REVISTA KRONOS
INSTITUTO ACADÉMICO DE IDIOMAS REVISTA KRONOS
UNIVERSIDAD CENTRAL DEL ECUADOR 6(2), AGOSTO 2025 - ENERO 2026, PP. 84-105

pISSN 12631-2840
eISSN 2631-2859

kronos.idiomas@uce.edu.ec

DOI: https://doi.org/10.29166/kronos.v6i2.8067
CC BY-NC 4.0 —Licencia Creative Commons Reconocimiento-NoComercial 4.0 Internacional
© 2026 Universidad Central del Ecuador

Carlos Díaz Ortiz | Centro de Educación Continua CEC - EPN-Ecuador
Oswaldo González Díaz | Universidad de las Fuerzas Armadas ESPE, Universidad Central del Ecuador-Ecuador
Janneth Chumaña Suquillo | Universidad Central del Ecuador-Ecuador
Nelly Espinoza Molina | Universidad Central del Ecuador-Ecuador

abstract Language learning teaching has been impacted by Artificial Intelligence (AI), which offers tools that support the
systematic advancement of communicative skills. This research examines the effect of the AI chatbot interaction for en-
hancing the learners' strategic competence in B1 English as a Foreign Language (EFL) learners at Colegio Bilingüe Marie
Clarac. Based on communicative language teaching and theory of strategic competence, the research design is characterized
as mixed methods, comprising pre- and post-test assessments, surveys and interviews to explore chatbot-mediated lan-
guage learning. The study investigates how planning, monitoring and regulation strategies for effective communication are
encouraged by chatbot interactions. It is found that AI chatbots help improve learners' structure of discourse, self-monitor
language use, and exploit repair strategies that are conducive to increased conversational fluency and autonomy. Results
from the statistical analyses elucidate significant increases in indicators of strategic competence following the intervention,
echoing strong evidence of the utility of AI feedback and adaptive scaffolding. This study contributes to the existing knowl-
edge base on AI assisted language learning demonstrating how chatbots can be used as apt learning tools in terms of being
interactive, responsive, and as a personalised tool of learning. Finally, this research concludes with the recommendation on
including AI chatbots into EFL curricula to complement the learners’ competence gaps in the use of strategies and promote
communicative proficiency in real life settings. Longitudinal impacts and the scalability of chatbot mediated learning across
different linguistic and educational contexts are a field for future research.

keywords AI Chatbots, Strategic Competence, EFL Learning, Language Acquisition, Communicative Strategies

fecha de recepción 05/04/2025 fecha de aprobación 14/11/2025

Interacción con Chatbot de IA para mejorar la competencia estratégica
en estudiantes de inglés como lengua extranjera de nivel B1

resumen La enseñanza de idiomas se ha visto afectada por la inteligencia artificial (IA), que ofrece herramientas que
apoyan el avance sistemático de las habilidades comunicativas. Esta investigación examina el efecto de la interacción con
el chatbot de IA para mejorar la competencia estratégica de los estudiantes de inglés como lengua extranjera (EFL) de
nivel B1 en el Colegio Bilingüe Marie Clarac. Basado en la enseñanza comunicativa de idiomas y la teoría de la com-
petencia estratégica, el diseño de la investigación se caracteriza por ser un método mixto, que comprende evaluaciones
previas y posteriores a la prueba, encuestas y entrevistas para explorar el aprendizaje de idiomas mediado por chatbot.
El estudio investiga cómo las interacciones con el chatbot fomentan las estrategias de planificación, supervisión y regula-
ción para una comunicación eficaz. Se ha descubierto que los chatbots con IA ayudan a mejorar la estructura del discurso
de los alumnos, a supervisar el uso del lenguaje y a aprovechar las estrategias de reparación que conducen a una mayor
fluidez y autonomía en la conversación. Los resultados de los análisis estadísticos ponen de manifiesto un aumento sig-
nificativo de los indicadores de competencia estratégica tras la intervención, lo que se hace eco de las sólidas pruebas de
la utilidad de la retroalimentación de la IA y el andamiaje adaptativo. Este estudio contribuye a la base de conocimientos
existente sobre el aprendizaje de idiomas asistido por IA, demostrando cómo los chatbots pueden utilizarse como herra-
mientas de aprendizaje adecuadas en términos de interactividad, capacidad de respuesta y personalización. Por último,
esta investigación concluye con la recomendación de incluir chatbots con IA en los planes de estudio de inglés como
lengua extranjera para complementar las deficiencias de los alumnos en el uso de estrategias y promover la competencia
comunicativa en situaciones de la vida real. Los impactos longitudinales y la escalabilidad del aprendizaje mediado por
chatbots en diferentes contextos lingüísticos y educativos son un campo para futuras investigaciones.

palabras clave Chatbots con IA, competencia estratégica, aprendizaje del inglés como lengua extranjera, adquisición
del lenguaje, estrategias comunicativas.

AI Chatbot Interaction on Enhancing Strategic Competence
in B1 EFL students

Díaz C. et al

85REVISTA KRONOS 6(2), agosto 2025 - enero 2026 | pISSN 12631-2840 | eISSN 2631-2859

INTRODUCTION

General background
The integration of ai in education has the potential to revolutionize language learning, par-
ticularly in enhancing strategic competence among B1 English as a Foreign Language (efl)
learners. Strategic competence is defined as the ability to use language learning strategies
effectively and is crucial for language acquisition and communication success (Oxford,
2017). Despite the importance of this skill, many efl learners, even those from privileged
backgrounds, lack well-developed strategic competence (Gilakjani, 2017). ai chatbots, such
as those studied by Karunarathne et al. (2024), Majorana et al. (2022), and Michalon &
Camacho-Zuñiga (2023), have shown promise in providing personalized, real-time sup-
port that enhances learning experiences and outcomes. These tools offer tailored assis-
tance, promote self-directed learning, and continuous engagement, which foster strategic
competence. Additionally, ai chatbots can address the diverse needs of students and pro-
vide support outside traditional classroom hours, making them a valuable resource in ed-
ucational settings. The positive feedback from students and educators alike underscores
the potential of ai chatbots in facilitating the acquisition of strategic competence among
B1 learners, suggesting a promising direction for future research and implementation. Ef-
fective communication hinges on proficient speaking skills, which is one of the toughest
challenges for efl learners. Proficiency in speaking enables learners to communicate ideas
effectively, participate in conversations, and engage in academic as well as social interac-
tions. Nonetheless, many efl students find it difficult to speak because of vocabulary con-
straints, issues with pronunciation, and fear of making errors. These speaking difficulties
are closely linked to limited strategic competence, as learners often lack the strategies re-
quired to manage breakdowns, maintain fluency, and compensate for linguistic gaps. As
a result, there is a great need to improve speaking skills so that learners can use English
in real-life contexts with much ease. This study aims to address this gap in the speaking
skill by investigating the impact of ai chatbot interaction on the strategic competence of
B1 learners at Colegio Bilingüe Marie Clarac in Tumbaco, Ecuador.
Problem to be investigated and the elements that justify the problem
This study assumes that meaningful interaction in the form of multiple conversations with
ai chatbots will boost strategic competence of B1-level language learners, leading to mea-
surable improvements in speaking performance such as increased fluency, coherence, and
effective repair during interaction through real-life communicative practice and instant
feedback. Namely, the overarching inquiry is: How helpful will the application of structured
conversations mediated by ai chatbots be to develop learners’ use of communication strategies?
ai chatbot interactions are expected to establish a low-anxiety setting that guides fluency
through strong and mindful training, which in return is expected to foster strategic com-
petence and subsequent improvement of B1 learners’ communicative competence in the
target language. In addition, conversations mediated by ai are anticipated to enhance learn-
ers’ ability to handle communication difficulties, coordinate meaning making and monitor
conversation more effectively than traditional classroom instruction, therein the reason of
its conducting.

