Integrating GenAI Tools in TEFL Practicum Settings: A Comprehensive Case Study
Introduction to GenAI in Teaching
As technology continues to evolve, the education landscape is reshaping itself to integrate new tools, one of the most transformative being Generative Artificial Intelligence (GenAI). This study adopts a qualitative case study approach to delve into how pre-service English language teachers incorporate GenAI tools during their practicum in Teaching English as a Foreign Language (TEFL). The investigation aims to elucidate the roles these tools play in pedagogical decision-making and their perceived impact on language learning.
Case studies are particularly valuable when exploring contemporary phenomena within their real-life contexts, allowing for an in-depth understanding of the interactions between the tools and their educational environments. The TEFL practicum—characterized by its complexity and authenticity—provides a dynamic backdrop for examining the integration of these innovative tools.
Methodological Framework
To enhance the study’s rigor, it was framed within the Lesson Study (LS) methodology, a well-established model for teacher professional development. This methodology emphasizes collaborative planning, observation, and reflection, permitting an iterative exploration of GenAI tool integration. Within this framework, teacher participants designed lessons utilizing AI tools, implemented them in their classrooms, and engaged in reflective dialogues with peers and mentors. This synergy allowed the researchers to gather rich data about the situated complexities of GenAI use and the evolving pedagogical reasoning of novice teachers.
The LS framework afforded a systematic structure for data collection across phases, facilitating triangulation from diverse sources and participants over time.
Participants
The study involved ten pre-service English language teachers in their final year of a four-year teacher education program at a state university in Türkiye. The cohort consisted of seven males and three females, aged between 22 and 24. They had completed the requisite theoretical coursework and were engaged in practicum experiences across various K–12 schools.
A purposive sampling strategy was utilized to select participants who had prior exposure to technology in materials design. This homogenous group was deemed appropriate as their English proficiency levels were similar, evidenced by their academic performance.
Educational Context
The research was conducted during the final semester of a teacher education program. At this point, participants had completed core coursework in areas such as second language acquisition and language teaching methodology. The emphasis on technology in previous courses equipped them with both theoretical and practical insights applicable in real-world teaching scenarios.
Participants concurrently attended their second and final teaching practicum, which featured dual supervision from experienced mentors and university supervisors. This capstone component emphasized the integration of digital technologies, encouraging participants to critically assess their teaching practices and those of their peers.
Research Procedure and Phases
The study unfolded in five distinct phases to provide a comprehensive exploration of GenAI integration:
Phase 1: Study – GenAI Tools and Discussion
During the initial weeks, participants attended a workshop aimed at familiarizing them with various GenAI tools, including Large Language Models like ChatGPT and activity generators such as Twee and Kahoot!. This phase provided critical discussions on ethical considerations, including privacy and academic integrity, encouraging participants to develop a code of ethics for GenAI use in their teaching.
Phase 2: Plan – Lesson Planning and Critical Evaluation
This phase focused on leveraging GenAI tools to create lesson materials aligned with their practicum curriculum. Participants generated tailored resources like quizzes and reading passages while emphasizing cultural appropriateness and transparency regarding AI-generated content. Collaboration in reviewing these materials fostered a culturally responsive and student-centered learning environment.
Phase 3: Teach – GenAI-Enhanced Lessons
Participants implemented their lesson plans in real classrooms, integrating GenAI tools to engage students. The use of interactive quizzes and games helped sustain student interest and foster participation, highlighting how immediate feedback facilitated language acquisition. A focus on safeguarding student data was also paramount during real-time AI applications.
Phase 4: Observation – Structured Observations and Feedback
During this phase, mentors and peers observed the implementation of AI-integrated lessons. Using structured checklists, they collected data on engagement and interaction levels, ensuring a well-rounded understanding of how GenAI tools enhanced lesson delivery. Following observations, feedback sessions provided valuable insights into teaching effectiveness and the implications for student-teacher dynamics.
Phase 5: Reflect – Reflective Analysis and Ethical Challenges
Participants engaged in reflective sessions where they analyzed the impact of GenAI on their teaching efficacy and student outcomes. Discussions highlighted specific instances where AI tools benefited their practices and identified challenges that arose. The collective reflections culminated in a code of practice for future use, offering guidelines for responsible GenAI integration in lesson planning.
Data Collection and Instruments
Qualitative data were collected from various sources, including lesson plans, self-reports, peer evaluations, and discussion notes. Each participant contributed lesson plans, observation checklists, and reflective journals, culminating in a rich dataset that facilitated analysis of GenAI tool integration into language teaching.
Data Analysis Procedures
The data analysis leveraged a thematic approach, allowing for the discernment of significant patterns within the participants’ experiences. The process involved several stages, starting with familiarization with the data through transcription and careful reading. Initial coding identified meaningful segments, which were then grouped into emerging themes.
The process allowed for cross-case comparisons, revealing how contextual factors influenced the integration of GenAI tools. To ensure validity, member checking and peer debriefing were implemented, confirming the authenticity of the interpretations derived from the data.
Conclusion
The integration of GenAI tools into TEFL practicums presents a promising avenue for enhancing teaching methodologies and improving outcomes for language learners. As novice teachers engage with these innovative technologies, their experiences provide invaluable insights into the evolving educational landscape and highlight the need for ongoing reflection and ethical considerations in pedagogical practices.