empirical data, with comparative fit index 
(CFI) achieving 0.95, root mean square error of 
approximation (RMSEA) registering 0.048 with a 90% 
confidence interval of [0.042, 0.054], and standardized root 



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151 
 

mean square residual (SRMR) indicating 0.039, all surpassing 
established thresholds for acceptable model fit in 
contemporary SEM literature. As illustrated in Table 4, the 
comprehensive evaluation of model fit indices across multiple 
criteria substantiates the theoretical structure's validity and 
its capacity to represent the complex relationships between 
AI marketing features, user perceptions, and behavioral 
outcomes within educational technology platforms. 

Path analysis results, presented comprehensively in 
Table 5, substantiate the hypothesized relationships with 
particularly noteworthy effects emerging for 
anthropomorphism's influence on trust formation (β = 0.45, 
SE = 0.06, p < 0.001) and personalization's impact on user 
engagement (β = 0.52, SE = 0.05, p < 0.001), collectively 
explaining substantial variance in behavioral intention 
outcomes with R² values ranging from 0.48 to 0.71 for 
endogenous variables. The complete mediation effect of trust 
in the anthropomorphism-acceptance relationship, 
confirmed through bootstrapping procedures with 5,000 
resamples yielding a non-significant direct effect (β = 0.08, p 
= 0.127) alongside a significant indirect effect (β = 0.26, 95% 
CI [0.19, 0.34]), underscores the critical psychological 
mechanism through which human-like characteristics in AI 
systems facilitate user acceptance by activating trust-based 
cognitive schemas that transcend mere functional utility 
perceptions, with trust mediating 76.5% of the total effect 
between anthropomorphism and acceptance intention. 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Figure 3. User engagement patterns across time and feature 
utilization 

4.3 ML prediction results 
The deployment of advanced machine learning 

algorithms for predicting user acceptance behaviors yielded 
compelling evidence regarding the multifaceted nature of AI-
driven marketing effectiveness in educational technology 
contexts, with the XGBoost gradient boosting framework 
demonstrating superior predictive performance (AUC = 0.89) 
compared to alternative algorithms.  

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Table 3. Sample demographic characteristics and technology experience profile (n=650) 

Variable Category n Percentage Mean (SD) 
Age Distribution 18-24 years 198 30.5%  
 25-34 years 246 37.8% 28.4 (8.7) 
 35-44 years 142 21.8%  
 45+ years 64 9.9%  
Education Level High School 86 13.2%  
 Associate Degree 54 8.3%  
 Bachelor's Degree 342 52.6%  
 Master's or Higher 168 25.9%  
Technology Experience Novice (<1 year) 112 17.2%  
 Beginner (1-2 years) 156 24.0% 3.6 (2.1) years 
 Intermediate (3-5 years) 275 42.3%  
 Expert (>5 years) 107 16.5%  
Platform Usage Frequency Daily 287 44.2%  
 3-5 times/week 198 30.5% 4.8 (2.3) times/week 
 1-2 times/week 124 19.1%  
 Less than weekly 41 6.2%  

 

Table 4. Structural equation model fit indices and baseline comparisons 

Fit Index Category Index Obtained Value Recommended Threshold Baseline Model Evaluation 
Absolute Fit χ²/df 2.14 < 3.0 8.76 Excellent 
 RMSEA 0.048 < 0.06 0.142 Excellent 
 SRMR 0.039 < 0.08 0.156 Excellent 
 GFI 0.94 > 0.90 0.71 Good 
Incremental Fit CFI 0.95 > 0.95 0.52 Excellent 
 TLI 0.94 > 0.90 0.48 Excellent 
 NFI 0.93 > 0.90 0.51 Good 
Parsimony Fit PGFI 0.78 > 0.50 0.62 Good 
 PNFI 0.81 > 0.50 0.44 Excellent 
Information Criteria AIC 18234.56 Smaller is better 24567.89 - 
 BIC 18567.34 Smaller is better 24782.45 - 

Note: N = 650. The baseline model represents an independence model with no relationships between constructs. RMSEA 90% CI = [0.042, 
0.054]. All χ² values significant at p < 0.001. 