The problem addressed in this study is the insufficient development of strategic com-
petence in the speaking skills of B1 efl learners, as identified through diagnostic assess-
ments, classroom observations, and speaking task performance. B1 efl learners at Colegio
Bilingüe Marie Clarac. This deficiency manifested as hesitancy in communication, difficulty
understanding authentic materials, and limited use of language learning strategies.

AI Chatbot Interaction on Enhancing Strategic Competence in B1 EFL students

86 REVISTA KRONOS 6(2), agosto 2025 - enero 2026 | pISSN 12631-2840 | eISSN 2631-2859

Research Aim
This research aims to investigate the impact of ai chatbot interaction on enhancing strategic
competence in B1 efl learners. To accomplish this, the study will employ a mixed-meth-
ods approach, seamlessly integrating quantitative and qualitative data collection and anal-
ysis techniques.
Specific Research Objectives
The specific objectives of the research are threefold:

• To identify the specific strategic competence gaps in the target learners through a
likert survey.

• To evaluate the efficacy of the ai chatbot intervention in enhancing strategic compe-
tence dimensions such as planning, monitoring and regulation by means of the T-test
analysis of the pre and post-test results

• To explore the perceptions of B1-level students regarding the impact of ai chatbot
interaction on enhancing strategic competence through an open-ended interview.

LITERATURE REVIEW

ai Chatbot Interaction
Research on the implementation of ai chatbots in the English as a foreign language setting
promptly emphasizes their usefulness in giving adaptive feedback and developing language
in a strategic manner. Across instructional settings, there is evidence that chatbots have
promoted learner centered interaction by providing personalized feedback in real time, and
therefore, helping students learn communicative strategies and support independent learn-
ing (Amin, 2023; Kavak et al., 2024; Pitychoutis, 2024). This flexibility fosters low-anxi-
ety classroom conditions, in which students are more likely to take a chance with language
and to take strategic risks towards language; they are found in the secondary school and
teacher-training situations (Kalenska, 2024; Shikun et al., 2024).

A second theme related to the use of chatbots is the issue of communication break-
downs and repair strategies management. Empirical studies indicate that chatbot-mediated
interaction provides good practice in asking clarification, paraphrasing speech and meaning
negotiation. These three skills are core to strategic competence and conversational fluency
(Bibauw et al., 2022; Dippold, 2023; Fryer and Carpenter, 2006). The resulting low-
stakes setting helps learners to react adaptively to misconceptions, thus enhancing strategic
awareness or confidence in the context of interaction.

However, limitations and tensions involved in chatbot-supported learning are also
mentioned in the literature. Although chatbots facilitate fluency, motivated strategic ex-
perimentation, and a general mood of encouragement (Thi‛Quynh, 2024), their often
task-driven and fully automated character may result in both a contextual gap and over-
simplified feedback which need to be addressed strategically by learners in the absence
of tone (Dippold, 2023). Furthermore, there are also issues of misinformation and the
need of digital literacy to underline that the success of chatbots can also be determined by
informed and pedagogically consistent implementation (Kalenska, 2024). As Bibauw et
al. (2022) note, the benefits of chatbot interaction can be fully realized when this usage
is critically tied to the goal of instruction.

Díaz C. et al

87REVISTA KRONOS 6(2), agosto 2025 - enero 2026 | pISSN 12631-2840 | eISSN 2631-2859

Altogether, these works indicate that using ai chatbots facilitates the formation of strate-
gic capacity by means of adaptive feedback, real-time practice, and mitigating the affective
barriers, although at the same time, they also highlight the necessity of critical utilization
and mediation instruction to reduce the existing limitations of such systems. Collectively,
these studies confirm that chatbots support strategic development by enabling real-time
practice, adaptive support, and a low-anxiety learning context.

STRATEGIC COMPETENCE

In spoken interaction, strategic competence enables learners to maintain communica-
tion flow despite lexical or grammatical gaps, directly supporting speaking fluency. Shaik
(2024) characterized it as a blend of linguistic, sociolinguistic, and strategic skills, with
emphasis on cultural and contextual awareness. Kamil and Anuar (2022) addressed its
workplace relevance, focusing on metacognitive and cognitive strategies for overcoming
communicative hurdles. Malykhin et al. (2024) emphasized the need for targeted support
in grammar, speaking, and writing to develop these skills. Zhang et al. (2024) provided
empirical evidence showing how strategic competence moderates the impact of task com-
plexity on speaking performance. Akeshova et al. (2023) advocated for integrating tech-
nology in teaching strategic planning and communication strategies.

Dawit (2020) described strategic competence in speaking as the use of verbal and
non-verbal strategies to manage communication difficulties and ensure successful inter-
action. This includes paraphrasing, circumlocution, and clarification requests—techniques
vital to navigating gaps in linguistic knowledge. ai chatbots offer a safe environment
for students to practice and refine such strategies in real time (Malykhin et al., 2024).
Over time, repeated chatbot-mediated interaction enhances learners’ resilience and flu-
ency (Zhang et al., 2024). Oktaviana (2021) emphasized the role of repair strategies in
maintaining conversational coherence during spontaneous communication. However, the
existing body of knowledge undoubtedly supports the importance of strategic competence
and highlights the possible value of enhanced technology practice, but a lack of systematic
study exists in the design and implementation of ai chatbots to develop strategic com-
petence in B1 efl speaking setup. This gap highlights the need for research that moves
beyond general descriptions of strategy use to examine structured, level-specific chatbot
interventions targeting strategic competence development.

