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Artificial intelligence and the learning process

in Economics students

La inteligencia artificial y el proceso de aprendizaje en

estudiantes de Ciencias Económicas

Santiago Vinueza-Vinueza

Universidad César Vallejo, Piura, Perú

Doctorado en Educación

svinuezav@ucvvirtual.edu.pe

https://orcid.org/0009-0008-7159-6098

Alejandra Fonseca-Factos

Ministerio de educación, Quito, Ecuador

Distrito 17d11 Mejía Rumiñahui

sonia.fonseca@educacion.gob.ec

https://orcid.org/0000-0002-2103-9698

(Received on: 02/03/2026; Accepted on: 02/04/2026; Final version received on: 29/05/2026)

Suggested citation: Vinueza-Vinueza, S. y Fonseca-Factos, A. (2026). Artificial intelligence

and the learning process in Economics students. Revista Cátedra, 9(2), 34-48

Abstract

The integration of artificial intelligence (AI) in higher education has generated new

opportunities to transform teaching and learning processes through tools that facilitate

content personalization, automated feedback, and access to digital educational resources.

Despite its increasing incorporation in university settings, there is still limited empirical

evidence on its impact on the learning process in Latin American contexts, particularly in

areas related to Economics. Therefore, this study aimed to analyze the influence of AI on the

learning process of students in the Statistics, Economics, and Finance programs at the

Central University of Ecuador, with an emphasis on its relationship to motivation and

academic performance. The research was conducted using a quantitative approach, with a

non-experimental, cross-sectional design. The population consisted of 243 students

enrolled in their second and third semesters, from which a sample of 149 was selected using

simple random, stratified proportional sampling. Data was collected using the Study

Processes Questionnaire (SPC), composed of 42 items evaluated using a Likert-type scale.

The analysis included descriptive statistics and inferential tests such as Student's t-test,

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Pearson correlations, Kendall's Tau-b, and Spearman's rho. The results show that artificial

intelligence positively influences academic motivation (M = 3.44; p < .001) and academic

performance (M = 2.96; p < .001), while 95.3% of students reported medium or high levels

of motivation and perception of educational quality. In conclusion, the pedagogical

integration of artificial intelligence-based tools can contribute to strengthening the learning

process in higher education.

Keywords

Artificial intelligence, learning process, academic motivation, academic performance, higher

education.

Resumen

La integración de la inteligencia artificial (IA) en la educación superior ha generado nuevas

oportunidades para transformar los procesos de enseñanza-aprendizaje mediante

herramientas que facilitan la personalización de contenidos, la retroalimentación

automatizada y el acceso a recursos educativos digitales. A pesar de su creciente

incorporación en entornos universitarios, aún existe limitada evidencia empírica sobre su

impacto en el proceso de aprendizaje en contextos latinoamericanos, particularmente en

áreas vinculadas a las Ciencias Económicas. Por ello, el presente estudio tuvo como objetivo

analizar la influencia de la IA en el proceso de aprendizaje de los estudiantes de las carreras

de Estadística, Economía y Finanzas de la Universidad Central del Ecuador, con énfasis en

su relación con la motivación y el desempeño académico. La investigación se desarrolló bajo

un enfoque cuantitativo, con diseño no experimental y corte transversal. La población

estuvo conformada por 243 estudiantes matriculados en segundo y tercer semestre, de los

cuales se seleccionó una muestra de 149 mediante muestreo aleatorio simple y estratificado

proporcional. Para la recolección de datos se aplicó el Cuestionario de Procesos de Estudio

(CPE), compuesto por 42 ítems evaluados mediante escala tipo Likert. El análisis incluyó

estadísticos descriptivos y pruebas inferenciales como la t de Student, correlaciones de

Pearson, Tau-b de Kendall y Rho de Spearman. Los resultados evidencian que la inteligencia

artificial influye positivamente en la motivación académica (M = 3.44; p < .001) y en el

desempeño académico (M = 2.96; p < .001), mientras que el 95.3 % de los estudiantes

reportó niveles medios o altos de motivación y percepción de calidad educativa. En

conclusión, la integración pedagógica de herramientas basadas en inteligencia artificial

puede contribuir al fortalecimiento del proceso de aprendizaje en la educación superior.

