University of Ciego de Ávila Máximo Gómez Báez

ISSN: 2309-8333

RNPS: 2411

|13(2) |2025|

This is an Open Access article under the license CC BY-NC-SA 4.0 (https://creativecommons.org/licenses/by-nc-sa/4.0/)

Estrategia y Gestión Universitaria EGU

Scientific and technological

research article

How to cite:

Vivanco Enriquez, J. L.,

Espinoza Gómez, S. T., Mateo Nuñez, H. R.,

Vilca Ramirez, P. A., & Chincha Llecllish, J.

M. (2025). Modeling of the adoption of

artificial intelligence tools in higher

education: a TAM -based approach.

Estrategia y Gestión Universitaria

, 13(2),

e9024.

https://doi.org/10.5281/zenodo.17653760

Received: 20/08/2025

Accepted: 15/11/2025

Published: 24/11/2025

Corresponding author:

jlvivanco@pucp.edu.pe

Conflict of interest:

the authors declare

that they have no conflict of interest,

which may have influenced the results

obtained or the proposed interpretations

Modeling of the adoption of artificial

intelligence tools in higher education:

a TAM -based approach

Modelado de la adopción de

herramientas de inteligencia artificial

en la educación superior: un enfoque

basado en TAM

Modelagem da adoção de ferramentas

de inteligência artificial no ensino

superior: uma abordagem baseada em

TAM

Abstract

Introduction: artificial intelligence (AI) has emerged as one of

the most transformative technologies in higher education,

providing tools that facilitate personalized learning and

optimize academic and administrative processes; however, its

adoption depends on individual and organizational factors.

Objective: to analyze the influence of perceived usefulness,

ease of use, attitude, and intention to use on the adoption of

AI tools among university students through the Technology

Acceptance Model (TAM).

Method: a quantitative descriptive-

correlational approach was employed, applying a validated

questionnaire to 55 students with prior experience in AI, and

the data were processed using Pearson correlations and

multiple regression analysis. Results: intention to use was

identified as the most relevant predictor of actual use (β =

0.679, p < 0.001), followed by perceived usefulness (β = 0.374,

p = 0.024), while ease of use had no significant impact, and

attitude showed an inconclusive negative relationship.

Conclusion: the TAM model proved pertinent in explaining the

adoption of AI tools in higher education, highlighting that

perceived usefulness and intention to use are the determining

factors to ensure effective implementation of these

technologies.

Keywords: artificial intelligence, higher education,

technology adoption, perceived usefulness

Resumen

Introducción: la inteligencia artificial (IA) se ha consolidado

como una de las tecnologías más transformadoras en la

educación superior, al ofrecer herramientas que facilitan la

personalización del aprendizaje y optimizan los procesos

académicos y administrativos; sin embargo, su adopción

depende de factores individuales y organizacionales.

Jesús L. Vivanco Enriquez

Pontificia Universidad Católica del Perú

https://orcid.org/0000-0002-4482-7726

jlvivanco@pucp.edu.pe

Perú

Silvana Tabata Espinoza Gómez

Universidad Católica de Santa María

https://orcid.org/0009-0000-1441-7470

s.t.espinozagomez@ieee.org

Perú

Hayashi Rafael Mateo Nuñez

Universidad Nacional Federico Villareal

https://orcid.org/0009-0002-5705-0757

hayashi@fablablima.org

Perú

Piero Alejandro Vilca Ramirez

Universidad Nacional Federico Villareal

https://orcid.org/0009-0004-0376-5479

pier388_bvl@hotmail.c

Perú

Jesús Manuel Chincha Llecllish

Universidad de San Martín de Porres

https://orcid.org/0000-0002-5083-1043

jmanuel.chincha@gmail.com

Perú

Estrategia y Gestión Universitaria

ISSN

: 2309-8333

RNPS:

2411

13(2) | July-December |2025|

Jesús Manuel Chincha Llecllish |

Objetivo:

analizar la influencia de la utilidad percibida, la facilidad de uso, la

actitud e intención de uso en la adopción de herramientas de IA en estudiantes

universitarios mediante el Modelo de Aceptación Tecnológica (TAM).

Método:

empleó un enfoque cuantitativo descriptivo-correlacional, aplicando un

cuestionario validado a 55 estudiantes con experiencia previa en IA, y los datos

fueron procesados mediante correlaciones de Pearson y regresión múltiple.

Resultados:

la intención de uso se configuró como el predictor más relevante del

uso real (β = 0.679, p < 0.001), seguida por la utilidad percibida (β = 0.374, p =

0.024), mientras que la facilidad de uso no tuvo un impacto significativo, y la

actitud mostró una relación negativa no concluyente.