DIMENSIONS AND INDICATORS

AI chatbot interaction is discussed in the context of a trifunctional model consisting of
Focus, Adaptivity as well as Dialogue Management, each of which integrates key elements
of strategic competence. Focus is related to the formulation of conversation goals, the ex-
pression of learning objectives, and domain control (Luxton, 2016); these operations are
used to organize planning and goal-setting, which is a central component of strategic com-
petence within the model by Bachman and Palmer (1996). The focus maintenance, as a
part of the conceptualization of Celce-Murcia (2007), helps the learners to match the dis-
course organization with the communicative purpose. Adaptivity is based on the socio-cul-
tural theory of Vygotsky (1978), which describes the ability of the learner to monitor and
adjust the use of language in relation to feedback and scaffolding, thus reflecting the stra-
tegic monitoring and the use of compensatory strategies put forth by both theoretical sys-
tems. The concept of Dialogue Management, operationalized by the portrayed dimensions

AI Chatbot Interaction on Enhancing Strategic Competence in B1 EFL students

88 REVISTA KRONOS 6(2), agosto 2025 - enero 2026 | pISSN 12631-2840 | eISSN 2631-2859

of Complexity, Accuracy, and Fluency (caf) as outlined by Shen et al. (2021) and Zhang
(2023) represents regulatory processes, in which the learners control the interactional
flow, correct the instances of communicative breakdowns, and balance the form and mean-
ing in real-time. These dimensions, in combination, form a succinct theoretically based
perspective on the measure of strategic competence in ai-mediated speaking interaction.

Strategic Competence as a dependent variable is divided into three dimensions: Plan-
ning, Monitoring, and Regulation. Planning includes Goal Setting and Information Gather-
ing (Pintrich, 2000), while Monitoring involves Conversation Flow, Comprehension, and
Problem Solving (Brown, 1987). Regulation focuses on Self-Evaluation, Error Correction,
and Repair Strategies (Zimmerman, 2002), all crucial for iterative language improvement.
The holistic framework emerging from this structure illustrates how ai chatbots can drive
the development of critical communication strategies and learner autonomy.

RESEARCH APPROACHES AND METHODOLOGY

This research investigates the enhancement of strategic competence in B1 efl learners at
Colegio Bilingüe Marie Clarac in Ecuador through ai chatbot interactions. Strategic compe-
tence, as defined by Cambridge English (2020), involves the use of communication strat-
egies to overcome language barriers, particularly in speaking. Given the transitional stage
of B1 learners, ai-driven interventions like chatbots offer a practical solution for fostering
personalized and low-stakes conversational practice. The study explores how chatbot-based
interactions affect learners’ communicative strategies and awareness of strategic compe-
tence (Creswell & Plano Clark, 2017; Dörnyei, 2020; Cohen et al., 2018).

A mixed-methods approach was employed, integrating both quantitative and qualita-
tive data to provide a comprehensive analysis. Pre- and post-tests measured objective gains
in strategic competence, while surveys and interviews provided contextual insights into
learners’ experiences. This triangulation of data enhances validity by corroborating find-
ings from multiple sources (Tashakkori & Teddlie, 2010). The combination of numeric
data and learner reflections ensures a holistic understanding of how ai chatbots influence
strategic competence development (Johnson & Onwuegbuzie, 2018; Creswell & Creswell,
2018; Braun & Clarke, 2006).
Research Questions

1. What are the specific gaps in strategic competence among B1 efl learners at Colegio
Bilingüe Marie Clarac?

2. How meaningful is the ai chatbot-based intervention to enhance the strategic compe-
tence of B1 efl learners at Colegio Bilingue Marie Clarac?

3. What are the perceptions of B1-level students regarding the impact of ai chatbot inte-
raction on enhancing their strategic competence?

The research design will adhere to a pre-test, intervention, and post-test model. The pre-
test will assess learners’ baseline levels of strategic competence, followed by an interven-
tion period during which learners will actively interact with the ai chatbot. The post-test
will then measure any shifts in strategic competence, enabling an evaluation of the inter-
vention’s effectiveness.

Díaz C. et al

89REVISTA KRONOS 6(2), agosto 2025 - enero 2026 | pISSN 12631-2840 | eISSN 2631-2859

Research Instruments
Quantitative Methods included a Likert-scale survey assessing students’ awareness and
perceptions of strategic competence and chatbot use (Johnson & Onwuegbuzie, 2018). Pre-
and post-tests were the Cambridge fce Speaking exam. The adapted rubric assessed plan-
ning, monitoring, and regulation on a 0–5 scale to evaluate students’ deployment of learning
strategies, such as contextual inference and clarification-seeking. Statistical analysis deter-
mined the significance of observed changes.
Qualitative Methods involved open-ended interviews to explore their experiences with
the chatbot, strategies used, and perceived benefits. Ten semi-structured questions explor-
ing self-regulatory behaviours and attitudes toward ai-mediated practice. Thematic anal-
ysis was used to identify recurring patterns in student responses, offering deeper insights
into learners’ reflections.
Delimitation of Population, Sample, and Sampling
The student population at a private Bilingual School, located in Tumbaco, Ecuador consists
of 269 students. However, the sample considered for this study consisted of 10 students
(7 women and 3 men), 14-17 years of age, in the Edinburgh B2 class; all students were
identified as B1-level English speakers based on pet Mock tests. Sampling is the process
of selecting a subset of individuals from a larger population, ensuring that they adequate-
ly represent the population’s characteristics (Creswell & Creswell, 2018). For this study,
purposive or intentional sampling was employed, guided by specific criteria:

1. Age and Educational Context: Participants were teenagers aged 14 to 17, currently
attending the Edinburgh B2 class at Colegio Bilingüe Marie Clarac.

2. English Proficiency: The students have a B1 level of English proficiency, as determi-
ned by standardized pet Mock tests.

3. Developmental Suitability: At this stage, participants are developmentally primed
to enhance strategic competence, which supports their communicative effectiveness.

4. Technological Affinity: Their demonstrated interest in technology aligns with the
study’s use of ai-based tools, fostering engagement and relevance to future career
aspirations.

5. Institutional Support: The school environment supports their participation in in-
novative learning methodologies, ensuring the feasibility of AI-driven interventions.