Palabras clave

Inteligencia artificial, proceso de aprendizaje, motivación académica, desempeño

académico, educación superior.

1. Introduction

Rapid technological development has significantly transformed the social, economic, and

educational spheres, generating the need to rethink traditional teaching models to meet the

demands of an increasingly digital society. In this context, AI has acquired a relevant role in

contemporary educational processes. According to Suárez-Lima et al. (2025), AI can be

understood as “the ability of machines to simulate human cognitive processes such as

learning, adaptation, and decision-making” (p. 3), which has allowed its progressive

incorporation into various fields of knowledge, including education.

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In the field of higher education, several studies agree on the potential of AI to transform

teaching and learning processes. In this regard, Tamayo-Arellano et al. (2024) argue that AI

fosters “new forms of interaction between teachers, students, and digital content” (p. 70).

These technologies enable the development of more flexible and personalized educational

practices, centered on student needs (Gutiérrez-Castillo et al., 2025, p. 194). Additionally,

the implementation of intelligent tutors and automatic feedback systems contributes to

improving the quality of university learning by offering individualized academic support

(Espinoza-Vidaurre et al., 2024, p. 405). Similarly, artificial intelligence plays a significant

role in the digital transformation of higher education, highlighting its influence on the

redefinition of educational practices and on current debates regarding its integration into

learning contexts (García-Peñalvo, 2023).

Other authors also emphasize that the incorporation of adaptive learning systems allows

for adjusting content and activities according to each student's pace and characteristics. In

this regard, Grijalva-Maigua et al. (2025) point out that these tools “facilitate the

construction of dynamic educational environments that promote student autonomy and

commitment to their own learning process” (p. 33). In line with this perspective, it is also

evident that the use of generative artificial intelligence tools has expanded access to

specialized information and fostered the development of analytical skills in areas such as

economics, finance, and statistics (Ribas and Provasi, 2024, p. 5).

Several empirical studies conducted in Latin America have also highlighted the importance

of strengthening digital competencies in higher education. In this sense, Cabero-Almenara

et al. indicate that digital competency frameworks constitute fundamental references for

guiding teacher training and performance in university contexts mediated by digital

technologies (Cabero-Almenara et al., 2020, p. 20). They also argue that the incorporation

of digital technologies into educational processes requires a critical and contextualized

pedagogical integration to promote educational innovation (Area and Adell, 2021, p. 85).

However, some authors warn that the incorporation of artificial intelligence into

educational processes also poses significant challenges. Torres-Cruz et al. point out that the

indiscriminate use of these technologies can generate risks related to technological

dependence, a decline in critical thinking, and various ethical dilemmas associated with the

use of automated tools in academic contexts (Torres-Cruz et al., 2023, p. 84). From this

perspective, it is necessary to critically and contextually analyze the impact of artificial

intelligence on university training processes.

Despite the growing number of studies on artificial intelligence in education, significant

gaps remain in the scientific literature, particularly in Latin American contexts and,

specifically, in the Ecuadorian higher education system. Most studies have been conducted

in international contexts or in disciplinary areas other than Economics, which limits the

understanding of the impact of these technologies in specific fields of knowledge.

In this regard, studies systematically examining the relationship between the use of artificial

intelligence-based tools, academic motivation, and student performance in economics,

finance, and statistics programs within the Ecuadorian context are still scarce. Therefore,

this study is justified by the need to provide empirical evidence that allows us to understand

the role of artificial intelligence in strengthening university learning, contributing to the

development of innovative pedagogical practices and the academic debate on the

incorporation of emerging technologies in higher education. In this context, the objective of

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this study is to analyze the influence of artificial intelligence on the learning process of

students in the Statistics, Economics, and Finance programs at the Central University of

Ecuador, with an emphasis on its impact on motivation and academic performance.

