Ongoing Research

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:

1. What types of knowledge do teacher candidates develop while studying the topic of "technology-based teaching strategies" in the LLM_ChatGPT environment?
2. To what extent do the teacher candidates’ online self-regulated learning skill levels influence their interactions within the LLM_ChatGPT environment?
3. How do the questioning patterns used by teacher candidates in their communication with LLM_ChatGPT affect their learning in this environment?
4. What are the cognitive patterns of the questions teacher candidates direct to ChatGPT regarding the given learning content?