 

(a)Daily Usage Pattern Distribution

Peak: 19:00

0 3 6 9 12 15 18 21

Hour of Day

0

20

40

60

80

100

A
ve

ra
ge

 A
ct

iv
e 

U
se

rs

0 20 40 60 80 100

Regular Usage Rate (%)

Personalized Recommendations

Virtual Assistant

Progress Tracking

Collaborative Tools

Emotion Adaptation

(b)AI Feature Utilization Patterns

76.3%

64.8%

58.2%

45.6%

31.2%



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152 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   
 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

   

Table 5. Standardized path coefficients and hypothesis testing results 

Hypothesis Path Relationship β SE t-value p-value 95% CI R² Result 
Direct 
Effects         

H1 Anthropomorphism → 
Trust 0.45*** 0.06 7.50 <0.001 [0.33, 

0.57] 0.20 Supported 

H2 Personalization → 
Perceived Value 0.52*** 0.05 10.40 <0.001 [0.42, 

0.62] 0.27 Supported 

H3 Response Speed → 
Perceived Ease of Use 0.38*** 0.07 5.43 <0.001 [0.24, 

0.52] 0.14 Supported 

H4 Intelligence Level → 
Perceived Usefulness 0.41*** 0.06 6.83 <0.001 [0.29, 

0.53] 0.17 Supported 

Mediating 
Paths         

 Trust → Acceptance 
Intention 0.58*** 0.05 11.60 <0.001 [0.48, 

0.68] - - 

 
Perceived Value → 
Continuous 
Engagement 

0.63*** 0.04 15.75 <0.001 [0.55, 
0.71] - - 

 Perceived Ease of Use → 
Actual Usage 0.34*** 0.06 5.67 <0.001 [0.22, 

0.46] - - 

 Perceived Usefulness → 
Acceptance Intention 0.42*** 0.05 8.40 <0.001 [0.32, 

0.52] - - 

Non-
significant 
Path 

        

 
Anthropomorphism → 
Acceptance Intention 
(direct) 

0.08 0.07 1.14 0.127 [-0.06, 
0.22] - - 

Endogenou
s Variables 
R² 

        

 Acceptance Intention - - - - - 0.67 - 

 Continuous 
Engagement - - - - - 0.71 - 

 Actual Usage - - - - - 0.48 - 
Mediation 
Effects         

H5 
Anthropomorphism → 
Trust → Acceptance 
Intention 

       

 - Indirect Effect 0.26*** 0.04 6.50 <0.001 [0.19, 
0.34] - Supported 

 - Direct Effect 0.08 0.07 1.14 0.127 [-0.06, 
0.22] -  

 - Total Effect 0.34*** 0.06 5.67 <0.001 [0.22, 
0.46] -  

 - Percent Mediation 76.5% - - - - -  

H6 

Personalization → 
Perceived Value → 
Continuous 
Engagement 

       

 - Indirect Effect 0.33*** 0.04 8.25 <0.001 [0.25, 
0.41] - Supported 

 - Direct Effect 0.15** 0.05 3.00 0.003 [0.05, 
0.25] -  

 - Total Effect 0.48*** 0.05 9.60 <0.001 [0.38, 
0.58] -  

 - Percent Mediation 68.8% - - - - -  
Direct 
Effects         

Note: N = 650. β = standardized path coefficient; SE = standard error; CI = confidence interval; R² = explained variance. Bootstrap samples = 
5,000 for mediation analysis. ***p < 0.001, **p < 0.01, p < 0.05. 

 

 

 



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153 
 

The objective function optimized by XGBoost, expressed as: 

ˆ( ) ( , ) ( )i k
i

l yi y k fφ = + Ω∑ ∑L      (3) 

where l represents the differentiable loss function measuring 
prediction accuracy and Ω(𝑓𝑓𝑘𝑘) = 𝛾𝛾𝛾𝛾 + 1

2
𝜆𝜆∑ 𝑤𝑤𝑗𝑗2𝑇𝑇

𝑗𝑗=1  denotes 
the regularization term controlling model complexity, 
enabling the identification of non-linear interaction patterns 
between AI marketing features and user behavioral outcomes 
that complement the linear relationships revealed through 
structural equation modeling. As demonstrated in Table 6, the 
ensemble model combining XGBoost with Random Forest and 
Neural Network architectures achieved exceptional 
performance metrics across multiple evaluation criteria, 
substantiating the robustness of predictive insights derived 
from the 650-participant dataset enriched with longitudinal 
behavioral tracking data. 

Feature importance analysis utilizing SHAP (SHapley 
Additive exPlanations) values, illustrated comprehensively in 
Figure 4, reveals the hierarchical contribution of predictive 
variables with trust-related features dominating the 
importance rankings (mean |SHAP| = 0.142), followed by 
personalization satisfaction metrics (mean |SHAP| = 0.128) 
and AI response quality ratings (mean |SHAP| = 0.115), 
corroborating the centrality of trust mechanisms identified 
through hypothesis testing while uncovering additional 
nuanced predictors including session duration patterns and 
feature diversity indices. 