6. Parental Consent: Parents of nearly all participants have provided consent, allowing
their children to engage in the study.

Description of the Methodological Proposal
The research proposal started with the diagnosis of students’ English language proficien-
cy which took place with the testing of pet mock exam in the academic year 2023-2024.
Having determined their proficiency, 11 students answered a Likert-survey in order to
gather insights regarding students’ awareness of strategic competence and expectations of
ai chatbot interaction, and consequently, took the pre-test. The pre-test consisted of the
Speaking section from fce exam. The scores about the level of strategic competence in
students were obtained by means of rubric tailored to measure Strategic competence in
student’s performance during the speaking section from fce exam (planning, monitoring,
regulation: 0–5 each). After that, the intervention was conducted. The proposed interven-
tion utilized ChatGPT’s dynamic role-play capabilities to simulate Cambridge fce Speaking
Exam tasks (Parts 1–4), structured through tailored prompts that target strategic com-
petence development. Students attended 4 lessons, one per week with a duration of 120
minutes each lesson. The learners attended four 120-min weekly lessons followed by the

AI Chatbot Interaction on Enhancing Strategic Competence in B1 EFL students

90 REVISTA KRONOS 6(2), agosto 2025 - enero 2026 | pISSN 12631-2840 | eISSN 2631-2859

post-test. Finally, the participants shared their perceptions and experiences regarding the
influence of ai chatbot interaction on their strategic competence.

GENERAL AND SPECIFIC OBJECTIVES OF THE PROPOSAL

General Objective:
To enhance B1 learners’ strategic competence in oral communication for effective perfor-
mance in interactive speaking tasks through structured ai chatbot simulations replicating
fce Speaking Exam tasks.
Specific Objectives:

1. To measure improvements in learners’ planning strategies by analyzing their ability
to set communicative goals and organize ideas during ai chatbot–mediated speaking
tasks, through pre- and post-test scores on the adapted fce Speaking rubric (planning
dimension: goal setting and information gathering, 0–5 scale) and supported by chat-
bot interaction logs showing response organization and task preparation.

2. To evaluate the development of monitoring skills by examining learners’ real-time
awareness of comprehension, conversation flow, and problem-solving during chatbot
interactions, assessed through pre- and post-test rubric scores (monitoring dimension),
quantitative gains identified via paired t-test analysis, and qualitative evidence from
interview responses reflecting self-monitoring and feedback utilization.

3. To assess growth in regulation abilities by documenting learners’ use of self-correction,
repair strategies, and reflective adjustments during ai chatbot interactions, measured
through pre- and post-test rubric results (regulation dimension), reductions in perfor-
mance variability, and thematic analysis of interview data highlighting self-evaluation
and strategic repair behaviors.

Research Question 1

What are the specific gaps in strategic competence among B1 efl learners at Colegio Bilingüe Marie Clarac?
Several gaps in strategic competence were notably identified after the administration of
the Likert survey. The 10 B1 students from Colegio Mariec Clarac reflected common in-
sights regarding their awareness of strategic competence and expectations of ai chatbot
interaction. As shown in the chart, approximately half of the survey items 5 out of the 10
items were used to assess specific aspects of strategic competence: planning, monitoring,
and regulation. The other 5 items evaluated student’s expectations for ai chatbot interac-
tion in accordance with focus, adaptability and dialogue management.

Findings revealed significant deficiencies in terms of planning, particularly in goal
setting and information gathering. Many students lacked clarity on the specific speaking
areas they needed to improve, which hindered structured learning (Oxford, 2017). The
absence of preparation often led to hesitancy and disorganized speech (Ahmed et al.,
2022). Instruction targeting metacognitive awareness and goal setting frameworks could
address these gaps (Akeshova et al., 2023).

Likewise, monitoring was another weak area. Students struggled to track their un-
derstanding during conversations and rarely asked for clarification or rephrased unclear
input, highlighting a lack of self-regulation (Ellis, 2005; Alem, 2020). ai chatbot inter-
actions, which provide instant feedback and scaffolding, have the potential to enhance
these skills by increasing learners’ awareness of speech patterns and comprehension gaps

Díaz C. et al

91REVISTA KRONOS 6(2), agosto 2025 - enero 2026 | pISSN 12631-2840 | eISSN 2631-2859

(Ahmed et al., 2022). Ultimately, regulatory skills were also underdeveloped. Students
rarely self-evaluated or reflected on their performance and were uncertain about how to
repair misunderstandings. This lack of metacognitive strategy use limited their autonomous
learning (Oxford, 2017). Structured chatbot tasks focused on reflection and feedback
could promote self-evaluation and repair strategies (Akeshova et al., 2023).

The gaps established after the survey administration were outlined into a table that
guided ai chatbot intervention design as shown in Table 1. This table synthesizes the most
recurrently low-rated items from the survey. In short, the empirical data shows a recur-
ring tendency in the lack of strategic competence that comes with a positive perspective
on the AI-enhanced interaction and consequently leads to the next research question that
examines how the current gap in the speaking performance of intermediate (B1) students
can possibly be addressed with the aid of a chatbot intervention.
Table 1. Strategic Competence Gaps
Gap Strategic Compe-

tence Aspect
Question Item

Lack of goal setting Planning I set specific goals for my speaking improvement, like
speaking more naturally or making fewer grammar mis-
takes.

Difficulty in infor-
mation gathering

Planning I feel confident gathering information I need for speaking
tasks, like thinking of key points for a conversation or
comparing photos.

Weak comprehen-
sion monitoring

Monitoring. I often check my understanding during conversations
and adjust if I don’t understand, like rephrasing or asking
for clarification.

Limited self-evalu-
ation

Regulation After speaking in English, I usually reflect on how I did
and consider what I could improve.

Struggles with repair
strategies

Regulation I know how to fix misunderstandings during a conver-
sation, like rephrasing what I said if the listener seems
confused.

Elaborated by the author

Research Question 2

How meaningful is the AI chatbot-based intervention to enhance the strategic competence of B1 efl learn-
ers at Colegio Bilingue Marie Clarac??
In order to measure the impact of ai chatbot interaction, a Cambridge fce Speaking mock
exam was employed. The rubric was adapted to explicitly assess strategic competence
through planning, monitoring, and regulation components (Akeshova et al., 2023). Pre-
and post-tests were administered to track student progress, and the results were analyzed
using a Paired T-test, which allows for the comparison of means in dependent samples and
helps determine significant changes over time (Ahmed et al., 2022).