Regarding the article's structure, Section 2 presents the research methodology. Section 3

presents the results regarding the influence of AI on motivation and academic performance.

Section 4 presents the discussion based on the results obtained. Subsequently, in section 5,

the conclusions of the study and its main implications of AI in the student learning process

are presented.

2. Analysis and Results

This study was conducted using a quantitative approach, with a non-experimental, cross-

sectional design, considering that this type of methodology allows for the analysis of

relationships between variables without direct manipulation and at a single point in time.

This approach is suitable for examining educational phenomena through the measurement

and statistical analysis of observable variables. Recent research on the incorporation of

artificial intelligence in higher education has employed similar methodologies to analyze

the relationship between variables linked to university learning. In this regard, Espinoza-

Vidaurre et al. (2024) point out that “the incorporation of artificial intelligence tools in the

university setting allows for the analysis of various factors that influence the efficiency of

students' academic performance” (p. 402). Similarly, Grijalva-Maigua et al. (2025) maintain

that “artificial intelligence has become a key element for understanding the new dynamics

of learning in higher education” (p. 31), which has motivated the development of

quantitative research aimed at identifying associations between educational variables

without directly intervening in the learning process. In accordance with this approach, the

study focused on examining the relationship between the pedagogical integration of

artificial intelligence-based tools and variables linked to the learning process in university

students, particularly motivation and perceived academic performance.

Data was collected using the Study Processes Questionnaire (SPC), an instrument based on

the learning approaches theory proposed by John B. Biggs. According to Biggs, learning is

shaped by the relationship between students' motivations and the cognitive strategies they

use to process information and achieve academic goals (Biggs, 1987, p. 12). This theory

primarily distinguishes three approaches: surface, deep, and achievement. This perspective

is consistent with contemporary research that highlights motivation and learning

approaches as relevant variables in understanding educational processes mediated by

digital technologies. Along these lines, Bermúdez et al. (2025) state that “teaching strategies

supported by artificial intelligence allow for the creation of personalized learning

experiences that strengthen students’ motivation and academic engagement” (p. 6829).

Likewise, studies on virtual assistants and intelligent tools in higher education have

emphasized that motivation is a relevant factor in students’ interaction with educational

technologies. Jardón et al. (2024) point out that “virtual assistants based on artificial

intelligence can improve academic performance when students demonstrate a positive

attitude and motivation toward using these tools” (p. 14). The CPE, composed of 42 items

evaluated using a Likert-type scale, allowed for the measurement of these dimensions from

a theoretical and empirical perspective consistent with the study's objectives.

The population consisted of 243 students enrolled in the second and third semesters of the

Statistics, Economics, and Finance programs at the Central University of Ecuador. The

selection of these academic levels responded to the need to work with a relatively

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homogeneous group in terms of academic progress, reducing the variability associated with

differences in prior education. To ensure representativeness, a simple random sampling

combined with proportional stratified sampling was applied, obtaining a sample of 149

students (68 from the second semester and 81 from the third), which allowed a balanced

distribution according to the academic level.

Data collection was carried out using the Google Forms platform, facilitating data

systematization in a digital environment consistent with the study's objective. Statistical

processing was performed using IBM SPSS Statistics version 29.0, establishing a significance

level of 5% for hypothesis validation. The analysis included descriptive statistics

(frequencies, means, and standard deviations) and inferential tests such as the one-sample

Student's t-test, Pearson correlations, and non-parametric tests (Kendall's tau-b and

Spearman's rho), with the purpose of identifying statistically significant associations

between the analyzed variables. This type of statistical procedure has been used in research

examining the relationship between educational technologies and learning variables in

higher education. In this regard, Tamayo-Arellano et al. (2024) point out that "the

application of statistical analysis allows us to evaluate the relationship between the use of

artificial intelligence tools and various university learning processes" (p. 86). Similarly,

Suárez-Lima et al. (2025) highlight that “the analysis of educational data using statistical

techniques facilitates understanding how artificial intelligence can contribute to

personalized learning” (p. 16). Given the non-experimental, cross-sectional design adopted,

the study's results allow for the identification of relationships or associations between the

analyzed variables within the research context. Therefore, their interpretation is limited to

describing statistical patterns without establishing direct causal relationships.