Conclusión:

el modelo TAM

resultó pertinente para explicar la adopción de herramientas de IA en la

educación universitaria, destacando que la utilidad percibida y la intención de

uso son los factores determinantes para garantizar una implementación efectiva

de estas tecnologías.

Palabras clave:

inteligencia artificial, educación superior, adopción tecnológica,

utilidad percibida

Resumo

Introdução: a inteligência artificial (IA) consolidou-se como uma das tecnologias

mais transformadoras no ensino superior, ao oferecer ferramentas que facilitam a

personalização da aprendizagem e otimizam os processos acadêmicos e

administrativos; entretanto, sua adoção depende de fatores individuais e

organizacionais. Objetivo: analisar a influência da utilidade percebida, da

facilidade de uso, da atitude e da intenção de uso na adoção de ferramentas de

IA por estudantes universitários, por meio do Modelo de Aceitação Tecnológica

(TAM). Método: foi empregado um enfoque quantitativo descritivo-correlacional,

aplicando-se um questionário validado a 55 estudantes com experiência prévia em

IA, e os dados foram processados por meio de correlações de Pearson e regressão

múltipla. Resultados: a intenção de uso configurou-se como o preditor mais

relevante do uso real (β = 0.679, p < 0.001), seguida pela utilidade percebida (β =

0.374, p = 0.024), enquanto a facilidade de uso não apresentou impacto

significativo, e a atitude mostrou uma relação negativa inconclusiva. Conclusão:

o modelo TAM mostrou-se pertinente para explicar a adoção de ferramentas de IA

na educação superior, destacando que a utilidade percebida e a intenção de uso

são os fatores determinantes para garantir uma implementação efetiva dessas

tecnologias.

Palavras-chave:

inteligência artificial, ensino superior, adoção tecnológica,

utilidade percebida

Jesús Manuel Chincha Llecllish |

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Introduction

Artificial Intelligence (AI) has established itself as one of the most

transformative technologies in the educational domain, offering significant

opportunities to personalize teaching, optimize administrative processes, and

enhance pedagogical decision-making (Galdames, 2024). In the context of higher

education, the incorporation of AI-based tools—such as intelligent tutors, automated

assessment systems, predictive analysis of student performance, and virtual

assistants—has the potential to redefine pedagogical practices, learning

environments, and institutional management.

Al Badi et al. (2024) found a positive correlation between the components

of the Technology Acceptance Model (TAM) and the factors influencing the use of

Microsoft Teams in virtual learning. However, aspects such as technological skill

level and years of teaching experience did not show significant differences in faculty

perceptions regarding the platform.

The emergence of AI is transforming teaching methods, administrative

processes, and learning strategies in the university context (Morales, 2025;

Galdames, 2024). Various studies have demonstrated its potential to personalize

education, optimize student performance assessment, and enhance institutional

efficiency (Contreras & Guerrero, 2025). Nevertheless, despite its growing adoption,

gaps remain in understanding the factors that condition its effective implementation

by students and educators, especially in contexts characterized by resource

limitations or cultural resistances (Widodo et al., 2024).

The most significant challenges identified in the literature lie in the

complexities of incorporating AI tools in academic contexts, where perceived utility,

usability, and attitudes toward technology are crucial (Davis, 1989; Venkatesh et al.,

2003). Research by Kanont et al. (2024) and Zambrano Vera et al. (2024) has

examined these variables (perceived usefulness, ease of use, and intention to use),

yielding results that differ depending on the context, thereby highlighting the

importance of conducting further studies that take into account the specificities of

each educational setting. The link between intent to use and actual adoption

behavior requires more investigation, as it does not always lead to sustainable

implementation (Sergeeva et al., 2025).

The TAM model serves as a reference framework used to forecast the

intention and usage of technologies in higher education. Vanduhe et al. (2020)

combined the TAM model with social motivation and Task-Technology Fit (TTF) to

analyze the intention to continue using gamification in higher education. TAM has

been employed to analyze the adoption of various educational technologies, such as

Learning Management Systems (LMS) (Fathema et al., 2015), mobile technologies

(Buana et al., 2021), e-learning, YouTube as a learning medium (Chintalapati et al.,

2017), cloud computing for academic purposes (Amron et al., 2021), and

gamification for training (Vanduhe et al., 2020).