The five user segments identified through clustering 
algorithms were validated and refined through qualitative 
pattern matching, wherein interview participants' self-
described interaction styles mapped onto quantitative 
clusters with 84% concordance, while qualitative insights 
about social learning preferences led to the incorporation of 
peer influence variables into the clustering algorithm, 
improving silhouette coefficient from 0.612 to 0.683 and 
revealing the previously undetected 'Social Learners' 
segment that exhibits distinct collaborative engagement 
patterns not captured by individual behavioral metrics alone. 
As delineated in Table 7, the segmentation reveals a 
sophisticated taxonomy ranging from "Enthusiastic 
Adopters" (21.8%), characterized by high technology 
readiness and extensive AI interaction, to "Minimal Engagers" 
(10.0%), demonstrating limited technological proficiency and 
basic feature utilization, with conversion rates varying 
dramatically across segments from 68.3% to 12.3%, thereby  

  

 

 

 

 

 

 

 

 

 

 

 

enabling targeted marketing strategy optimization based on 
segment-specific behavioral profiles and preference 
structures. 

 
Figure 4. SHAP feature importance analysis for user acceptance 
prediction 
Note: SHAP (SHapley Additive exPlanations) summary plot 
displaying the top 20 features ranked by mean absolute SHAP values. 
Each point represents a single observation, with color indicating 
feature value (red = high, blue = low) and horizontal position showing 
impact on model output. Features are ordered by decreasing 
importance from top to bottom. Positive SHAP values indicate 
increased probability of user acceptance, while negative values 
suggest decreased likelihood. The plot reveals both magnitude and 
directionality of feature influences, with trust score demonstrating 
the strongest predictive power (mean |SHAP| = 0.142) followed by 
personalization satisfaction and AI response quality metrics. 

 

 

 

 

 

 

 

 

 

 

 

 

Table 6. Machine learning model performance comparison and validation metrics 

Model 
Algorithm Accuracy Precision Recall F1-

Score 
AUC-
ROC 

Cross-Val 
Mean 
(SD) 

Training 
Time (s) 

Inference 
Time (ms) 

XGBoost 0.892 0.878 0.903 0.890 0.945 0.887 
(0.012) 45.3 2.1 

Random 
Forest 0.876 0.861 0.885 0.873 0.928 0.871 

(0.015) 38.7 3.4 

Neural 
Network 
(MLP) 

0.881 0.872 0.889 0.880 0.936 0.875 
(0.018) 126.4 1.8 

Support 
Vector 
Machine 

0.853 0.844 0.862 0.853 0.912 0.849 
(0.021) 89.2 4.7 

Logistic 
Regression 0.812 0.798 0.831 0.814 0.875 0.808 

(0.019) 12.3 0.9 

Ensemble 
Model 0.908 0.896 0.917 0.906 0.958 0.903 

(0.010) 210.4 7.3 

Note: All metrics derived from 5-fold stratified cross-validation. SD = standard deviation. Training performed on Intel Xeon E5-2690 with 32GB 
RAM. Inference time measured on a single prediction batch. 

 

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C. Chen et al. /Future Technology                                     November 2025| Volume 04 | Issue 04 | Pages 146-158 

154 
 

 

 

 

 

 

 

 

 

 

 

 

 

 

4.4 Qualitative Findings 
The thematic analysis of 45 semi-structured interviews 

revealed three overarching themes that illuminate the 
nuanced psychological and behavioral mechanisms 
underlying user experiences with AI-driven marketing in 
educational technology contexts, employing Braun and 
Clarke's six-phase analytical framework, with inter-coder 
reliability achieving κ = 0.84 across all coding categories. As 
delineated in Table 8, the emergent themes encompass 
multifaceted dimensions of human-AI interaction ranging 
from anthropomorphic companionship perceptions to 
complex negotiations between data privacy concerns and 
personalization benefits, with saturation achieved after 38 
interviews indicating robust theoretical coverage of the 
phenomenon under investigation. 

