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Action Research Design Template

I. What is the topic of your action research?

The topic of this action research focuses on the implementation of AI-simulated conversational tools as a blended learning modality to support oral proficiency in a high school Spanish classroom. While peer-to-peer conversation is valuable, it does not fully replicate the experience of engaging with a fluent Spanish speaker. This study explores how AI-based simulated conversations can provide students with personalized, adaptive, and low-anxiety opportunities to practice Spanish conversation aligned to instructional content.
 

Second language acquisition is most effective when learners engage in meaningful interaction that develops communicative competence rather than isolated grammar or vocabulary practice. According to Masats, language learning is acquired through interaction that integrates verbal and non-verbal communication, emphasizing authentic conversation as a central component of fluency development (Masats, 2017).
 

II. What is the purpose of your study?

The purpose of this study is to examine how AI-simulated conversational tools can enhance students’ speaking skills, confidence, and engagement in a secondary Spanish classroom. This research aims to determine whether simulated conversational experiences can support authentic language use while reducing communication anxiety commonly associated with speaking in a second language.
 

Additionally, this study seeks to inform instructional practices and administrative decision-making related to the integration of artificial intelligence within World Language instruction. By collecting classroom-based data, this action research will provide evidence regarding the instructional value of AI-supported conversation practice in a blended learning environment.
 

III. What is your research question?

  • How does the use of AI-simulated conversational tools impact high school Spanish students’ oral proficiency, confidence, and engagement in second language learning?
     

Action research addresses instructional challenges by designing, implementing, and evaluating solutions within authentic classroom contexts. This research question focuses on improving conversational practice by integrating AI as a supportive instructional tool rather than a replacement for teacher-led or peer-based interaction. The study centers on how AI can function as a supplemental conversational partner that adapts to students’ proficiency levels and instructional needs.
 

IV. What is your research design (Qualitative, Quantitative, or Mixed Methods)?

This study will use a mixed-methods action research design that incorporates both qualitative and quantitative data to evaluate the effectiveness of AI-simulated conversational tools. Action research is appropriate because it allows the teacher-researcher to study the impact of an instructional innovation while actively teaching and reflecting on student outcomes.
 

a. Why did you choose this design?

A mixed-methods design provides a more comprehensive understanding of student learning and experience. Quantitative data will allow for measurement of changes in students’ oral proficiency and confidence, while qualitative data will capture student perceptions and engagement. Blended learning environments allow students to learn at their own pace and pathway, making them especially effective for personalized language practice (Horn & Staker, 2015). This design supports iterative improvement of instructional practice within a real classroom setting.
 

V. What data will you collect?

The data collected in this study will focus on student performance and student experience. Data sources will include:

  • Pre- and post-speaking assessments to measure growth in oral proficiency

  • Student surveys to measure confidence, anxiety, and perceptions of AI-based conversation practice

  • Student written reflections describing their learning experiences

  • Teacher observations documenting engagement and participation during AI-supported activities

The AI conversational tasks will be aligned with specific vocabulary, grammatical structures, and communicative goals taught in class to ensure instructional relevance.
 

VI. What types of measurement will you use?

Quantitative data from speaking assessments and surveys will be analyzed using descriptive statistics to identify trends in student growth and confidence. Qualitative data from student reflections and teacher observations will be analyzed thematically to identify recurring patterns related to engagement, anxiety reduction, and perceived effectiveness of AI-supported conversations.

Research suggests that chat-based and computer-mediated conversations can provide flexible, low-anxiety environments that support second language development (Piyumi et al., 2019). Combining quantitative and qualitative measures will allow for a richer understanding of how AI-simulated conversations influence both linguistic outcomes and learner affect.
 

VII. What is the focus of your literature review?

The literature review will establish a theoretical foundation for this study by examining the following constructs:

  • Interaction and communicative competence in second language acquisition

  • Blended learning models in K–12 education

  • Computer-mediated and AI-supported conversational learning

  • Learner confidence and anxiety in second language speaking

 

Masats emphasizes the importance of interaction in language acquisition, highlighting conversation as a critical component of fluency development (Masats, 2017). Blended learning frameworks support learner autonomy and flexibility, allowing students to engage in learning anytime and anywhere (Horn & Staker, 2015). Additionally, research on computer-mediated communication indicates that chat-based learning environments can reduce anxiety and improve learner confidence by providing familiar and flexible modes of interaction (Piyumi et al., 2019). Together, these perspectives inform the design and implementation of AI-simulated conversational tools as an instructional innovation in World Language education.
 

References 

Masats, D. (2017). Conversation analysis at the service of research in the field of second language acquisition (ERIC No. ED573594). ERIC. https://eric.ed.gov/?id=ED573594
 

Horn, M. B., & Staker, H. (2015). Blended: Using disruptive innovation to improve schools. Jossey-Bass.
 

Piyumi, W. A., Ola, K., & Sirkku, M. B. (2019). Complexity and potential of synchronous computer-mediated corrective feedback. In Proceedings of the EUROCALL 2019 Conference. Research-publishing.net.

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