Regarding ethical considerations, student participation was voluntary, and respect for the

principles of confidentiality and protection of personal information was guaranteed. Before

answering the questionnaire, participants were informed about the study's objectives, the

academic use of the data, and their right not to participate or to withdraw from the process

at any time. This procedure constituted informed consent for their participation. The

instrument was administered via a digital form on the Google Forms platform, which

requested basic identification information for participation control and sample verification

purposes. However, during the statistical analysis process, personal data were coded and

anonymized, eliminating any element that could identify the participants. In this way, the

information was processed only in aggregate form and used exclusively for academic and

scientific purposes, guaranteeing the confidentiality of the students and the responsible

handling of the collected data.

3. Analysis and Results

Through the analysis of the tables and figures shown below, the behavior of the study unit

will be observed in order to gain a better understanding of the characteristics identified in

the research. Table 1 shows that the majority of surveyed students come from the Finance

program (59.7%), followed by the Statistics program (24.2%) and the Economics program

(16.1%). This distribution was relevant because the application and perception of AI can

vary slightly among these disciplines within Economic Sciences. Furthermore, the sample

distribution for this academic unit is concentrated in the area related to the social sciences

(75.8%), while the applied statistics area represents 24.2% of the sample.

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Career

Frequency

Percentage

Cumulative

Percentage

Economics

16.1 %

Finance

59.7 %

75.8 %

Statistics

24.2 %

100.0 %

Total

149

100.0 %

Table 1. Distribution of students according to their field of study

According to Table 2, there is an equitable distribution between second- and third-semester

students, with percentages of 45.6% and 54.4%, respectively. This distribution by academic

level was intentional, allowing for control of variability in university experience and

familiarity with the content of Economics. By concentrating the sample in these

intermediate semesters, it was ensured that participants had a similar knowledge base,

which is crucial for more accurately evaluating the influence of AI on their learning process,

preventing differences in their level of academic progress from being a distorting factor.

Semester

Frequency

Percentage

Cumulative

Percentage

Second semester

45.6 %

Third semester

54.4 %

100.0 %

Total

149

100.0 %

Table 2. Distribution of students by semester

Table 3 shows the equitable gender distribution, with 51.0% women and 49.0% men in the

total sample. This distribution ensured that the study results were not biased by gender,

allowing us to infer the impact of AI on the learning process from a balanced perspective.

Furthermore, this distribution ensured the external validity of the findings, as the sample

reflects gender diversity, strengthening the generalizability of the conclusions.

Semester

Frequency

Men

Women

Percentage

Second semester

51.0 %

Third semester

49.0 %

Total

149

100.0 %

Table 3. Distribution of students by sex

Analysis of Table 4 reveals that the majority of surveyed students are between 18 and 21

years old, representing 73.2%. This concentration at early stages of university life is

consistent with the second- and third-semester student population, resulting in a sample

composed primarily of students who are actively engaged in their studies and have grown

up in an increasingly digital environment.

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Age range

Frequency

Percentage

Cumulative Percentage

Between 18 and 19

years old

23.5%

Between 20 and 21

years old

49.7%

73.2%

Between 22 and 23

years old

17.4%

90.6%

Between 24 and 25

years old

4.7%

95.3%

Over 25 years old

4.7%

100.0%

Total

149

100.0%

Table 4. Distribution of students according to age range

To analyze the variables associated with the learning process in the context of using

artificial intelligence-based tools, the following statistical hypotheses were proposed: H₀:

there are no statistically significant differences in the variables of motivation and academic

performance in the studied population; H₁: there are statistically significant differences in

the variables of motivation and academic performance in the studied population. In this

regard, the results of the Student's t-test for a single sample, presented in Table 5, indicate

that the evaluated variables show levels of statistical significance (p < .001). The Motivation

variable registered the highest mean (M = 3.439) and a t-value of 64.332, with p < .001,

which demonstrates a statistically significant difference with respect to the reference value

considered in the analysis. Similarly, the Academic Performance variable presented a mean

of M = 2.958 and a t-value of 45.753, with p < .001. These results allow us to identify

statistically significant differences in the analyzed variables within the studied population.