The foundations of the TAM model are theoretical and support the process

of analyzing technology adoption (Buana et al., 2021). Research frequently employs

a pure TAM approach, focusing on usability and ease of use (Chintalapati et al.,

2017). Recently, studies have adapted the pure TAM model by adding other relevant

Jesús Manuel Chincha Llecllish |

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external constructs in higher education (Fathema et al., 2015). Linear regression

analysis and structural equation modeling (PLS-SEM) are typical strategies for

evaluating and validating the TAM model in this area (Amron et al., 2021).

Furthermore, the TAM model has been systematically studied to examine its

application for understanding the sustainability of higher education, particularly in

the context of the COVID-19 pandemic (Rosli et al., 2022).

User perceptions of usefulness (UP) of AI and a growth mindset are crucial

elements that impact user attitudes towards technology adoption, serving as

essential components in the transition from consideration to active incorporation of

AI in various settings (Ibrahim et al., 2025).

The implementation of artificial intelligence (AI) instruments in higher

education symbolizes a revolution in pedagogy that demands detailed and organized

study. In this context, Mourtajji and Arts-Chiss (2024) indicate that the current

exploratory study marks the beginning of a multi-stage research process, starting

with an extensive qualitative analysis aimed at educators to refine the conceptual

model and understand the professional and personal applications of technologies

such as ChatGPT. Subsequently, a quantitative phase focusing on student

perceptions and actions is proposed, facilitating comparisons of AI adoption

dynamics between educators and students.

This research aimed to provide evidence on how perceptions of usefulness,

ease of use, and attitudes influence decisions to integrate AI technologies in the

academic sphere. The results contribute to existing literature and offer insights for

designing educational policies that promote more effective and equitable adoption

of AI in higher education institutions.

Methods and materials

This research adopted a descriptive-correlational quantitative method,

focusing on modeling the implementation of artificial intelligence (AI) tools in higher

education based on the Technology Acceptance Model (TAM). Sánchez-Prieto et al.

(2016) studied how the adoption of mobile technologies in this sector could be

understood through the TAM, highlighting its development and the crucial elements

determining adoption by educators.

The objective was to examine the perceptions, attitudes, and usage

intentions of AI tools among university students, considering five dimensions:

perceived usefulness, perceived ease of use, attitude towards use, intention to use,

and actual use.

The target population comprised higher education students from

technological institutions located in Metropolitan Lima. A non-probabilistic

convenience sampling method was employed, selecting students from different

majors and academic cycles who had prior exposure to AI tools during their training.

The final sample consisted of 55 participants, who voluntarily completed a digital

questionnaire.

Jesús Manuel Chincha Llecllish |

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For data collection, a structured questionnaire was designed, comprising 20

items distributed across the five dimensions of the TAM model adapted to the

educational context.

1. Perceived usefulness (4 items): Assesses the extent to which students

believe that the use of AI tools enhances their academic performance and facilitates

learning.

2. Perceived ease of use (4 items): Measures students' perceptions regarding

the simplicity and accessibility of using AI tools in their academic activities.

3. Attitude towards use (3 items): Investigates the overall evaluation that

students have regarding the use of AI as support in their education.

4. Intention to use (3 items): Evaluates students' future willingness to

continue using AI tools and their readiness to recommend them.

5. Actual use (4 items): Measures the frequency and type of current use of

AI tools (such as ChatGPT, Copilot, or Grammarly) in specific academic activities.

Each item was framed as a statement and evaluated using a 5-point Likert

scale, ranging from 1 (strongly disagree) to 5 (strongly agree).

To measure the internal consistency of the instrument, the Cronbach's alpha

coefficient (α) was determined, resulting in a total value of α = 0.974, indicating

high reliability across all items.

Data collection took place in the second quarter of 2025, using digital forms

that were distributed among student groups. Participation in the survey was ensured

to be voluntary and anonymous, thereby respecting ethical principles related to

educational research.

The data were processed and analyzed using the Google Colab platform,

utilizing the Pingouin library. The pingouin.cronbach_alpha() function was employed

to calculate the Cronbach's alpha coefficient and conduct multiple regression

analysis. This tool allowed for the assessment of the internal consistency of the

questionnaire, both overall and within each dimension of the TAM model.

Results and discussion

The findings derived from the study of the correlations among the constructs

of the Technology Acceptance Model (TAM), used in the analysis of the adoption of

artificial intelligence (AI) tools in higher education. Table 1 displays the Pearson

correlations among the constructs: perceived usefulness (UP), perceived ease of use

(PEOU), attitude towards use (ATU), intention to use (IU), and actual use (UB).

Table 1

Correlations among TAM model constructs

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PEOU

ATU

1.00

0.73

0.79

0.77

0.75

PEOU

0.73

1.00

0.63

0.76

0.73

ATU

0.79

0.63

1.00

0.78

0.62

0.77

0.76

0.78

1.00

0.83

0.75

0.73

0.62

0.83

1.00

Source: Authors’ own elaboration.