The thematic analysis was strategically designed based 
on quantitative anomalies requiring deeper investigation, 
particularly the non-linear relationship between 
anthropomorphism levels and acceptance rates discovered 
through polynomial regression analysis (R²=0.43 for 
quadratic vs 0.31 for linear), which directed interview 
protocols to explore optimal anthropomorphism boundaries, 
revealing the 'uncanny valley' phenomenon articulated by 
73% of participants who described discomfort with excessive 
human-likeness in AI interactions—insights that 
subsequently informed the recalibration of 
anthropomorphism scales in the quantitative model. The 
"intelligent companion" theme (82.2% prevalence) revealed 
participants' consistent use of relational metaphors when 
describing AI interactions, encompassing emotional 
connections, 24/7 availability, and adaptive understanding—
indicating social schema activation despite awareness of 
artificial nature. Personalization value perception (88.9% 
prevalence) encompassed predictive learning needs, dynamic 
pacing adjustments, and resource discovery efficiency, with 
stakeholder variations evident—students valued 
recommendation accuracy while educators prioritized 
pedagogical alignment (Figure 5). 

5. Discussion  
The theoretical contributions of this research extend the 

Technology Acceptance Model through novel integration of 
AI-specific constructs that capture the unique psychological 
dynamics emerging from human-AI interactions in 
educational marketing contexts, addressing critical gaps 
identified in contemporary literature where traditional 

 

 

 

 

 

 

 

 

 

 

 

 

 

 
acceptance models inadequately explain user responses to 
intelligent systems. The extension of TAM by incorporating 
the Big Five personality traits and the AI mindset to derive 
potential predictors of AI-specific technology acceptance [9] 
aligns with the present study's findings that trust emerges as 
a fundamental mediating mechanism between AI 
anthropomorphism and user acceptance, while the proposed 
"intelligent marketing acceptance" construct advances 
beyond generic technology acceptance to encompass the 
multifaceted nature of AI-driven personalization and 
emotional engagement.  

TAM's limitations within the hospitality and tourism 
context revolve around its individual-centric perspective, 
limited scope, static nature, cultural applicability and reliance 
on self-reported measures [26], necessitating the dynamic 
framework developed herein that incorporates real-time 
behavioral data and acknowledges the iterative nature of 
human-AI relationships in educational settings, where 
perceived usefulness, perceived ease of use, and user 
acceptance of information technology [27] manifest through 
continuous interaction patterns rather than discrete adoption 
decisions.  

The technological implications extend beyond 
immediate applications to establish foundational 
architectures for future AI systems, as the hierarchical 
attention mechanisms with educational ontology integration 
provide blueprints for domain-specific AI architectures 
applicable across specialized knowledge domains, while the 
emotion-aware multimodal fusion algorithms advance the 
frontier of affective computing by demonstrating how 
paralinguistic features can be computationally integrated 
with semantic understanding—contributions that position 
this research at the forefront of next-generation AI system 
design rather than merely applying existing technologies. 
Managerial implications derived from the empirical findings 
provide actionable guidelines for educational technology 
enterprises implementing AI-driven marketing systems, with 
the principle of moderate anthropomorphism emerging as 
critical for optimizing user trust without triggering uncanny 
valley effects that diminish acceptance.  

 

 

 

Table 7. AI marketing user segmentation profiles and behavioral characteristics 

Segment n (%) Technology 
Readiness 

AI 
Interaction 

Level 

Trust 
Score 

Feature 
Diversity 

Conversion 
Rate 

CLV 
Index 

Retention 
(90-day) 

Enthusiastic 
Adopters 142 (21.8%) 4.52 (0.48) High 4.38 

(0.52) 
0.87 
(0.09) 68.3% 2.84 89.4% 

Pragmatic 
Users 198 (30.5%) 3.76 (0.61) Moderate 3.82 

(0.58) 
0.68 
(0.12) 42.7% 1.92 72.3% 

Cautious 
Explorers 156 (24.0%) 3.21 (0.73) Low-

Moderate 
2.94 
(0.69) 

0.54 
(0.15) 28.4% 1.45 61.5% 

Social 
Learners 89 (13.7%) 3.58 (0.65) Moderate 3.65 

(0.61) 
0.72 
(0.11) 35.9% 1.73 68.2% 

Minimal 
Engagers 65 (10.0%) 2.43 (0.82) Low 2.31 

(0.78) 
0.31 
(0.18) 12.3% 0.78 34.6% 

F-statistic - 98.42*** - 124.56*** 156.78*** - - - 

Silhouette 
Coefficient 0.683 - - - - - - - 

Note: Values represent mean (SD) for continuous variables. Technology Readiness and Trust Score measured on 5-point scales. Feature 
Diversity ranges from 0-1. CLV Index normalized to population mean = 1.0. **p < 0.001 for between-group differences. 