Consequently, the null hypothesis (H₀) is rejected, and the alternative hypothesis (H₁) is

accepted.

Motivation for learning is primarily distributed at intermediate levels, corresponding to

categories 3 (Medium) and 4 (High), with level 3 being the most frequent among the

surveyed students. Furthermore, the proportion of students at the lowest motivation levels

(levels 1 and 2) is small, indicating a limited presence of demotivation in the analyzed

population. However, the low representation at the highest motivation levels (levels 5 and

6) suggests that there is still room to strengthen this aspect within the learning process.

Overall, approximately 90% of the students are concentrated at motivation levels 3 and 4,

demonstrating a predominant trend toward medium and relatively high levels of academic

motivation in the analyzed context. These results allow us to describe a favorable pattern of

student motivation in the studied population within the considered educational context.

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Variable

(bilateral)

Media

IC 95%

Inferior

IC 95%

Superior

Career

40.211

148

< .001

2.081

1.980

2.180

CPE_P1

30.025

148

< .001

2.745

2.560

2.930

Motivation

64-.332

148

< .001

3.439

3.333

3.545

Performance

45.753

148

< .001

2.958

2.830

3.086

TICS_P1

56.869

148

< .001

1.054

1.020

1.090

Table 5. Distribution of students by age range. Source: Study Process Questionnaire (SPQ), question (Q1),

Information and Communication Technology (ICT), question 1 (Q1).

Pearson's correlation (Table 6) was used to demonstrate the linear relationship between

the technological and academic domains. The results showed no statistically significant

linear relationships between the evaluated factors (academic motivation, technological

access, and academic performance); all p-values were greater than .05 (the correlation

between subject choice based on personal satisfaction or market interest and academic

performance was r = .086, p = .299). This suggests that access to technological tools alone

does not directly modify the learning process. The alternative hypothesis is partially

accepted, indicating that the quality of technology use and its pedagogical integration with

AI are more significant than mere possession of the technology.

Variable

1. Choice of subjects based on personal satisfaction or

market interest

1.000

.001

.086

2. Has a computer at home

.001

1.000

.126

3. Academic performance

.086

.126

1.000

Sig. (bilateral)

—

.990

.299

—

.125

Table 6. Pearson correlation between motivation, technological access and academic performance in

Economics students

Note: Pearson correlation (two-tailed) relationship between variables. No statistically

significant correlations were found (p > .05). N = 149 for the variables.

The results of the one-sample t-test on the influence of AI on motivation (Table 7) showed

statistically significant differences with respect to the zero reference value (p < .001).

Specifically, "motivation for pedagogical use" had a mean of M = 3.47 and "Pedagogical

impact of AI" a mean of M = 3.32. These findings, along with the perception of the quality of

education received (where 74% of students reported a high or very high perception), allow

us to accept the alternative hypothesis (Hi). It is confirmed that AI positively influences the

pedagogical dimension, improving motivation, resource use, and the overall perception of

educational quality.

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Variable

p (bilateral)

Media

Pedagogical Impact of AI

24.38

< .001

3.32

Use of Pedagogical Tools

21.57

< .001

2.98

Motivation for Pedagogical Use

26.14

< .001

3.47

Improvement in Academic Performance

22.05

< .001

3.11

Variable

p (bilateral)

Media

Table 7. Influence of AI on motivation for pedagogical use: test results t

Note: One-sample t-test.

The results in Table 8 showed no statistically significant links between motivation for

subject selection and academic performance (τ = .072; p = .259), nor between the presence

of a computer at home and academic performance (τ = .096; p = .169). This suggests that

instrumental access to technology alone does not have a direct influence. However, a

previous multivariate study showed that motivation does explain a significant percentage

of academic performance (adjusted R² = 0.327; p < .001), indicating a more complex and

mediated impact: AI, by increasing motivation, may have an indirect impact on academic

performance.

