Revista multidisciplinaria
investigación Contemporánea ISSN-e: 2960-8015Vol. 4 -No. 4
DOI: https://doi.org/10.58995/redlic.rmic.v4.n4.a226 22 - 34
Pomavilla et al. El impacto de Amazon Alexa en la comprensión auditiva y expresión oral
10 - 2026
Artículo original. Vol. 4 - Nro. 4, pp. 22 - 34. Diciembre 2026. E-ISSN: 2960-8015
The Impact of Amazon Alexa on Listening and Speaking Skills in
EFL Higher Education Students
El impacto de Amazon Alexa en la comprensión auditiva y expresión oral
en estudiantes de inglés de educación superior
José Luis Pomavilla Patiño 1, Mauro David Villacrés Benalcázar 2*
Hilda Gabriela Portilla Torres 3, Kevin Timothy Rams 4
1 Universidad Yachay Tech; jpomavilla@yachaytech.edu.ec. Ibarra, Ecuador.
2 Universidad Yachay Tech; mvillacres@yachaytech.edu.ec. Ibarra, Ecuador.
3 Universidad Yachay Tech; gportilla@yachaytech.edu.ec. Ibarra, Ecuador.
4 Universidad Yachay Tech; krams@yachaytech.edu.ec. Ibarra, Ecuador.
¿Como citar?
Pomavilla Patiño, J. L. ., Villacrés Benalcázar, M. D. ., & Portilla Torres, H. G. . (2026). El
impacto de Amazon Alexa en la comprensión auditiva y expresión oral en estudiantes de
inglés de educación superior. Revista Multidisciplinaria Investigación Contemporánea, 4(4),22-
34. https://doi.org/10.58995/redlic.rmic.v4.n4.a226
Recibido: 18-05-2026
Aceptado: 23-06-2026
Publicado: 01-10-2026
Nota del editor
REDLIC se mantiene neutral con respecto a reclamos
jurisdiccionales en mensajes publicados y aliaciones
institucionales.
Editorial
Red Editorial Latinoamericana de Investigación
Contemporánea (REDLIC)
www.editorialredlic.com
Fuentes de nanciamiento
La investigación fue realizada con recursos propios
Conictos de interés
No presentan conicto de intereses.
Este es un artículo de acceso abierto, bajo licencia
Creative Commons (CC BY 4.0) de Atribución 4.0
Internacional
https://doi.org/10.58995/redlic.rmic.v4.n4.a226
Revista multidisciplinaria
investigación Contemporánea ISSN-e: 2960-8015Vol. 4 -No. 4
DOI: https://doi.org/10.58995/redlic.rmic.v4.n4.a226 23 - 34
Pomavilla et al. El impacto de Amazon Alexa en la comprensión auditiva y expresión oral
10 - 2026
Resumen
Este estudio aborda el uso de herramientas de inteligencia articial para el desarrollo de las
habilidades de comprensión auditiva y expresión oral en estudiantes universitarios de inglés
como lengua extranjera. El objetivo fue determinar la efectividad del uso de Amazon Alexa para
mejorar la escucha, el habla y la motivación en estudiantes de nivel A1–A2. Se utilizó un diseño
cuasi-experimental cuantitativo con pretest y postest, con 50 estudiantes. Los datos se recolectaron
mediante pruebas de desempeño y un cuestionario de motivación, y se analizaron con el método
matemático-estadístico. Los resultados individuales de los participantes evidenciaron mejoras
signicativas en comprensión auditiva en estudiantes con bajo rendimiento en los pre-tests,
mientras que la expresión oral no presentó cambios signicativos. Se concluye que la herramienta
es ecaz para habilidades receptivas, pero su impacto es limitado para las productivas.
Palabras clave: Alexa; escuchar; hablar; educación superior; inteligencia articial.
Abstract
This study addresses the use of articial intelligence tools for developing listening and speaking
skills in university students learning English as a foreign language. The objective was to determine
the eectiveness of using Amazon Alexa to improve listening, speaking, and motivation in A1–
A2 level students. A quantitative quasi-experimental design with pre-test and post-test was
employed, involving 50 students. Data was collected through tests and a motivation questionnaire
and analyzed using the mathematical-statistical method. The individual results of the participants
showed signicant improvements in listening comprehension among students with low pre-test
performance, while speaking skills did not show signicant changes. It is concluded that the tool
is eective for receptive skills, but its impact on productive skills is limited.
Keywords: Alexa; listening; Speaking; Higher education, Articial Intelligence.