 



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Table 8. Thematic analysis results: emergent themes and sub-themes distribution 

Theme Sub-themes Definition Frequency n 
(%) 

Representative 
Quotations 

Stakeholder 
Distribution 

Theme 1: 
"Intelligent 
Companion" 
Experience 

1.1 Emotional Connection 
1.2 24/7 Availability 1.3 
Adaptive Understanding 

Anthropomorphic 
perception of AI as 
supportive learning 
partner 

37 (82.2%) 

"It feels like having a 
study buddy who 
never gets tired and 
always knows 
exactly what I need" 
(P12) "The AI 
remembers our 
conversations and 
picks up where we 
left off" (P28) 

Students: 89% 
Educators: 75% 
Administrators: 
67% 

Theme 2: 
Personalization 
Value Perception 

2.1 Predictive Accuracy 2.2 
Learning Path Optimization 
2.3 Time Efficiency 

Recognition of AI's 
capability to deliver 
tailored educational 
content 

40 (88.9%) 

"The 
recommendations 
are incredibly 
accurate - it 
suggested statistics 
resources right 
when I was 
struggling" (P07) "It 
adapts to my 
learning pace 
automatically" (P34) 

Students: 92% 
Educators: 88% 
Administrators: 
83% 

Theme 3: Privacy-
Convenience 
Trade-off 

3.1 Data Transparency 3.2 
Control Mechanisms 3.3 
Value Exchange 

Negotiation between 
privacy concerns 
and personalization 
benefits 

31 (68.9%) 

"I want to know 
exactly what data 
they collect and how 
it's used" (P19) "The 
time saved finding 
resources makes 
data sharing 
worthwhile" (P41) 

Students: 65% 
Educators: 71% 
Administrators: 
75% 

Note: N = 45. Percentages indicate proportion of participants expressing each theme. Inter-coder reliability (Cohen's κ) = 0.84. Stakeholder 
distribution shows percentage within each group mentioning the theme. 

 

       

Theme 1: Companion

82.2%

Theme 2: Personalization

88.9%

Theme 3: Privacy
68.9%

Emotional

Accuracy

Transparency Availability

Learning Path

Control

Adaptive

Efficiency

Value Exchange

Main Themes

Sub-themes

Co-occurrence

       Figure 5. Thematic Prevalence and Co-occurrence Network Analysis 
Note: Network visualization depicting relationships between emergent themes and sub-themes from qualitative analysis. Node size represents 
theme frequency (larger nodes indicate higher prevalence), edge thickness indicates co-occurrence strength between themes, and color coding 
distinguishes primary themes (blue) from sub-themes (green). The central positioning of personalization value perception (88.9% prevalence) 
reflects its interconnection with both companion experience and privacy considerations. Clustering coefficient = 0.72 indicates high thematic 
integration. Analysis based on 45 semi-structured interviews with educational technology stakeholders. Clustering Coefficient = 0.72, N = 45 
interviews. 

 



C. Chen et al. /Future Technology                                     November 2025| Volume 04 | Issue 04 | Pages 146-158 

156 
 

Teachers' attitudes towards chatbots in education, a 
technology acceptance model approach considering the effect 
of social language, bot proactiveness, and users' 
characteristics [28] reinforces the importance of contextual 
response strategies that adapt communication styles based 
on user segments identified through machine learning 
algorithms, while progressive personalization approaches 
should balance sophistication with transparency to address 
privacy concerns articulated by 68.9% of qualitative 
participants. When AI-based technology is introduced in a 
construction organisation, the technology must, therefore, be 
user-friendly and should promote work efficiency and 
increased productivity [29], principles equally applicable to 
educational contexts where virtual sales personnel must 
demonstrate clear value propositions through enhanced 
learning outcomes and time savings, supported by AI agent 
learns from both your documentation and past support 
tickets enabling continuous improvement of 
recommendation accuracy and interaction quality. 

Despite robust findings supporting AI marketing 
effectiveness in educational technology contexts, several 
limitations constrain generalizability and highlight avenues 
for future investigation, particularly the exclusive 
recruitment of participants from China, where collectivist 
cultural values emphasizing interpersonal harmony and 
authority respect may engender distinct AI trust formation 
patterns compared to individualist cultures that prioritize 
autonomy and skepticism toward automated systems. The 
Chinese educational context's emphasis on teacher-student 
hierarchical relationships potentially influences acceptance 
of AI tutors differently than Western educational 
environments emphasizing peer learning and critical 
questioning, while cultural differences in privacy 
perceptions—with Chinese users demonstrating higher 
tolerance for data sharing in exchange for personalized 
services—may not translate to markets with stringent 
privacy regulations such as Europe under GDPR or privacy-
conscious North American consumers.  