Variable

1. Choice of subjects based on personal satisfaction or

market interest

1.000

–.007

.072

2. Has a computer at home

–.007

1.000

.096

3. Academic performance

.072

.096

1.000

Sig. (bilateral)

—

.924

.259

—

.169

Table 8. Kendall's Tau-b correlations on the instrumental influence of AI on motivation, technological access,

and academic performance

Note: Ordinal and non-parametric variables.

The Spearman's rho statistic shown in Table 9 determined that the correlation between

motivation in subject selection, the presence of a computer at home, and academic

performance was not statistically significant (Motivation/Performance ρ=.090, p=.272;

Computer/Performance ρ=.113, p=.170). This reinforces the idea that neither simple

motivation nor access to technology directly influences performance. However, the

multivariate analysis showed that motivation plays a crucial role in the impact of AI on

academic performance, confirming that AI significantly increases student engagement and

motivation, which in turn contributes to improved academic performance.

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Variable

1. Choice of subjects based on personal satisfaction or

market interest

1.000

–.008

.090

2. Has a computer at home

–.008

1.000

.113

3. Academic performance

.090

.113

1.000

Sig. (bilateral)

—

.924

.272

—

.170

Table 9. Spearman correlations on the influence of AI on the cognitive dimension

Note: Ordinal variables and ranks.

Artificial intelligence has a positive impact on the learning process of Economics students

at the Central University of Ecuador. The high t-values and p < .001, such as those for

motivation (t = 64.33; M = 3.44), academic performance (t = 45.75; M = 2.96), and

pedagogical perception of Artificial Intelligence (t = 24.38; M = 3.32), indicate that students

perceive the use of AI as effective in their studies. Furthermore, 95.3% of survey

participants showed medium or above-average levels in motivation and educational quality,

while 88.6% scored between levels 3 and 5 in self-assessed academic performance. Thus,

the impact of Artificial Intelligence is genuine, positive, and statistically significant

4. Discussion

The main finding of this study demonstrates that artificial intelligence has a positive impact

on the teaching and learning process of Economics students, particularly on academic

motivation and perception of academic performance. This result confirms the hypothesis

and shows that the pedagogical integration of AI-based tools is a relevant factor in

strengthening academic performance in higher education. These results are consistent with

recent research. In this regard, Suárez-Lima et al. point out that artificial intelligence allows

for the optimization of learning processes through adaptive and personalized pedagogical

strategies (Suárez-Lima et al., 2025, p. 14). Similarly, other authors highlight that the

development of digital competencies in university professors favors the implementation of

methodological strategies aligned with the demands of digital learning environments

(Cabero-Almenara et al., 2020, p. 28).

The interpretation of the results shows that artificial intelligence does not act solely as a

technological resource, but as a pedagogical mediator that directly impacts students'

motivation, autonomy, and active participation in their learning process. In this sense,

Galván-Fernández (2024) argues that “artificial intelligence should be understood from its

educable dimension, that is, as a tool capable of integrating into pedagogical processes and

contributing to the student's critical development” (p. 1). The significant relationship

identified between motivation and academic performance reinforces the importance of

motivational factors in the construction of learning. Tarira-Caice et al. (2018) point out that

“motivation is an essential element in the student learning process, as it directly influences

their interest in and commitment to academic activities” (p. 170). Similarly, Martín-Cruz et

al. (2009) highlight that “intrinsic motivation favors the transmission and construction of

knowledge by encouraging the active participation of individuals in the learning process”

(p. 193).

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In this context, the integration of artificial intelligence in university environments fosters

the development of more personalized and meaningful learning experiences. Ludeña-

Yaguana et al. (2025) demonstrate that “personalized pedagogy supported by artificial

intelligence contributes to strengthening motivation and improving students’ academic

performance” (p. 231). Likewise, Suárez-Lima et al. (2025) indicate that AI-mediated

personalized learning allows for adapting content to the individual needs of students,

increasing their academic engagement (Suárez-Lima et al., 2025, p. 16). Additionally, Jardón

et al. (2024) report that “virtual assistants based on artificial intelligence generate

improvements in academic performance and in the perception of the quality of university

learning” (p. 15).