The assessments, based on standardized fce mock speaking tests, were used to es-
tablish a baseline before the intervention and then to evaluate progress afterward. The
intervention itself ran from January 6 to February 6, 2025, and comprised four carefully
structured lesson plans. Each lesson targeted a different part of the fce Speaking section

AI Chatbot Interaction on Enhancing Strategic Competence in B1 EFL students

92 REVISTA KRONOS 6(2), agosto 2025 - enero 2026 | pISSN 12631-2840 | eISSN 2631-2859

and followed a four-stage structure: (1) Instruction and modeling of strategies, (2) Guided
chatbot practice with real-time feedback, (3) Independent exam-style practice, and (4)
Evaluation based on performance during chatbot interactions. The lessons were spaced
weekly: January 6–10, January 13–17, January 20–24, and January 27 to February 6. A
post-test on February 6 concluded the intervention phase. This structured approach en-
sured systematic practice and assessment of strategic competence.

Results showed improvements in key strategic competence indicators, especially in
planning. As detailed in Table 2, students demonstrated enhanced ability to structure
speech and set communication goals. Their mean planning score increased from 3.65 to
4.00, with percentage scores rising from 73% to 80%, indicating progress in goal setting
and information organization. An increased in standard deviation from 0.58 to 0.67 sug-
gested greater consistency across learners. The ai chatbot interaction supported structured
thinking and conversational preparedness, contributing to improved speech planning.
Table 2. Planning Pretest and Posttest Results
Nº Pretest (PLANNING) Posttest (PLANNING)
Student 1 3 3,5
Student 2 3 3,5
Student 3 4 4
Student 4 4 4
Student 5 4 5
Student 6 4 4,5
Student 7 4 4
Student 8 4,5 5
Student 9 3 3,5
Student 10 3 3
Average 3,65 4,00
Standard Deviation 0,58 0,67

Elaborated by the author

As Table 2 displays, the mean planning score increased from 3.65 in the pretest to 4.00
in the posttest, which is an indicator of an enhanced ability in goal setting and informa-
tion gathering, two major subcomponents. The increase in percentage scores from 73% to
80% showed a gain of 0.0 – 1.0 points, most learners did make progress. The increase in
the standard deviation from 0.58 to 0.67 indicates that competence also is uniform across
the group. The ai chatbot interaction reinforced conversational objectives and promoted
structured learning, which would have facilitated students’ speech planning by promoting
more effective outcomes, meaning that students would have prepared responses in a more
confident and organized manner.

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93REVISTA KRONOS 6(2), agosto 2025 - enero 2026 | pISSN 12631-2840 | eISSN 2631-2859

Figure 1. Planning Comparison Pretest and Posttest

As the statistical graph shows in Figure 1, the scores for planning post intervention have a
consistent upward trend. The posttest line drops above the pretest line indicating learners
improved their ability in planning conversations as can be seen in the cases of Students
5 and 8. Structured chatbot interactions also peak in individual performance where ex-
plicit support is provided for organizing ideas & setting goals. By analyzing the difference
in scores between the pretest and posttest, adaptivity of chatbot was noted since learn-
ers were delivered tailored feedback and scaffolding which in turn helped them to refine
their conversational preparation strategies. This notion fits well with the principles of Di-
alogue Management, which improved complexity, accuracy, and fluency (caf) in chatbot,
enabled simulations of actual real communication settings. Finally, the graph supports nu-
merical analysis that the ai chatbot interactions meaningfully contribute to improving the
planning skill through providing goal setting abilities and adaptive support in real time.
In terms of monitoring, self-monitoring skills, critical for real-time conversational adjust-
ments demonstrated substantial improvement as can be shown in Table 3.
Table 3. Monitoring Pretest and Posttest Results
Nº Pretest (MONITORING) Posttest (MONITORING)
Student 1 2 2,5
Student 2 3 3,5
Student 3 3,5 4
Student 4 4 4,5
Student 5 5 4,5
Student 6 4 4,5
Student 7 4 4,5
Student 8 4 5
Student 9 3 3,5
Student 10 4 4,5
Average 3,65 4,10
Standard Deviation 0,82 0,74

Elaborated by the author

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The ai chatbot interaction greatly improved students’ monitoring skills which constitute
an integral part of strategic competence. The mean score from the pretest monitoring
was 3.65 and at the posttest monitoring was 4.10, demonstrating improvement in the re-
al-time awareness of conversation flow, comprehension, and problem-solving strategies.
Besides, the percentages also improved from average 73% to 82% with gain from 0.0 to
1.0 points, supporting a better student’s ability to track comprehension and dynamically
adjust speech. After intervention, the standard deviation went from 0.82 to 0.74, imply-
ing greater uniformity in learners’ performance. This enhancement is, therefore, attribut-
able to the chatbot’s adaptation of scaffolding and personalized feedback, which reinforced
monitoring strategies by encouraging learners to detect misunderstandings, advocate for
changes in response, and sustain conversation flow. Structured prompts of the chatbot and
instant corrective feedback enabled more active engagement in internalizing the chatbot’s
suggestions of effective monitoring techniques for spoken communication.
Figure 2. Monitoring Comparison Pretest and Posttest

Elaborated by the author

As shown in figure 2, most students displayed upward score trajectories, particularly Stu-
dents 3, 6, and 8, whose gains were the most pronounced, showing that the students’ stron-
ger self-regulation strategies developed as they interacted with ai. Specifically, periods of
high-performance reflected occasions when chatbot scaffolding led to significantly higher
effects in facilitating adaptive comprehension adjustments and problem-solving skills. The
results gap between pretest and posttest confirmed that ai driven monitor support led to
an improvement in the learners’ spoken interactions, therefore, the chatbot intervention
was very successful in promoting learner’s ability to monitor and adjust speaking dynam-
ically in real-time conversation.

Lastly, the intervention yielded substantial gains in regulation skills, which are cru-
cial for repairing misunderstandings and refining spoken communication as is depicted
in table 4.

