Ongoing Research
The integration of AI into educational settings has created a growing demand for educators with AI-related competencies. In response, this study aims to prepare future teachers who are not only digitally literate but also capable of effectively integrating AI into their teaching practices. The outcomes of this project aim to lay a foundation for incorporating AI competencies into teacher education programs. By aligning the course design with the DigCompEdu framework and extending it to include AI-specific dimensions, the project supports the development of future-ready educators. The findings will contribute to the expanding body of literature on digital and AI competencies in teacher education, offering practical insights for curriculum developers, educational stakeholders, and policymakers. Moreover, both prospective and in-service teachers stand to benefit from these findings, which highlight actionable strategies for professional growth in technology-enhanced learning contexts. To examine the impact of the designed course, the study is guided by the following overarching and sub-research questions:
Overarching Research Question
How do prospective teachers' AI competencies evolve after engaging with the designed course?
- RQ1. How pedagogically and technologically appropriate are the AI-integrated lesson plans developed by prospective teachers?
- RQ2. What are the prospective teachers’ opinions on utilizing AI-driven educational applications before and after engaging with the course?
- RQ3. What are the prospective teachers’ perceptions of their digital competencies in relation to their AI competencies after engaging with the course?
Technological advancements provide various tools to enhance teachers' teaching capabilities and students' learning capacities. One such tool is artificial intelligence applications based on large language models (LLMs). LLM-based AI applications can perform functions such as generating exam questions and exercise materials for learning environments, solving problems in various subject areas, providing feedback, grading exams, and recommending learning resources both within and outside learning management systems. Utilizing this new type of educational technology requires a significantly different approach compared to previous educational technologies. This is because these applications can produce content across a wide range of areas, and the quality of the content generated depends on the user's approach. When employing LLMs, the inquiry strategies used in interactions with these environments become an important variable.
This study aims to examine the questioning patterns and the learning outcomes of teacher candidates as they interact with a large language model-based environment, ChatGPT (LLM_ChatGPT), while studying specific theories and approaches related to "technology-based teaching strategies." The study seeks to address the following research questions:
- What types of knowledge do teacher candidates develop while studying the topic of "technology-based teaching strategies" in the LLM_ChatGPT environment?
- To what extent do the teacher candidates’ online self-regulated learning skill levels influence their interactions within the LLM_ChatGPT environment?
- How do the questioning patterns used by teacher candidates in their communication with LLM_ChatGPT affect their learning in this environment?
- What are the cognitive patterns of the questions teacher candidates direct to ChatGPT regarding the given learning content?
Kalkınma Bakanlığı Araştırma Altyapısı: Robotik ve Yapay Akıl Laboratuvarları (ROYAL)
PI: Prof. Dr. Diler Öner
2023-2025