However, several authors warn that the pedagogical impact of artificial intelligence depends

largely on the motivation and preparation of the teaching staff. Franco (2023) argues that

“the incorporation of artificial intelligence in education requires teachers prepared to

integrate these technologies from a pedagogical perspective and not solely a technological

one” (p. 2). Similarly, Galván-Fernández (2024) emphasizes that AI should be understood

as an integrating component of the educational process and not as a merely instrumental

resource (Galván-Fernández, 2024, p. 2). Ludeña-Yaguana et al. (2025) underscore that the

planned use of artificial intelligence-based technologies enhances both student motivation

and academic performance when there is conscious and contextualized didactic mediation

(Ludeña-Yaguana et al., 2025, p. 236).

Compared to recent literature, the findings of this study align with research indicating that

AI significantly contributes to student motivation and improved academic performance

when strategically integrated into the educational process. Franco (2023) states that

“contemporary education must move towards a critical understanding of artificial

intelligence as part of the digital educational ecosystem” (p. 1). In this sense, the results

expand upon these contributions by demonstrating that, while the perception of AI is

positive, structural limitations still persist that restrict its full utilization, particularly

regarding teacher training and curriculum adaptation.

The relevance of these findings lies in their contribution to the current debate on the role of

AI in higher education, especially in fields related to Economics, where automation and data

analysis are increasingly in demand. In this regard, Tarira-Caice et al. emphasize that

academic motivation is a determining factor for effective learning (Tarira-Caice et al., 2018,

p. 172). Similarly, it is highlighted that the interaction between intrinsic and extrinsic

motivation influences knowledge construction (Martín Cruz et al., 2009, p. 195). In this

context, artificial intelligence can be understood as a resource that enhances these

motivational processes by fostering more dynamic and interactive learning experiences.

Likewise, it is argued that the critical integration of emerging technologies allows for the

articulation of technological innovation with human development in the educational field

(Galván-Fernández, 2024, p. 2). In accordance with the above, personalized pedagogy

mediated by artificial intelligence promotes motivation and academic performance in

university students (Ludeña-Yaguana et al., 2025, p. 238). Similarly, it is noted that virtual

assistants can improve the perception of the learning process when they are appropriately

integrated into the pedagogical design (Jardón et al., 2024, p. 18).

However, it is necessary to acknowledge certain limitations of the study. First, the research

was conducted at a single higher education institution, which could limit the generalizability

of the results to other academic contexts. Furthermore, the use of instruments based on

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student perceptions may have introduced subjective biases. These limitations could be

addressed in future research through longitudinal designs, larger samples, and the

incorporation of mixed methods approaches that allow for contrasting perceptions with

objective indicators of academic performance. In this regard, the importance of expanding

empirical research on artificial intelligence in education to understand its effects in different

academic contexts is highlighted (Ludeña-Yaguana et al., 2025, p. 240). It is also noted that

the development of new research will strengthen the pedagogical integration of AI in

educational systems (Franco, 2023, p. 3).

From both a practical and theoretical perspective, the results of this study suggest the need

to strengthen institutional policies focused on teacher training in digital competencies and

curriculum updates in line with advances in AI. In this regard, Galván-Fernández (2024)

emphasizes that the incorporation of emerging technologies in education must respond to

a comprehensive pedagogical approach (Galván-Fernández, 2024, p. 2). Similarly, Jardón et

al. (2024) highlight that the effectiveness of intelligent systems in education depends on

their planned integration into instructional design and teaching strategies (Jardón et al.,

2024, p. 20).

In summary, this study demonstrates that artificial intelligence constitutes a strategic

resource for improving the motivation and academic performance of Economics students,

provided that its implementation is accompanied by adequate pedagogical and curricular

training. These results coincide with recent research. In this sense, Ludeña-Yaguana et al.