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Table 4. Regulation Pretest and Posttest Results
Nº Pretest (REGULATION) Posttest (REGULATION)
Student 1 3 3,5
Student 2 3 3,5
Student 3 3,5 4
Student 4 3 3,5
Student 5 4 4,5
Student 6 4 4,5
Student 7 3 3,5
Student 8 4 4
Student 9 3 3,5
Student 10 3 4
Average 3,35 3,85
Standard Deviation 0,47 0,41

Elaborated by the author

The table 4 shows the increase of the mean regulation score from 3.35 to 3.85, which
means that the ability for self-evaluation, error correction, and repair strategies improved.
Furthermore, the average percentage scores increased from 67% in pretest to 77% in
posttest, demonstrating more effective way of adjusting speech when communication break-
downs occurred. Finally, the standard deviation went down from 0.47 to 0.41, which
means participants were more uniform in their use of strategic self-regulation skills, im-
plying that chatbot interactions had increased the uniformity of strategic self-regulation
skills among learners. Adaptive scaffolding and real time feedback mechanisms of chat-
bot enabled scaffolding of structured error identification and self-correction opportunities
which accounted for the improvement made by the learners.
Figure 3. Regulation Comparison Pretest and Posttest

Elaborated by the author

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The overall improvement of learners’ ability to regulate their speech effectively, measured
by the overall improvement, is verified through the posttest line being above pretest line
in Figure 3. The chatbot’s Dialogue Management dimension allowed learners to analyze
their spoken errors, restructure responses and more academically refine their conversa-
tional style. The upward trend of the graph points to that with the support from chatbot,
more autonomous speech adjustment in response to feedback was performed and contin-
uous learning was achieved. Notably, peaks of performance in the posttest occurred af-
ter chatbot induced interventions that promote structured self-correcting strategies. This
affirmed research claims that AI generated corrective feedback encourages metacognitive
engagement, letting learners reflect on and bolster their performance while delivering oral
productions in real time.

The intervention dynamically helped learners identify errors, modify responses, and
strategically use language by providing task specific outcomes within a structured conver-
sational domain. Somehow, through the integration of the Conversational and Cognitive
AI aspects within the chatbot framework, a measurable and statistically significant im-
provement in the students’ ability to self-regulate speech was observed, thus successfully
testing out the efficacy of ai assisted interventions in helping students become autonomous
in language learning.
Table 5. T-test Results
N

Dimension
T-test
T-statistic P-value

10
Planning -3,28 ,010
Monitoring -3,86 ,004
Regulation -6,71 ,0001

Elaborated by the author

Results from the Paired T-test (p < 0.05) in Table 5, substantiate that the observed im-
provements were statistically significant and imply that the intervention played a part in
the students’ development of strategic competence. The p-values of the mean scores in
planning, monitoring, and regulation showed a statistically significant increase (p=0.010,
0.004, and 0.0001, respectively) corresponding to the efficacy of the intervention. This
could corroborate the statement that the ai chatbots act as good supplementary tools for
efl learners in helping them learn languages through guided feedback (Alem, 2020; Ake-
shova et al., 2023).

Altogether, data demonstrates that there is a clear distribution of improvement on the
three dimensions of strategic competence, and the gains are best exhibited in regulation,
monitored and, finally, in planning. This tendency indicates that whereas learners rein-
forced their ability to organize and plan oral answers at the beginning of the ai chatbot
intervention, the most significant pedagogical contribution of ai chatbot intervention was
the emergence of the strategic control in real-time, in particular, self-correction, repair,
and adaptive language usage. The statistically significant improvements in each of the three
dimensions are supported by both descriptive statistics and paired t -test outcomes and
suggest that, under structured chatbot interaction, the entire cycle of strategic competence
preparation through real-time awareness and autonomous regulation can efficiently be
scaffolded. Pedagogically, the results highlight the importance of ai chatbots as an adjunct
instructional aid offering consistent, low-anxiety fostering guided practice facilitating
learning of strategic behaviors to be effective recommended in touring oral communica-

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tion by B1 efl students. Combining goal-oriented prompts, an adaptive feedback system,
and dialogue-management functions in accordance with exam-oriented speaking tasks, the
intervention shows that ai-supported settings can promote strategic competence develop-
ment in a significant way; furthermore, this behavior occurs under classroom-situation efl.
Research Question 3

What are the perceptions of B1-level students regarding the impact of ai chatbot interaction on enhancing
their strategic competence?
The open-ended interview sought a perspective view from students on the uses of ai chat-
bots to improve English communication skills with an emphasis on strategic competence
development. Edinburgh B2 students participated in the interview in order to express their
perceptions of their experience interacting with the ai chatbot. The instrument consisted
of 10 items. The corresponding thematic analysis of the qualitative data was conducted
on ATLAS.ti from which common codes were drawn as shown in Figure 5 to be further
parsed and sorted into a table. The ten questions in the interview were designed to cap-
ture different aspects of strategic competence, such as conversational focus, adaptivity, di-
alogue management, planning, monitoring, and regulation. The purpose was to explore
students’ perceptions of the chatbot interactions and their speaking abilities, the strategies
to resolve potential speaking problems, the setting of possible goals, and self-monitoring.

The questions targeted different components of strategic competence: One area exam-
ined how the chatbot was supportive in helping students with organizing their thoughts
(Learning Objective); some addressed how adaptive the chatbot was in giving constructive
feedback and support (Adaptivity); there were a number of items addressing how the
chatbot made students aware of keeping activities in focus during conversational flow and
solving speaking difficulties (Dialogue Management and Monitoring). In addition, self-reg-
ulation questions were included so that the students could discuss how they evaluated
their own performance and made some changes (Self-Evaluation and Repair Strategies).
The interview was planned in a way that responses would be no less than 20 words long
so that detailed answers could give a good account of students’ experiences and ideas.
The interview primarily aimed to measure the extent of the effectiveness of ai chatbots in
raising students’ strategic competence in English communication.

Planning. Based on the results from the thematic, students emphasized that vocabu-
lary expansion was one of the major effects; many students found that feedback from the
chatbot introduced to them new lexical items and expressions that helped them formulate
more precise and contextually fitting responses (Nation, 2001). Moreover, the experience
of the chatbot fostered goal-oriented behaviour, as the students reported that they set
specific linguistic goals, e.g. aim to improve the fluency, or to enlarge the vocabulary,
which is consistent with the notion of self-directed learning. The centrality of these codes
indicates that planning strategies were frequently activated during chatbot interactions.
The findings suggest that ai chatbot interaction supported learners’ ability to plan spoken
responses more deliberately by fostering lexical preparation and structured goal setting.
Planning Thematic Analysis
Vocabulary Expansion: «Practicing with the chatbot improved my use of vocabulary and words
in general»; «The chatbot gave me new vocabulary and ways to express myself better»; «It gave
me tips such as vocabulary and syntax recommendations»

Goal Orientation: «I now focus on precision and rich vocabulary»; «I started setting small,
measurable goals»; «I began aiming to be a better English speaker»

Confidence and Clarity: «Helped me explain my ideas clearly without rushing»; «It deepened
my thinking and made me confident»; «I became more confident in expressing ideas naturally»