The findings revealed positive and significant connections among all studied

constructs. Perceived usefulness (UP) demonstrated a strong correlation with

attitude towards use (ATU) (r = 0.79), intention to use (IU) (r = 0.77), and actual use

(UB) (r = 0.75), thereby corroborating the main hypothesis of the TAM model

regarding how perceived usefulness directly influences technology adoption.

A high correlation was shown between perceived ease of use (PEOU) and

intention to use (r = 0.76) and actual use (r = 0.73), thereby supporting its role as a

facilitator of adoption behavior. However, its correlation with attitude towards use

(r = 0.63) was moderate, indicating that, in this scenario, the perception of ease

contributed to a positive disposition, though not decisively.

Finally, intention to use (IU) emerged as the primary predictor of actual use

(UB), with a correlation of r = 0.83—the highest in the matrix—underscoring its

mediating role between initial perceptions (UP, PEOU, ATU) and actual usage

behavior. Figure 1 presents the results obtained from the correlation analysis.

Figure 1

Correlations among TAM model constructs applied

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Source: Authors’ own elaboration.

The results of the multiple linear regression model (Table 2) indicated that,

among the analyzed constructs, intention to use (IU) emerged as the most important

predictor of actual use of artificial intelligence (AI) tools (UB) in the context of

higher education. The standardized coefficient for IU was β = 0.679, and its p-value

was p < 0.001, indicating a strong and conclusive positive impact from a statistical

perspective.

Similarly, perceived usefulness (UP) was identified as a relevant factor (β =

0.374, p = 0.024), indicating that as students perceive AI tools to add value to their

academic tasks, their level of use increases.

Perceived ease of use (PEOU) was not significant (p = 0.2197), suggesting

that it did not have a direct effect on actual use when controlling for the other

factors in this model. This finding could be interpreted as a prioritization of

functional advantages over operational advantages in academic environments,

where references are more often made to effects on learning or performance.

Meanwhile, attitude towards use (ATU) presented an inversely proportional

relationship with actual use (β = -0.208) but did not reach significance at the 5%

level (p = 0.0992). This indication revealed some complexity in the relationship

between attitudinal tendencies and practical action, which may require

considerations from a qualitative research perspective or mediation frameworks.

Table 2

Predictors of AI tools use (UB)

Independent

variable

Coefficient

(β)

Standard

error

t-value p-value

95% CI

Lower

95% CI

Upper

Constant -0.087 0.362 -0.239 0.8117 -0.814 0.64

Perceived

usefulness

(UP)

0.37 0.16 2.33 0.02 0.05 0.70

Perceived

ease of use

(PEOU)

0.17 0.13 1.24 0.22 -0.10 0.43

Attitude

towards use

(ATU)

-0.21 0.12 -1.68 0.10 -0.46 0.04

Intention to

use (IU)

0.68 0.15 4.52 0.00 0.38 0.98

Source: Authors’ own elaboration.

Intention to use (IU) was established as the most potent and significant

predictor of actual use (UB) of AI tools, consistent with previous research that

identifies intention as the best determinant of actual behavior in technological

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contexts (Davis, 1989; Venkatesh et al., 2003).

Perceived usefulness (PU) also demonstrated a positive and significant

impact on actual use, reinforcing the idea that users primarily value the functional

benefits these technologies bring to their academic activities. This finding aligns

with the literature highlighting perceived usefulness as a key driver in technology

adoption, especially in contexts where performance improvement is prioritized

(Venkatesh et al., 2003).

These results are consistent with reports by Yong et al. (2010), who observed

that the use of ICT increases as students progress through their university education,

largely due to curricular demands, especially in technological fields. Additionally,

they identified that students with a three-year bachelor's degree tend to use ICT

more compared to those with only two years of study. Calle-Díaz et al. (2024)

highlighted that perceived usefulness and ease of use directly influence students'

willingness to employ technological tools and share academic content. Greater

technological acceptance leads to more effective information dissemination,

underscoring the importance of promoting platforms perceived as accessible and

functional within the educational environment.

In contrast, perceived ease of use (PEOU) was not a significant predictor of

actual use when examined alongside the other constructs, although it remained

significant in its relationship with intention to use and actual use. This could indicate

that, within higher education, ease of use may serve more as an indirect facilitator

influencing intention to use rather than directly predicting effective behavior. It can

be assumed that students with prior experience or digital skills may not view ease

of use as a significant barrier, giving more importance to the advantages than to the

convenience of operations.