1. Introducción
The English language is continually being
presented with new obstacles and new
paths to take in the digital age and has
been established as an integral part of the
global academic, professional, and cultural
environment. It’s not uncommon for
students to have diculties with learning
English as a foreign language, particularly
when it comes to listening and speaking
skills; many students will demonstrate
insufficient progress in these areas
(Crompton et al., 2024). To address these
challenges, articial intelligence (AI) has
recently been introduced into the teaching
of the English language as an innovative
method of supporting language teaching
and learning. For example, in many
settings—especially China—AI is regarded
as a key resource for improving English
language learning outcomes (Hsu et al.,
2021). Current research also indicates that
AI provides signicant resources for the
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development of students’ English language
skills and promotes eective teaching
practices in English language pedagogy
(Peña-Acuña & Corga Fernandes Durão,
2024).
Several studies have recently shown
how helpful AI can be for teaching and
learning English. For example, a me-
ta-analysis reported that using AI-based
tools in academic settings has shown a
signicant improvement in oral expres-
sion for all learners (Jantakoon et al.,
2025). Other systematic reviews have
found that online learning through AI-ba-
sed systems improves student motivation
and pronunciation and reduces the anxie-
ty of learning a second language (Vančo-
vá, 2023). In addition, voice recognition
systems, which provide real-time and im-
mediate feedback about how well one is
pronouncing something, have also shown
to positively aect speaking ability (Chen
et al., 2022). Evidence from experimental
studies of younger students suggests that
virtual assistants have improved both lis-
tening and speaking skills and may also
promote participation and motivation
during the learning process (Alqarni &
Alhramelah, 2025). These studies provide
evidence that AI improves teaching and
learning by creating interactive and su-
pportive environments for all learners.
Virtual assistants have become impor-
tant tools in helping teach and learn lan-
guages; some of these assistants include
Amazon Alexa and Google Assistant as
well as Siri. A study showed that students
who practiced speaking and pronuncia-
tion with Alexa (Amazon) during their
university classes demonstrated substan-
tial improvements in their spoken lan-
guage when compared to a control group
(Qiao & Zhao, 2023). Another study of the
qualitative nature indicated that enga-
ging with Alexa increased students’ mo-
tivation, their condence levels and their
ability to learn the English language, thus
providing greater participation in their
English language learning (Luria, 2024).
A third study utilized a case study de-
sign and reported that students had very
positive perceptions of Amazon Alexa
and believed it was a very valuable and
enjoyable tool for practicing the English
language (Dizon et al., 2022). Finally, re-
search looking at the voice characteristics
of Amazon Alexa’s voice found that in-
creased expressiveness and enthusiasm
in Amazon Alexa’s tone created less cog-
nitive load on the individual and produ-
ced strong positive emotional responses;
however, the amount to which these voi-
ce characteristics may have increased the
student’s motivation was not signicantly
dierent (Liew et al., 2023).
In Ecuador, the use of AI is also
growing in English language education.
There are studies that show that the use
of AI has resulted in signicant improve-
ments in some quantitative measures con-
nected to education (Atencio & Criollo,
2025). A quasi-experimental study com-
paring high school students in Ecuador
clearly indicates that students who used
AI platforms scored better in language
learning than those who received tradi-
tional instruction (Acosta & Bedon, 2025).
This may be attributed to improvements
in oral communication skills and access to
immediate feedback, as well as new types
of learning activities. However, challenges
remain for both limited access to techno-
logy and insucient teacher training for
successful implementation (Gavilanes &
Naranjo, 2024). Regardless of these limita-
tions, the literature in the region supports
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that if appropriate conditions exist, AI can
improve the quality and accessibility of
English language education (Fajardo-Ra-
mos et al., 2025).
Within this context, although both glo-
bal and local research support the eecti-
veness of AI tools, empirical research on
the use of Amazon Alexa in higher edu-
cation in Ecuador remains limited. The-
refore, this study aims to determine the
eectiveness of integrating articial inte-
lligence through Amazon Alexa to streng-
then listening and speaking skills as well
as students’ motivation in English leveling
courses at Yachay Tech University.
Materials and methods
The research was conducted using a mixed
approach with a quasi-experimental
pre-test and post-test design to obtain
quantitative data for the analysis, as
well as qualitative data from a survey
designed to receive feedback and to collect
students’ perceptions on the use of Alexa.
This design made it possible to compare
the eect of using Amazon Alexa in the
learning process on the development
of listening and speaking skills, as well
as motivation, before and after the
implementation of Alexa. The research
project was reviewed and approved by
the research department of Yachay Tech
University.
The study population consisted of
50 university students enrolled in the
English leveling course at Yachay Tech
University, which represents 30% of the
total students enrolled in this level. The
participants signed a letter of consent,
and they were also informed about the
activities and the rubrics to be used in
the study. All students expressed their
willingness to participate, and students
who withdrew from the course during the
intervention period were excluded from
the analysis. These criteria ensured that
the nal sample consisted only of students
who fully met the study conditions.
The researchers designed activities for
the use of Alexa in the classroom. These
activities were created based on the content
and syllabus for the leveling course, which
corresponds to levels A1 and A2 according
to the Common European Framework.
The pre-test for listening included an A1
audio of two people talking about their
personal interests and hobbies and a test
with 10 questions about the audio with 10
maximum points. The post-test included
an A2 audio about a person describing
past activities on a holiday; the test had
10 questions with a maximum score of 10
points. To assess speaking and observe
students’ interaction with the device in
class, the researchers created a rubric
to assess the criteria of pronunciation,
uency, grammar, and vocabulary (to
measure the accuracy in formulating
questions) and the type of interaction
of students with the device during the
activities. This instrument had a score
ranging from 0 to 5 for each criterion with
a maximum of 25 points. The total score
was then converted to a grade out of 10
points. This instrument was also used as
pedagogical support to monitor students’
participation and progress throughout
the interaction with the device. All the
instruments were reviewed and validated
by other professors of this level to ensure
the questions and the rubric aligned with
the level requirements.
The period of intervention was from
April 7th to August 4th, 2025. During
this time, the researchers carried out
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the listening and speaking activities
previously designed, whose aim was to
make students use the device. Among the
activities, students had to ask questions
about vocabulary or make requests (to play
music, to tell a short story, or to translate
words from Spanish to English); all the
activities were conducted in class. A post-
test for the listening and speaking test was
applied to nd any variation in means and
scores after the intervention. In this way,
variations in students’ communicative
performance could be observed, and
descriptive comparisons between the
results could be established. At the end
of the period of implementation students
completed a perception survey on the use
of Alexa during the English classes.
The survey had ve sections, and all
the questions were in Spanish to ensure
that all students understand what is being
asked. The rst section was about students’
general experience with Alexa, the second
section was about students’ perceptions
of their speaking with Alexa, and the
third section was about listening. Each of
these sections included 5 questions with a
Likert scale. Additionally, there were open
questions about the diculties t hat t he
participants faced when using the device,
the willingness to use the device in future
classes, and some recommendations for
future implementations.
The research was conducted with strict
adherence to ethical considerations for
research involving human participants.
Student participation was voluntary,
ensuring participant anonymity and
condentiality of the collected data.
Likewise, eorts were made to ensure
that the implementation of Alexa did
not negatively aect students’ regular
academic performance.
Results
Based on the pretest and posttest results
for speaking and listening, missing values
were imputed. The analysis was conducted
using R statistical software, and the selec-
ted imputation method was Multivariate
Imputation by Chained Equations (MICE)
with Predictive Mean Matching (PMM).
This approach ensures that imputed va-
lues remain within the observed data ran-
ge (0–10). MICE is considered appropriate
for small sample sizes, such as the present
cohort of 50 students (Van Buuren & Van
Buuren, 2012), and is preferred over k-nea-
rest neighbors (KNN), which may produ-
ce biased signicance estimates due to its
deterministic nature (Little & Rubin, 2019).
Additionally, MICE preserves relations-
hips between variables in complex data-
sets, although it is computationally less
ecient (Mohammed et al., 2021).
The listening results for the comple-
te dataset after imputation indicated a
decrease from M = 7.1 to M = 6.4 (∆M =
−0.70), which was not statistically signi-
cant (p = .15).
The speaking results for the complete
dataset indicated a decrease from M = 8.1
to M = 7.6 (∆M = −0.50), which was not
statistically signicant (p = .06).
A visual analysis using parallel coordi-
nates plots (Figure 1) was conducted to ex-
plore the unexpected decline in mean sco-
res. The results indicate that the decrease
in mean values was primarily driven by
students with high pretest scores. This
pattern suggests the presence of a ceiling
eect, where participants with initial high
performance had limited opportunity
for further improvement. Consequently,
these students tended to obtain lower or
similar scores in the posttest, which may
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have aected the overall mean and pro-
duced misleading aggregate results. In Fi-
gure 1, bold lines represent mean scores,
whereas lighter lines represent individual
student trajectories.