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Extended Vocabulary Use: «I expanded my vocabulary and responded more fluently»; «The
chatbot helped me connect and lengthen my ideas»; «It provided useful vocabulary for handling
long discussions»

Motivation for Improvement: «It motivated me to set both daily and long-term goals»;
«The chatbot influenced me to practice more and improve my speaking»; «I set even higher goals
to improve my speaking for a better future»

Monitoring. This dimension was positively impacted by chatbot interaction according
to Figure 7. The importance of providing structural guidance as well as immediate feed-
back was highlighted by students because it enabled them to organize their thoughts
more effectively and also to assess their performance in real time. Formative feedback
is seen to help learners self-monitor through being able to identify mistakes and make
adjustments in time (Hyland, 2003). This was consistent with the fact that students men-
tioned being more aware of the grammatical accuracy and coherence, as a result they
produced more coherent sentences as well as a logical progression in their spoken output
(Ellis, 2009). The prominence of these codes suggests that students perceived monitoring
as an active and ongoing process during chatbot use. Data indicate that chatbot-mediated
feedback played a key role in enhancing learners’ capacity to monitor comprehension,
accuracy, and conversational flow while speaking.

Structural Guidance: «I now follow a certain structure to answer the best»; «The chatbot
interaction helped me organize my thoughts by showing, comparing, and discussing different pho-
tos»; «It taught me to prioritize ideas instead of giving vague answers»

Feedback for Clarity: «The continuous feedback helped me read my answers and correct
them»; «It gave me advice before and after answering, helping me organize my thoughts»; «I was
able to analyze what I said and restructure my ideas»

Self-Evaluation: «It helped me identify mistakes and correct them»; «I analyze what I say
and make adjustments»; «I am more critical and focused on details»

Feedback Utilization: «I can evaluate myself using the chatbot’s feedback»; «The chatbot’s
corrections helped me adjust my speaking in real time»; «Feedback helped me improve my per-
formance faster»

Detail Awareness: «Now I am aware to add well-structured ideas»; «I learned to add rel-
evant details and use transition phrases»; «I became more aware of how much detail I provided»

Regulation. The chatbot interaction appeared most transformative in the final com-
ponent of regulation. Students reported adopting learned adaptive communication strat-
egies, for instance, rephrasing to avoid breakdowns, and using fillers to increase fluency of
conversation. Adaptive approaches conform well with strategic competence frameworks
emphasizing compensatory strategies for managing the communication gaps (Dörnyei
& Scott, 1997). These regulatory behaviors were reinforced by the chatbot’s capacity to
provide prompts and solutions at moments of hesitation (Goh & Burns, 2012) to bolster
their resilience to conversational challenges. The frequency of these codes indicates that
learners perceived chatbot interaction as particularly influential in supporting autonomous
speech adjustment. Findings showcase that chatbot interaction encouraged learners to ac-
tively regulate their spoken output through repair, adaptation, and strategic self-correction.

Self-Correction: «I corrected my ideas when the chatbot misunderstood me»; «I rephrased
and clarified my ideas during breakdowns»; «I fixed vocabulary mistakes in a history presentation»

Adaptive Communication: «I used new expressions to buy time when stuck»; «I found
alternative ways to express my thoughts»; «I explained and corrected errors with more confidence»

Prompt Support: «It provided me prompts and alternative expressions»; «I asked for exam-
ples when I ran out of ideas»; «It suggested ideas when I was stuck»

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Expansion Techniques: «Helped me take up previous ideas and add details»; «Gave me solu-
tions to keep the conversation engaging»; «Provided solutions and ideas for real-life conversations»
Conversation Management: «I found it easier to maintain a conversation by adding questions»;
«After feedback, my conversation sounds more natural»; «I know that a good interaction is im-
portant during the test»
Elaborated by the author

The findings point towards chatbot assisted practice as helpful to improve strategic com-
petence, nevertheless, some limitations were found. Many students even revealed that the
chatbot helped to plan and monitor, but at times gave unhelpful, generic feedback and thus
had to be clarified further. Such practice is consistent with studies that show the need for
personalized scaffolding in technology assisted language learning. In addition, the students
raised the issue of over reliance on the support of ai and emphasized the need for balanc-
ing interaction with ai with peer and teacher feedback in order to build overall communi-
cative competence (Warschauer, 2004). Therefore, it appears that ai chatbot interaction
helped B1 level students to develop their strategic competence, which is, namely, to en-
hance their planning, monitoring and regulation abilities (see Table 21). This resonates
with existing literature on the efficiency of interactive media in serving the purpose of au-
tonomous learning and strategic language use (Benson, 2011).

The comparison of the three thematic clusters demonstrates that there is a progres-
sive pattern in the perceptions of the learners. Whilst planning strategies can be related
to preparation and goal setting, and monitoring strategies with real-time awareness and
adjustment, regulatory strategies seem to be a higher level of strategic control; as a matter
of fact, the most common type of strategies that students focus on are regulatory aspects,
which presupposes that chatbot interaction can be very effective in aiding autonomous and
real-time management of verbal communication. This progression across themes reinforc-
es the view that ai chatbot interaction not only supports discrete strategic skills but also
facilitates their integration during authentic communicative performance.

Initial open coding (presented in Table 6) identified recurrent patterns in students’
responses, which were subsequently grouped and merged based on semantic similarity,
functional overlap, and alignment with the three predefined dimensions of strategic com-
petence: planning, monitoring, and regulation. Codes in which there was recurrence in
response within different answers were validated through frequency of use and conceptual
consistency. This was done to make sure that every final code was a measurable and sig-
nificant indicator of the strategic involvement of the learners in the interaction with the ai
chatbots without any repetition and maintaining the clarity of the analysis.
Table 6. Code Categorization

Planning Monitoring Regulation
Vocabulary Expansion Structural Guidance Self-Correction
Goal Orientation Feedback for Clarity Adaptive Communication
Motivation for Improvement Self-Evaluation Prompt Support
Extended Vocabulary Use Feedback Utilization Expansion Techniques
Confidence and Clarity Detail Awareness Conversation Management
Vocabulary Expansion Structural Guidance Self-Correction

Elaborated by the author

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FINDINGS

The aim of this research study was to investigate the contribution of ai chatbot interaction
to improve the strategic competence of B1-level efl learners at Colegio Bilingue Marie Cl-
arac. This objective was realized through the applications of several instruments, includ-
ing pretest and posttest assessments, a Likert-scale survey, and qualitative interviews. The
data analysis clearly showed that the intervention met the research objectives and demon-
strated a notable impact on students’ strategic competence. Overall, data analysis revealed
significant gains in learners’ strategic competence following ai chatbot interaction