Research such as that by Ngabiyanto et al. (2021) provided relevant nuances

by extending the TAM model, demonstrating that ease of use does not always

positively affect adoption intention. In their study, teacher disposition was the most

determinant factor, even above perceived usefulness, while ease of use had a

negative significant effect on the intention to utilize online learning platforms.

Furthermore, there exists a negative, although not significant, link between

attitude towards use (ATU) and actual use, suggesting complexity and heterogeneity

in attitudes during the technology adoption process. This result highlights the need

for future research to analyze how contextual, emotional, or social factors may

mediate this relationship, utilizing qualitative techniques or more sophisticated

structural models.

In this context, Al-Abdullatif (2024) expanded the Technology Acceptance

Model (TAM) by integrating AI competency, intelligent TPACK, and perceived trust,

emphasizing the importance of considering not only individual variables but also

organizational and pedagogical factors to more explicitly explain the barriers and

facilitators for adoption. He warns against the limitations of using self-reported

measures, which may not accurately reflect actual technology use, suggesting that

future research should couple these measures with observational data for a more

precise view of AI adoption as part of university teaching.

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The correlations established among the constructs corroborated the internal

consistency of the TAM model in this analysis, reinforcing the idea that initial

perceptions impact intention, which in turn affects final behavior. Similarly, Barz et

al. (2024) validated the TAM model among university students, highlighting the

additionality of self-regulated learning and the inclination towards technology in

increasing acceptance of e-learning environments. Although they also considered

digital self-efficacy as an external construct, they found no significant effect in its

relationship with perceived ease of use or perceived usefulness, evidencing that

technology adoption can be a complex process that requires further research.

Limitations and future research directions

The study has limitations related to the small sample size (N = 55), which

restricts the stability and generalization of the results. Additionally, the use of a

non-probability convenience sampling method limits the external validity of the

findings. Although the Cronbach's alpha (α = 0.974) reflects good reliability across

the items, it is recommended to refine and validate the instruments in future

research. To strengthen the robustness and applicability of the results, it is

suggested to increase the sample size, employ probability sampling techniques, and

explore new variables.

Conclusions

This study reaffirms the usefulness and validity of the TAM for explaining the

implementation of artificial intelligence tools in the context of higher education. It

has been determined that Intention to Use (IU) is the most significant predictor of

actual use, indicating that motivation and willingness to employ AI are essential

elements for the effective implementation of these tools.

Moreover, Perceived Usefulness (PU) also plays an important role by

positively influencing actual use, reflecting the fact that individuals using these tools

value the practical and functional benefits that AI can provide to their academic

activities. In contrast, Perceived Ease of Use (PEOU) has been established as not

significant for the direct prediction of actual use, suggesting that in the context of

higher education, the simple and successful utilization of these tools may not play

as important a role compared to the perception of value in using the corresponding

technology.

On the other hand, the attitude towards use demonstrated a negligible

negative correlation with actual use, providing evidence that suggests a complex

relationship, encouraging its study under complementary methodological

approaches.

Thus, educational institutions should prioritize strategies that enhance the

perceived usefulness of artificial intelligence through formative practice, promote

intention to use via ongoing awareness programs, understand attitudes through

qualitative studies to counteract barriers, and monitor technical and organizational

aspects that may make adoption more likely. All these guidelines are essential to

ensure the effective and sustained use of AI tools in the academic realm.

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About the main author

Jesús L. Vivanco Enriquez

Lecturer in workshops and innovation labs in higher

education, with work experience as a database assistant and creator of educational

content on AI. Master's degree in Innovation and Technology Management and Policy

from the Pontifical Catholic University of Peru and Bachelor's degree in Education

Sciences from the National University of Educ

ation 'Enrique Guzmán y Valle'.

Declaration of author responsibility

Jesús L. Vivanco Enriquez

Conceptualization, Data curation, Formal analysis,

Research, Methodology, Resources, Software, Supervision, Validation/Verification,

Visualization,

Writing/original draft and Writing, review and editing.

Silvana Tabata Espinoza Gómez

Conceptualization, Data Curation, Formal

Analysis. Original Draft/Writing and Editing

Hayashi Rafael Mateo Nuñez

Conceptualization, Resources, Software,

Supervision, Validation. Original draft and writing

Piero Alejandro Vilca Ramirez

Formal Analysis, Research, Resources, Formal

Analysis, Supervision. Original Drafting and Writing

Jesús Manuel Chincha Llecllish 5:

Formal Analysis, Research, Resources,

Formal

Analysis, Supervision.

Original Drafting and Writing.

Financing:

Own resources.

Special Acknowledgments: