The rapid advancement of technology is shaping educational policies while increasing the demand for a qualified workforce. Lesson planning plays a critical role in enhancing success in education and making the teaching process efficient (Yıldırım & Yıldırım, 2019). Teachers face challenges such as large class sizes and varying learning speeds, making it difficult to provide individual attention (Dhananjaya et al., 2024). By strengthening digital skills and adopting innovative approaches, well-organized lessons enhance teacher performance and provide students with an effective learning environment (Yıldırım & Yıldırım, 2019). In recent years, emerging technologies such as artificial intelligence have been utilized in education, aiming to improve learning and performance (Park et al., 2023).

Artificial intelligence (AI) enables machines to model learning processes by mimicking human intelligence (Coşkun & Gülleroğlu, 2021). In education, AI offers a wide range of applications not only through knowledge-based approaches but also through data- and logic-driven systems (Woolf, 2009). Generative AI refers to artificial intelligence systems with human-like language capabilities, typically trained using deep learning and neural networks. These systems can process data to produce, transform, or interpret meaningful content (Bozkurt, 2023). Generative AI contributes significantly to education, from personalized learning to improving administrative processes in schools (Holmes et al., 2019). A study examining the impact of generative AI on teachers and learners highlighted that AI makes classroom interactions more dynamic for teachers and provides students with personalized learning experiences (Dhananjaya et al., 2024).

Generative artificial intelligence (AI) has the potential to transform the roles of educators, allowing them to offer more personalized learning experiences (Kır & Şenocak, 2022). Teachers can delegate traditional tasks to AI, such as grading and providing feedback, while focusing on guidance and mentorship (Bozkurt & Sharna, 2023). However, the use of generative AI in education raises ethical concerns. While it can support educational practices, it may also undermine them. In addition to its ability to generate information, concerns about misinformation and disinformation persist (Lim et al., 2023).

On the other hand, teachers report being burdened with heavy workloads in tasks such as lesson preparation and student motivation, which negatively impact their psychological well-being (Kaymaz, 2021). It is emphasized that workload needs to be carefully assessed (Huyghebaert et al., 2018), and generative AI tools are said to have the potential to address these issues in instructional planning. However, due to concerns surrounding the use of AI in education, it is recommended to adopt a balanced approach and conduct studies on how this balance can be achieved (Bozkurt, 2023).

Considering this, the study aims to investigate the pedagogical suitability of generative AI tools used for lesson planning and to examine teachers' perspectives on these tools. By doing so, it seeks to reveal how and to what extent teachers can benefit from these tools, ensuring their effective use in education, and to present principles for their efficient implementation.

Within this scope, the research seeks to answer the following questions:

1. Can AI generate pedagogically appropriate lesson plans for computer science courses?

2. In what aspects do AI-generated lesson plans differ from those prepared by teachers?

3. How do teachers evaluate the lesson plans generated by AI?

This study is a qualitative research project aimed at examining lesson plans prepared using generative AI tools from the perspective of teachers. Qualitative research is an approach that seeks to explore and understand problems within their natural context (Klenke, 2016), and the phenomenological design was employed. This design typically focuses on phenomena that we are aware of but do not fully understand in depth (Yıldırım & Şimşek, 2011). The study aims to understand how teachers evaluate AI-supported lesson plans and their perspectives on these plans, making the phenomenological design the most suitable method for this purpose. In this research, prompts were provided to two generative AI tools to create lesson plans that include 5th-grade algorithm and problem-solving objectives and offer activities suitable for schools with and without computer labs. A total of four lesson plans were generated using the outputs of the two tools, and these plans were shared with teachers working in schools with and without computer labs, respectively. a. Study Group The study group consists of a total of 10 individuals who work as Information Technology and Software teachers in public or private institutions. b. Data Collection Tools The materials provided to teachers were prepared using the tools Teachology.ai and MagicSchool.ai. For data collection, a semi-structured interview was conducted using an extended version of Atmaca's (2006) "Daily Lesson Plan Evaluation Scale." c. Data Analysis The lesson plans generated by the two AI tools were evaluated through content analysis of interviews conducted with 10 teachers. Teachers were asked to assess the lesson plans based on objectives, content, activities, and assessment-evaluation criteria. To test the pedagogical suitability of the lesson plans and analyze teachers’ evaluations, a qualitative interview method was employed. The data were analyzed using the content analysis method. In this context, themes such as objectives (mismatch with lesson duration), content (density, consideration of individual differences, and alignment), assessment and evaluation (coverage), and generative AI tools (pedagogical suitability) were identified. A kappa analysis of inter-coder reliability revealed a high level of agreement between coders (κ = 0.82).