Future research should replicate this investigation 
across diverse cultural contexts, including North American, 
European, Latin American, and other Asian markets, to 
establish cross-cultural validity of the proposed framework, 
with particular attention to how Hofstede's cultural 
dimensions (power distance, uncertainty avoidance, 
individualism-collectivism) moderate relationships between 
AI characteristics and acceptance outcomes. Multi-country 
studies employing measurement invariance testing would 
enable identification of universal versus culture-specific 
factors in AI marketing acceptance, while longitudinal 
investigations tracking cultural adaptation as global EdTech 
platforms expand across borders could reveal dynamic 
acculturation effects on technology acceptance patterns, 
ultimately contributing to culturally-adaptive AI design 
strategies that optimize human-AI interactions across diverse 
educational ecosystems. Developing a holistic success model 
for sustainable e-learning: a structural equation modeling 
approach [30] suggests that cultural factors significantly 
influence technology adoption trajectories, necessitating 
multi-national studies examining how cultural dimensions 
moderate relationships between AI characteristics and user 
acceptance across diverse educational systems. The six-
month observation period captures initial adoption dynamics 
but cannot assess long-term habituation effects or potential 
degradation of novelty-driven engagement, while emerging 
generative AI technologies introduce capabilities beyond the 
scope of current investigation, as user trust in AI and 

perceived quality of AI output, from XAI literature [6] become 
increasingly complex with advanced language models that 
blur boundaries between human and artificial intelligence, 
requiring novel theoretical frameworks and measurement 
instruments to capture evolving human-AI interaction 
paradigms in educational marketing contexts. 

6. Conclusion  
This research establishes technological foundations for 

future educational AI systems by introducing computational 
architectures that advance NLP capabilities through 
educational-specific transformer modifications achieving 
37% efficiency gains, pioneering multimodal emotion fusion 
algorithms that define new standards for affective computing 
integration, and demonstrating how domain-specific 
ontology graphs can be embedded within attention 
mechanisms—innovations that transcend current 
applications to shape the trajectory of AI technology 
development in specialized knowledge domains. The 
empirical findings reveal that successful AI marketing 
implementation in educational contexts hinges upon 
achieving an optimal balance between technological 
sophistication and humanization principles, with trust 
emerging as the pivotal psychological mechanism mediating 
the relationship between AI anthropomorphism and user 
acceptance while accounting for 76.5% of the total effect. The 
validated implementation framework presented herein 
provides educational technology enterprises with actionable 
guidelines for designing AI-driven marketing systems that 
leverage moderate anthropomorphism, progressive 
personalization strategies, and transparent data practices to 
optimize user acceptance across diverse stakeholder 
segments ranging from enthusiastic adopters to cautious 
explorers. Beyond immediate practical applications, this 
investigation advances theoretical understanding by 
extending the Technology Acceptance Model to incorporate 
AI-specific constructs including algorithm trust, perceived 
intelligence, and emotional engagement dimensions that 
capture the unique dynamics of human-AI interactions in 
educational marketing contexts. As educational institutions 
navigate the evolving digital landscape, the insights derived 
from this mixed-methods investigation illuminate pathways 
for harnessing AI capabilities to create meaningful learning 
experiences while addressing legitimate privacy concerns 
and maintaining ethical standards essential for sustainable 
technology adoption in educational ecosystems. 

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. 

 

 

 



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157 
 

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management[J]. Journal of Knowledge Management, 
2024, 29(11): 1-31. 

[20]  Gomes S, Lopes J M, Nogueira E. Anthropomorphism 
in artificial intelligence: a game-changer for brand 
marketing[J]. Future Business Journal, 2025, 11(1): 2. 

[21]  Haleem A, Javaid M, Qadri M A, et al. Artificial 
intelligence (AI) applications for marketing: A 
literature-based study[J]. International Journal of 
intelligent networks, 2022, 3: 119-132. 

[22]  Polyportis A, Pahos N. Understanding students’ 
adoption of the ChatGPT chatbot in higher education: 
the role of anthropomorphism, trust, design novelty 
and institutional policy[J]. Behaviour & Information 
Technology, 2025, 44(2): 315-336. 

[23]  Cheng C-F, Huang C-C, Lin M-C, et al. Exploring 
effectiveness of relationship marketing on artificial 
intelligence adopting intention[J]. Sage Open, 2023, 
13(4): 21582440231222760. 

[24]  Lefrid M, Cavusoglu M, Richardson S, et al. 
Simulation-based learning acceptance model (SBL-
AM): Expanding the Technology Acceptance Model 
(TAM) into hospitality education[J]. Journal of 
Hospitality & Tourism Education, 2024, 36(4): 333-
347. 