RQ #1. The first research question aimed to identify particular gaps in the strategic
competence of B1 efl learners at the institution. The review of the available pre-test data
(n=10) demonstrated that there are evident weaknesses in the three aspects of strate-
gic competence planning, monitoring, and regulation. The average pre-test scores show
that regulation was the most problematic area of the learners (M 0 = 3.35), then comes
planning (M 0 = 3.65) and monitoring (M 0 = 3.65). The students had difficulties in
setting clear learning goals in the use of the language, in gathering adequate information
for speaking tasks, and in maintaining the flow of oral conversation. Another big gap was
insufficient self-regulation, which involved problems in rephrasing or asking for clarifi-
cation during breakdowns in communication. Results were inferred and based on pretest
data in which students were very challenged when planning their speech and managing
communication.

RQ #2. The second research question answered the extent by which ai chatbots
intervention would impact increase the students’ strategic competence. Results from the
posttest indicate that significant improvement across the three areas of strategic compe-
tence-planning, monitoring, and regulation-was made by students. The data showed that
students were better able to plan their reactions, maintain conversation flow, and regulate
their speech after interaction with the chatbot. Using paired t-tests for statistical analysis,
these developments were confirmed as significant and not due to random chance, given
the low p-values: M= 3.65 to M= 4.00 (p=.010); M= 3.65 to M= 4.10 (p=.004) and
M= 3.35 to M= 3.85 (p=.0001). These scores suggest that the learners were in a better
position to plan the spoken responses, track the understanding and accuracy of the real
time, and regulate their speech by using self-correction and repair methods.

RQ #3. The third research question intended to inquire about the students’ percep-
tions of the effects of the ai chatbot on their strategic competence. The qualitative analysis
resulting from interviews showed that, according to the students, the chatbot was very
effective in organizing their minds as far as their vocabulary is concerned. Many of the
students also reported that the chatbot helped them to set a goal in their speaking im-
provement such as targeting fluency and grammatical accuracy. Besides, they found that
the chatbot could provide feedback and adapt according to their needs, which assisted them
in monitoring their speech and self-correction when necessary. However, some of them
mentioned that feedback is generic sometimes and suggested that they should be given
more personalized support to enhance their experience further.

The intervention generalized its purpose towards enhancing participant strategic
competence by successfully meeting the proposed objectives. Through merging ai-mediated
interactions with the task-based language teaching format students obtained individualized
feedback for speaking practice of authentic tasks. Through both planning and monitoring
speech activities participants developed capable self-regulation tactics. Strategic competence
development in language learners became possible significantly because of the chatbot’s
structured tasks along with adaptive guidance and feedback process.

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Ultimately, the proposal demonstrated the feasibility and relevance of using ai chatbots as
an innovative tool in language learning. The results confirm that ai chatbots can serve as
a valuable resource for efl learners, offering a scalable and personalized solution to im-
prove speaking skills. The findings suggest that future research could further explore how
ai chatbots can be optimized to provide even more targeted support, particularly in ad-
dressing specific gaps identified during this study. The integration of ai in language learn-
ing not only enhances students’ strategic competence but also contributes to the broader
goal of fostering autonomous learners who can effectively manage their language develop-
ment. The quantitative gains and qualitative perceptions demonstrate that ai chatbot in-
teraction meaningfully enhanced learners’ strategic competence while also revealing areas
for refinement, which directly informs the discussion of pedagogical implications and lim-
itations presented in the following section.

CONCLUSIONS

The use of ai chatbot interactions in language learning has enabled new directions to im-
prove strategic competence in efl learners. The purpose of this study has been to assess if
ai chatbots are effective promoting planning, monitoring and regulating skills to B1 level
students at Colegio Bilingüe Marie Clarac. The research analyzed data from pre and post-
tests, surveys, and also interviews, utilising the findings to empirically prove that chatbot
based interventions can help develop learner autonomy and communicative adaptability.
The outcome of this study finds its fit into the ongoing discussions on ai-mediated lan-
guage education and its pedagogical implications.

Results of this study suggest that ai chatbot interaction can play a major role in im-
proving strategic competence of B1 efl learners (from Colegio Bilingüe Marie Clarac). Ev-
idence supports the effectiveness of chatbots through structuring, adapting, and simulating
interactive conversations aimed at improving learners’ planning, monitoring, and regulating
abilities in their language use. Data from the pretest and posttest assessments, surveys,
and interviews provided plenty of evidence to show that learners’ strategic competence
was positively influenced by the intervention. The mean improvements across the three
dimensions of strategic competence—planning, monitoring, and regulation—indicate that ai
chatbots serve as an effective supplementary tool in fostering communicative adaptability
and autonomy among efl learners.

Pedagogical and Theoretical Implications. This research makes a key contribution
in that it systematically applies ai chatbot technology for language learning, based on a
well-defined theoretical framework. Based on the principles of Focus, Adaptivity, and Di-
alogue Management for chatbot interactions between learners and the chatbot, this study
shows how AI mediated interventions can scaffold learners’ engagement with meaning-
ful communicative practices. Structured conversation design has been important to this
process because the chatbot is able to tailor feedback to the individual learning needs.
Vygotsky (1978) theory of scaffolding agrees with this since learners are gradually taught
communicative strategies that facilitate their ability to run conversations by themselves
although with guidance. Based on the results in the planning dimension, we can conclude
that chatbot interactions assist learners to establish communicative goals, gather necessary
information for speech production and plan to achieve their goals efficiently.

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CONTRIBUTOR ROLES

Carlos Díaz Ortiz: Conceptualization, research design, theoretical framework development,
methodology, instrument design, data collection, quantitative and qualitative data analysis,
interpretation of results, figure and table creation, original draft writing, review and edit-
ing, and final manuscript preparation.

Oswaldo González: Methodological consultation, support with statistical analysis, and
revision of results.

Janneth Chumaña: Assistance with data collection and participation in the review of
interview instruments.

Nelly Espinoza: Support in literature review compilation and minor manuscript re-
vision.

ETHICAL IMPLICATIONS

The authors state that there are no ethical implications associated with this study. Partici-
pation was voluntary, informed consent was obtained from all participants, and data were
handled confidentially.

CONFLICTS OF INTEREST

The authors declare that there are no financial or non-financial conflicts of interest that
could have influenced the work presented.

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