Figure 1. a) Listening scores:
Parallel coordinates plots showing individual changes in listening and speaking scores from the
pretest to the posttest.
Figure 1. b) Speaking scores:
Parallel coordinates plots showing individual changes in listening and speaking scores from the
pretest to the posttest.
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To further examine the results, a one-
way ANOVA was conducted to compa-
re performance across groups dened
by initial prociency levels. Participants
were categorized based on their pretest
scores to account for the potential inuen-
ce of ceiling eects observed in the overall
results.
The analysis revealed dierences in
listening performance across prociency
groups. As shown in Table 1, no statisti-
cally signicant change was observed for
the full cohort (p = .152). However, when
participants were grouped by initial sco-
res, the subgroup with pretest scores ≤6
demonstrated a statistically signicant
improvement (M gain = 1.09, p = .01),
indicating that gains were concentrated
among lower-performing students.
These results indicate that the obser-
ved decline in overall mean scores was in-
uenced by higher-performing students,
whereas meaningful improvements were
detected among learners with lower ini-
tial prociency. This pattern is consistent
with a ceiling eect, in which participants
with initially high performance have limi-
ted opportunity for further improvement
(Wang et al., 2008).
Table 1. Targeted gains in listening pro-
ciency by initial prociency group.
Analysis Group N
Size
Mean
Gain P_Value
1Full
Cohort 50 -0.47 0.15
2Pre-
test ≤7 26 0.61 0.11
3Pre-
test ≤6 21 1.09 0.01
As shown in Table 1, no statistically
signicant dierences were observed in
the full cohort (p = .15). However, the
subgroup with pretest scores ≤6 demons-
trated statistically signicant gains (p =
.01), indicating that improvements were
concentrated among lower-performing
students.
Using ANOVA, the speaking was not
statistically improved across any of the
groups that were seen in the listening part
(Table 2).
Table 2. ANOVA results for speaking
gains across prociency groups.
Analysis Group N
Size
Mean
Gain P_Value
1 Full
Cohort
50 0.02 0.85
As shown in Table 2, no statistically
signicant dierences were observed in
speaking performance (p = .850), indica-
ting that the intervention did not result in
measurable improvements in this skill.
In gure 2, the perceptions of students
about the use of Alexa in the English clas-
ses are presented. The distribution of res-
ponses across survey items is presented as
follows.
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Figure 2
Students’ perceptions of the use of Amazon Alexa in English classes (percentage of responses).
Discussion
The ndings of this study reveal a
signicant divergence between the
development of receptive (listening) and
productive (speaking) skills when Alexa
from Amazon is used as a support tool
in EFL teaching. While the intervention
was highly eective for lower-prociency
learners in listening, these improvements
were not observed in speaking. These
results partially support previous studies
on the use of voice assistants and AI in
English teaching.
The signicant improvement in
listening among students with low
grades suggests that Amazon Alexa
worked eectively as a “one-to-many”
A post-intervention perception
survey (N = 56) indicated generally
positive student attitudes toward the
use of Amazon Alexa in the classroom.
Most participants reported enjoying the
use of the device and perceived that it
increased engagement and motivation
during English lessons. Participants also
reported perceived improvements in
listening comprehension, particularly
in recognizing vocabulary and
understanding spoken English. In
contrast, responses related to speaking
development were more varied, with
fewer students reporting improvement
in pronunciation and condence. Most
participants expressed willingness to
continue using Alexa in future classes.
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Pomavilla et al. El impacto de Amazon Alexa en la comprensión auditiva y expresión oral
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broadcasting tool, which is consistent
with the ndings of Hsu et al. (2021), who
found that Alexa can help develop the
listening skill through exposure to spoken
input. Therefore, Alexa can be a helpful
tool in a large classroom setting; all
students can simultaneously receive high-
quality linguistic input when the device
speaks. The results also align with the
work of Alqarni and Alhramelah (2025),
who reported improvements among
English students interacting with voice
assistants. The present study extends to
the Ecuadorian context, where similar
studies in higher education are still scarce.
The positive impact on listening
can be explained by the exposure of
multiple learners at the same time to the
spoken English. This is also consistent
with research indicating that AI-based
tools can enhance language learning
through interactive and accessible input
environments (Peña-Acuña & Corga
Fernandes Durão, 2024; Vančová, 2023).