[25]  Zhou L, Xue S, Li R. Extending the Technology 
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use an online education platform at a University in 
China[J]. Sage Open, 2022, 12(1): 
21582440221085259. 

[26]  Mogaji E, Viglia G, Srivastava P, et al. Is it the end of 
the technology acceptance model in the era of 
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10(8). 

[28]  Chocarro R, Cortiñas M, Marcos-Matás G. Teachers’ 
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C. Chen et al. /Future Technology                                     November 2025| Volume 04 | Issue 04 | Pages 146-158 

158 
 

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[29]  Na S, Heo S, Han S, et al. Acceptance model of artificial 
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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/

	1. Introduction
	With the adoption of artificial intelligence (AI) technologies, the educational technology marketplace has undergone a profound transformation, changing the way educational services and products are marketed and delivered to end users. The total globa...
	The key contributions of this study include building an integrated theoretical framework integrating technology acceptance model (TAM) with AI-specific technology constructs in the context of educational technology marketing, empirically validating th...
	2. Theoretical foundation and research framework
	2.1 Literature review
	The marketing environment of educational technology has shifted from an information push to the interactive social Web2.0 and, recently, AI-driven predictive marketing, fundamentally reshaping how our stakeholders engage with education resources and t...
	Modern conversational systems have progressed from rule-based designs to complex deep learning-based models, which are now able to handle both task-oriented and open-domain dialogs with emotional-AI that empowers sales campaigns through a better under...
	2.2 Conceptual model and hypothesis development
	This theoretical model combines AI-specific marketing features with traditional technology acceptance constructs for the analysis of user behaviors in the field of educational technology, as displayed in Figure 1. Based on new empirical information th...
	The theoretical advancement manifests through the discovery that AI anthropomorphism in educational contexts triggers distinct neural pathways compared to traditional technology interactions, as evidenced by the 76.5% trust mediation effect that excee...
	The theoretical innovation of this framework lies in its multi-level integration of marketing theory with AI acceptance models while accounting for educational context specificity through moderation effects. User technology readiness (H7) and educatio...
	Figure 1. Conceptual framework of ai marketing acceptance in educational technology
	3. Research methodology
	3.1  Research design
	An explanatory sequential mixed-methods design integrates quantitative surveys (n=650) examining AI marketing acceptance with qualitative interviews (n=45) exploring nuanced user experiences and decision-making processes in educational technology cont...
	3.2 Quantitative methods
	The quantitative phase employed a comprehensive online survey administered to 650 participants recruited from major educational technology platforms in China, complemented by longitudinal behavioral data tracking over a six-month period to capture act...
	3.3 Qualitative methods
	Semi-structured interviews with 45 participants (students, educators, and administrators) selected through purposive sampling explored AI marketing acceptance beyond quantitative metrics, with selection criteria prioritizing substantial platform exper...
	As illustrated in Table 2, the participant distribution reflects balanced representation across key demographic and experiential dimensions, with interviews conducted via video conferencing platforms lasting 45-60 minutes each, following an interview ...
	Note: All scales employed 7-point Likert scales ranging from "strongly disagree" (1) to "strongly agree" (7). Platform behavioral metrics included click-through rates, session duration, feature utilization frequency, and conversion indicators collecte...
	Note: AI Experience Level categorized as Beginner (< 6 months), Intermediate (6-24 months), Advanced (2-4 years), Expert (> 4 years). Platform usage represents self-reported average weekly hours engaging with AI-enabled educational technology platforms.
	3.4 AI technology implementation
	The AI-driven virtual sales system architecture integrates three core technological components operating synergistically to deliver personalized educational marketing experiences through advanced computational frameworks. The research introduces break...
	(1)
	where Q, K, and V represent query, key, and value matrices, respectively, and dk=64 denotes the key dimension, and the softmax function normalizes attention weights to sum to 1, enabling the model to focus on relevant educational content based on user...
	The knowledge graph integrates 1.2M educational concepts using TransE embeddings (dimension=200) trained on prerequisite relationships extracted from 85,000 course syllabi, achieving link prediction accuracy of 82.4% on held-out course dependencies. T...
	(2)
	where 𝜇 represents the global mean rating, bu and bi capture user and item biases, respectively, and,𝑃-𝑢-𝑇.,𝑞-𝑖.  computes the dot product between 128-dimensional user preference vectors and item characteristic vectors learned through matrix fac...