The study observed that the themes most frequently highlighted by participants were "objectives (mismatch with lesson duration)" and "content (density, individual differences)." Teachers stated that the activities in the lesson plans were overly dense (n=8, 80%) and that the allocated lesson time was insufficient for these activities (n=7, 70%). Participants noted that AI does not present pedagogical issues for computer science courses (n=8, 80%) but emphasized the need to diversify activities to address student differences (n=8, 80%). They highlighted the importance of extension activities in the MagicSchool tool (n=8, 80%) and stated that the lesson content was appropriate for both students and learning objectives (n=10, 100%). Additionally, they expressed the need to diversify assessment activities (n=6, 60%). Participants indicated that the quality of AI-generated lesson plans was not different from that of teacher-prepared lesson plans. However, they emphasized the importance of peer/self-assessment activities (n=5, 50%) and noted that due to individual differences, activities and topics should be tailored to specific classes (n=10, 100%). The participants stated that AI-generated lesson plans are practical for real-world use (n=10, 100%), but they emphasized that these plans should always be reviewed due to ethical concerns (n=10, 100%). They also remarked that AI facilitates teaching processes by presenting lessons clearly (n=6, 60%), aligning them with learning levels (n=9, 90%), and integrating real-life examples and systematic activities (n=10, 100%).

Atmaca, S. (2006). Fen Bilgisi Öğretmen adaylarının etkin öğrenme yaklaşımı konusundaki bilgi ve becerilerinin değerlendirilmesi. Ankara Üniversitesi Eğitim Bilimleri Enstitüsü, Ankara. Bozkurt, A. (2023). ChatGPT, Üretken Yapay Zeka ve Algoritmik Paradigma Değişikliği. Bozkurt, A., & Sharma, R. C. (2023). Challenging the status quo and exploring the new boundaries in the age of algorithms: Reimagining the role of generative AI in distance education and online learning. Asian Journal of Distance Education, 18(1). Coşkun, F., & Gülleroğlu, H. D. (2021). Yapay Zekanın Tarih İçindeki Gelişimi ve Eğitimde Kullanılması. Ankara University Journal of Faculty of Educational Sciences (JFES), 54(3), 947-966. https://doi.org/10.30964/auebfd.916220 Dhananjaya, G. M., Goudar, R. H., Govindaraja, K., Kaliwal, R. B., Rathod, V. K., Deshpande, S. L., ... & Hukkeri, G. S. (2024). Enhancing Education with ChatGPT: Revolutionizing Personalized Learning and Teacher Support. EAI Endorsed Transactions on Internet of Things, 10. Huyghebaert, T., Gillet, N., Beltou, N., Tellier, F. and Fouquereau, E. (2018). Effects of workload on teachers’functioning: A moderated mediation model induding sleeping problems and overcommitment. Stress and Health. 34, 601-611 Holmes, W., Bialik, M. ve Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Boston, MA: Center for Curriculum Redesign. Kaymaz, A. (2021). Uzaktan eğitim sürecinde değişen iş yükü ve etkilerine ilişkin öğretmen görüşleri. Uluslararası Liderlik Eğitimi Dergisi, I(I), 71-85. Kır, Ş., & Şenocak, D. (2022). Açık ve Uzaktan Öğrenme Sistemlerinde Yapay Zekânın Öğrenen Destek Hizmeti Bağlamında Kullanımı. Dijital Teknolojiler ve Eğitim Dergisi, 1(1), 36-56. Klenke, K. (2016). Qualitative research as method. In Qualitative research in the study of leadership (pp. 31-55). Emerald Group Publishing Limited. Lim, W. M., Gunasekara, A., Pallant, J. L., Pallant, J. I., & Pechenkina, E. (2023). Generative AI and the future of education: Ragnarök or reformation? A paradoxical perspective from management educators. The International Journal of Management Education, 21(2), 100790. Park, J., Teo, T.W., Teo, A. et al. Integrating artificial intelligence into science lessons: teachers’ experiences and views. IJ STEM Ed 10, 61 (2023). https://doi.org/10.1186/s40594-023-00454-3 Woolf, B. P. (2009). Building intelligent interactive tutors: Student-centered strategies for revolutionizing e- learning. San Francisco, CA: Morgan Kaufmann. Yıldırım, A. ve Şimşek, H. (2011). Sosyal Bilimlerde Nitel Araştırma Yöntemleri. Ankara: Seçkin Yayıncılık. Yıldırım, E., & Yıldırım, Ö. (2020). İlkokul ve Ortaokul Öğretmenlerinin Ders Planlama Yeterliklerinin İncelenmesi. Milli Eğitim Dergisi, 49(228), 7-37.