(2025) demonstrate the potential of AI to strengthen educational processes when it is

critically and contextually integrated into teaching practice (Ludeña-Yaguana et al., 2025, p.

241).

5. Conclusions

The results of this study identify a statistically significant relationship between the use of

artificial intelligence-based tools and variables associated with the learning process of

students majoring in Statistics, Economics, and Finance at the Central University of Ecuador.

Specifically, a positive trend was observed in academic motivation levels and in the

perception of student performance within the analyzed context. These findings suggest that

the incorporation of artificial intelligence tools can be a valuable resource within university

educational environments, facilitating more flexible learning dynamics that align with

current technological demands.

From an educational perspective, the results highlight the importance of considering

artificial intelligence as a complementary element in training processes, especially in areas

related to data analysis, statistics, and economics, where digital tools can support the

development of analytical and problem-solving skills. However, due to the non-

experimental, cross-sectional design of the study, the findings should be interpreted in

terms of observed associations within the investigated context, without establishing direct

causal relationships.

Based on the above, it is recommended to promote pedagogical strategies that integrate the

use of artificial intelligence tools in the classroom in a planned manner, aimed at

strengthening student motivation and fostering more personalized learning experiences.

Likewise, it is pertinent to promote teacher training programs focused on the pedagogical

use of artificial intelligence-based technologies, so that teachers can critically and effectively

incorporate these tools into their educational practices.

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Similarly, it is suggested that the curriculum in Economics-related fields be updated to

incorporate digital competencies and the use of artificial intelligence tools applied to

information analysis and decision-making. Finally, it is recommended that future research

with longitudinal or experimental designs be developed to further analyze the relationship

between artificial intelligence and university learning, expanding the sample size and

considering other educational variables that may influence the learning process.

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Authors

SANTIAGO VINUEZA-VINUEZA earned his PhD in Educational Research from César Vallejo

University in Peru in 2025, a Master's degree in Communication Networks from the Faculty

of Engineering at the Pontifical Catholic University of Ecuador in 2016, a Master's degree in

Educational Information Systems from Israel Technological University in 2009, a Bachelor's

degree in Education with a specialization in Computer Science from the Faculty of

Philosophy, Letters, and Educational Sciences at the Central University of Ecuador in 2002,

and a Bachelor's degree in Computer Engineering from the Autonomous University of Quito

in 2002. He is currently a full-time professor in the Faculty of Economic Sciences at the

University of Ecuador. His main research focuses on education and Information and

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Communication Technologies. He is the author of book chapters and articles published in

high-impact journals (Emerging Sources Citation Index, Scopus, Latindex, Redalcy, SciELO).

ALEJANDRA FONSECA-FACTOS earned her Master's degree in Education with a

specialization in Innovation and Educational Leadership from Indoamerica University

(Ecuador). She also holds a degree in Electronics and Telecommunications Engineering

from the University of the Armed Forces-ESPE.

Currently, she teaches both the regular and supplementary technical high school programs

at the Uyumbicho Educational Unit in Mejía Canton, Pichincha Province. She has served as

a peer reviewer for the journal Conectividad-Rumiñahui University Institute. She

collaborates with the Ministry of Education, Sports, and Culture, providing technical and

pedagogical support for the implementation of educational robotics projects in schools

within the education system. Her main research interests include STEAM approaches,

educational robotics, educational innovation, the didactics of exact and natural sciences

(physics), biomedicine, and technologies applied to education. She is the author of several

articles published in conferences and high-impact journals (IEEE Xplore, Scielo, Latindex,

DOAJ).).

Declaration of authorship-CRediT

SANTIAGO VINUEZA-VINUEZA: State of the art, related concepts, methodology, validation,

data analysis, writing – first draft.

ALEJANDRA FONSECA-FACTOS: State of the art, related concepts, data analysis,

organization and integration of collected data, conclusions, final writing and editing.

Statement on the use of artificial intelligence

The authors declare that they did not use Artificial Intelligence (AI) tools for any part of the

manuscript. All material was reviewed and validated by the authors, who are responsible

for its accuracy and rigor.