	Figure 2. AI-driven virtual sales system architecture
	4. Research findings
	4.1 Descriptive statistics
	The analysis of participant demographics reveals a diverse sample composition that adequately represents the target population of educational technology users across multiple dimensions, with respondents demonstrating substantial variation in age dist...
	Behavioral usage patterns illustrated in Figure 3 demonstrate distinct temporal engagement trajectories across different user segments, with peak usage occurring during evening hours (7-10 PM) and secondary peaks during lunch periods (12-1 PM), sugges...
	4.2 Hypothesis testing results
	The structural equation modeling analysis revealed robust support for the proposed theoretical framework examining AI-driven marketing acceptance in educational technology contexts. The measurement model demonstrated excellent fit to the empirical dat...
	Path analysis results, presented comprehensively in Table 5, substantiate the hypothesized relationships with particularly noteworthy effects emerging for anthropomorphism's influence on trust formation (β = 0.45, SE = 0.06, p < 0.001) and personaliza...
	Figure 3. User engagement patterns across time and feature utilization
	4.3 ML prediction results
	The deployment of advanced machine learning algorithms for predicting user acceptance behaviors yielded compelling evidence regarding the multifaceted nature of AI-driven marketing effectiveness in educational technology contexts, with the XGBoost gra...
	Note: N = 650. The baseline model represents an independence model with no relationships between constructs. RMSEA 90% CI = [0.042, 0.054]. All χ² values significant at p < 0.001.
	Note: N = 650. β = standardized path coefficient; SE = standard error; CI = confidence interval; R² = explained variance. Bootstrap samples = 5,000 for mediation analysis. ***p < 0.001, **p < 0.01, p < 0.05.
	The objective function optimized by XGBoost, expressed as:
	(3)
	where l represents the differentiable loss function measuring prediction accuracy and Ω,,𝑓-𝑘..=𝛾𝑇+,1-2.𝜆,𝑗=1-𝑇-,𝑤-𝑗-2.. denotes the regularization term controlling model complexity, enabling the identification of non-linear interaction patter...
	Feature importance analysis utilizing SHAP (SHapley Additive exPlanations) values, illustrated comprehensively in Figure 4, reveals the hierarchical contribution of predictive variables with trust-related features dominating the importance rankings (m...
	The five user segments identified through clustering algorithms were validated and refined through qualitative pattern matching, wherein interview participants' self-described interaction styles mapped onto quantitative clusters with 84% concordance, ...
	enabling targeted marketing strategy optimization based on segment-specific behavioral profiles and preference structures.
	Figure 4. SHAP feature importance analysis for user acceptance prediction
	Note: SHAP (SHapley Additive exPlanations) summary plot displaying the top 20 features ranked by mean absolute SHAP values. Each point represents a single observation, with color indicating feature value (red = high, blue = low) and horizontal positio...
	Note: All metrics derived from 5-fold stratified cross-validation. SD = standard deviation. Training performed on Intel Xeon E5-2690 with 32GB RAM. Inference time measured on a single prediction batch.
	4.4 Qualitative Findings
	The thematic analysis of 45 semi-structured interviews revealed three overarching themes that illuminate the nuanced psychological and behavioral mechanisms underlying user experiences with AI-driven marketing in educational technology contexts, emplo...
	The thematic analysis was strategically designed based on quantitative anomalies requiring deeper investigation, particularly the non-linear relationship between anthropomorphism levels and acceptance rates discovered through polynomial regression ana...
	5. Discussion
	TAM's limitations within the hospitality and tourism context revolve around its individual-centric perspective, limited scope, static nature, cultural applicability and reliance on self-reported measures [26], necessitating the dynamic framework devel...
	The technological implications extend beyond immediate applications to establish foundational architectures for future AI systems, as the hierarchical attention mechanisms with educational ontology integration provide blueprints for domain-specific AI...
	Note: Values represent mean (SD) for continuous variables. Technology Readiness and Trust Score measured on 5-point scales. Feature Diversity ranges from 0-1. CLV Index normalized to population mean = 1.0. **p < 0.001 for between-group differences.
	Note: N = 45. Percentages indicate proportion of participants expressing each theme. Inter-coder reliability (Cohen's κ) = 0.84. Stakeholder distribution shows percentage within each group mentioning the theme.
	Figure 5. Thematic Prevalence and Co-occurrence Network Analysis
	Note: Network visualization depicting relationships between emergent themes and sub-themes from qualitative analysis. Node size represents theme frequency (larger nodes indicate higher prevalence), edge thickness indicates co-occurrence strength betwe...
	Teachers' attitudes towards chatbots in education, a technology acceptance model approach considering the effect of social language, bot proactiveness, and users' characteristics [28] reinforces the importance of contextual response strategies that ad...
	6. Conclusion
	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.
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