In contrast, the absence of a signicant
improvement in speaking diers from
previous studies that reported a positive
impact on this skill. For instance, Qiao
and Zhao (2023) found that the use
of AI systems for language learning
helped university students develop their
speaking skills. Jantakoon et al. (2025)
also reported positive eects on speaking
and listening. One possible explanation
of this discrepancy is the implementation
condition of the present study, which used
a single Alexa device for a large group
of students, which might have limited
the opportunities for oral interaction;
consequently, the participants of this
study might not have received enough
feedback, and the number of speaking
activities planned was not enough to have
a measurable positive impact.
The perception survey also supports
the motivational benets associated
with AI-based assistants reported by
Luria (2024) and Dizon et al. (2022).
Most students indicated that they
enjoyed using Alexa during the English
classes, and they consider the device
to help the teacher to make the class
more engaging and interesting. A large
majority of students reported feeling
more motivated to participate when it
was time to use Alexa in the class. Most
participants agreed that the device helped
them improve their listening in terms
of recognizing vocabulary and some
grammar structures. These perceptions
align with the quantitative ndings of the
present study, which revealed some gains
among students who obtained low grades
in the listening pre-test.
Most of the participants expressed
their willingness to continue using Alexa
in future English classes, which aligns
with the ndings of Qiao and Zhao
(2023), who suggest that the device serves
not only as an instructional tool but also
as a motivational resource. However,
despite these positive perceptions, the
quantitative data indicate that while
students may feel more condent and
motivated when interacting with Alexa,
to have measurable improvements in this
skill, it is necessary to plan and carry
out more speaking activities using the
device. Future studies should consider the
implementation of multiple devices and
the increase in the number of speaking
activities where students can use the
device and receive feedback to improve
their oral production.
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Conclusions
This study evaluated the eectiveness
of using Amazon Alexa to enhance A1
and A2 university learners’ listening and
speaking. Evidence suggests that results
from the use of Amazon Alexa as a tool to
improve students’ listening and speaking
abilities vary according to their skill or
level of prociency and should consider
the relevant statistical and contextual
considerations.
The data analysis of the sample does
not show statistically signicant gains
in speaking skills. As reported, there
are slightly lower mean posttest scores
than pretest scores for the listening skill;
however, additional analysis shows that
these reductions can be largely attributed
to the existence of ceiling eects for
participants who did better on the
pretest and/or had very limited room for
improvement from their original score.
Therefore, the importance of considering
factors beyond overall averages is critical
when determining the eectiveness of
instructional interventions.
One-way ANOVA tests and Tukey HSD
analysis demonstrated data patterns that
were undetectable from initial analysis.
These patterns showed signicant
improvement in levels of listening skill for
lower-level learners, especially those who
had scores of 6 or below on the pretest.
Therefore, an eective way of using Alexa
to develop the receptive skill of listening
for beginning learners can occur when
there is consistent auditory exposure.
In contrast, there were no signicant
improvements in speaking across all
prociency levels. This would suggest
limitations related to intervention design
versus the actual technology itself.
Typically, speaking is an active skill that
requires individual practice. Therefore,
with one device in use, the students
involved had limited active engagement
and less total time speaking to practice
their speaking ability.
The overall outcome of this research
supports the fact that Amazon Alexa acts
as an eective teaching device to help
lower-level learners improve their ability
to understand spoken English. However,
due to limited opportunities for interaction
and the use of devices, the eectiveness of
Alexa in developing students’ speaking
skills is very limited.
It would be benecial for future
implementations to add more devices,
revise classroom task structures so that
students are able to interact with one
another as much as possible, and develop
more activities to support spoken language
production. This research also emphasizes
what appropriate statistical techniques to
use for evaluating technological devices
and tools used to support language
learning in the classroom, such as
reporting ceiling eects.
Contribución de los autores
J. P.: Diseño del proyecto de investigación,
recolección de datos, conclusiones.
M.V: Recolección de datos
H.P.: Recolección de datos
K.R.: análisis de resultados, discusión, re-
visión nal.
Aprobación del comité de
ética y consentimiento para
participar en el estudio
El proyecto de investigación cuenta con la
revisión y la aprobación del departamen-
to de investigación y del comité de ética
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Pomavilla et al. El impacto de Amazon Alexa en la comprensión auditiva y expresión oral
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de la Universidad de Investigación de Tec-
nología Experimental Yachay Tech bajo el
código DPI 24-10.
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Copyright (c) 2026. José Luis Pomavilla Patiño, Mauro David Villacrés Benalcázar ,
Hilda Gabriela Portilla Torres, Kevin Timothy Rams.
Este es un artículo de acceso abierto, bajo licencia Creative Commons
(CC BY 4.0) de Atribución 4.0 Internacional
