Yapay Zeka Tarafından Oluşturulan İngilizce Öğretimi Ders Planlarının Değerlendirilmesi: MagicSchool.ai ve ChatGPT Üzerine Rubrik Temelli Bir Karşılaştırmalı Analiz

Öz

Yapay zeka (YZ) araçları, eğitim bağlamlarında giderek daha fazla benimsenmektedir; ancak bu araçların pedagojik açıdan titiz İngilizce Öğretimi (ELT) ders planları üretme kapasitesine ilişkin ampirik kanıtlar sınırlıdır. Bu çalışma, yaygın olarak kullanılan iki YZ platformunun (MagicSchool.ai ve ChatGPT) ELT için ders planlama araçları olarak yapılandırılmış, rubrik temelli bir karşılaştırmalı değerlendirmesi yoluyla bu boşluğu ele almaktadır. Standartlaştırılmış bir öğretim senaryosu (A2 seviyesi, üniversite hazırlık sınıfı İngilizce, 18 öğrenci, hava kirliliği konulu 80 dakikalık oturum) kullanan araştırmacılar, yapılandırılmış uyum (Biggs, 1996), Bloom Taksonomisi (Anderson & Krathwohl, 2001), Diller için Avrupa Ortak Başvuru Metni (CEFR) ve Öğrenme Tasarımı spesifikasyonuna (Koper & Olivier, 2004) dayanan çok boyutlu bir değerlendirme rubriği uygulamıştır. Bulgular, her iki aracın da saniyeler içinde yapısal olarak işlevsel ders planları ürettiğini ve her birinin kendine özgü olanaklar sunduğunu göstermektedir: MagicSchool.ai, Rosenshine'in Öğretim İlkeleri ile örtük bir uyum sergilerken; ChatGPT, daha yüksek iç tutarlılık ve kaynak özgüllüğüne sahip, iskele oluşturulmuş çift oturumlu planlar üretmiştir. Her iki araç da farklılaştırma derinliği, dijital medya entegrasyonu ve süre tahsisi konularında sınırlılıklar göstermiştir. Çalışma, YZ çıktı kalitesini artırmak için pratik bir komut mühendisliği çerçevesi olan SCOPE protokolünü (Senaryo, Bağlam, Hedefler, Pedagoji, Genişletmeler) önermektedir. Öğretmen eğitimi, kurumsal YZ yönetişimi ve ELT mesleki gelişimi için çıkarımlar tartışılmaktadır.

Anahtar Kelimeler

• Yapay zeka
• ChatGPT
• İngilizce öğretimi (ELT)
• Ders planlama
• MagicSchool.ai
• Komut mühendisliği (Prompt mühendisliği)

Evaluating AI-Generated Lesson Plans for English Language Teaching: A Rubric-Based Comparative Analysis of MagicSchool.ai and ChatGPT

Öz

Artificial intelligence (AI) tools are increasingly being adopted in educational contexts, yet empirical evidence regarding their capacity to generate pedagogically rigorous English Language Teaching (ELT) lesson plans remains limited. This study addresses that gap through a structured, rubric-based comparative evaluation of two widely used AI platforms—MagicSchool.ai and ChatGPT—as lesson planning tools for ELT. Using a standardised teaching scenario (A2-level, university preparatory English, 18 students, 80-minute session on air pollution), the researchers applied a multi-dimensional evaluation rubric grounded in constructive alignment (Biggs, 1996), Bloom's Taxonomy (Anderson & Krathwohl, 2001), the Common European Framework of Reference for Languages (CEFR), and the Learning Design specification (Koper & Olivier, 2004). Findings reveal that both tools generated structurally functional lesson plans within seconds, each with distinct affordances: MagicSchool.ai demonstrated implicit alignment with Rosenshine's Principles of Instruction, while ChatGPT produced scaffolded, dual-session plans with higher internal coherence and resource specificity. Both tools exhibited limitations in differentiation depth, digital media integration, and time allocation. The study proposes a practical prompt engineering framework—the SCOPE protocol (Scenario, Context, Objectives, Pedagogy, Extensions)—to enhance AI output quality. Implications for teacher training, institutional AI governance, and ELT professional development are discussed.

Anahtar Kelimeler

• Artificial intelligence
• ChatGPT
• ELT
• Lesson planning
• MagicSchool.ai
• Prompt engineering.

Kaynakça

1. Anderson, L. W., & Krathwohl, D. R. (Eds.). (2001). A taxonomy for learning, teaching, and assessing: A revision of Bloom's taxonomy of educational objectives. Longman.
2. Barrett, A., & Pack, A. (2023). Not all artificial intelligence is equal: Considerations for AI in ELT contexts. RELC Journal, 54(2), 585–590. https://doi.org/10.1177/00336882221149104
3. Baytak, A. (2024). The content analysis of the lesson plans created by ChatGPT and Google Gemini. Research in Social Sciences and Technology, 9(1), 329–350. https://doi.org/10.46303/ressat.2024.19
4. Biggs, J. (1996). Enhancing teaching through constructive alignment. Higher Education, 32(3), 347–364. https://doi.org/10.1007/BF00138871
5. Brown, H. D. (2007). Teaching by principles: An interactive approach to language pedagogy (3rd ed.). Pearson Longman.
6. Cambridge Dictionary. (n.d.). ELT. In Cambridge Dictionary online. https://dictionary.cambridge.org Canagarajah, A. S. (1999). Resisting linguistic imperialism in English teaching. Oxford University Press.
7. Chang, J., Park, J., & Park, J. (2023). Using an artificial intelligence chatbot in scientific inquiry: Focusing on a guided-inquiry activity using Inquirybot. Asia-Pacific Science Education, 9(1), 44–74. https://doi.org/10.1163/23641177-bja10062
8. Chiu, T. K. F., Moorhouse, B. L., Chai, C. S., & Ismailov, M. (2023). Teacher support and student motivation to learn with artificial intelligence (AI) based chatbot. Interactive Learning Environments. Advance online publication. https://doi.org/10.1080/10494820.2023.2172044

9. Council of Europe. (2020). Common European framework of reference for languages: Learning, teaching, assessment: Companion volume. Council of Europe Publishing. http://www.coe.int/lang-cefr
10. Crompton, H., & Burke, D. (2023). Artificial intelligence in higher education: The state of the field. International Journal of Educational Technology in Higher Education, 20(22), 1–22. https://doi.org/10.1186/s41239-023-00392-8
11. Crompton, H., Burke, D., & Lin, Y. C. (2024). AI in ELT: A systematic review of the literature. Language Teaching Research. Advance online publication. https://doi.org/10.1177/13621688241229312
12. Darvishi, A., Khosravi, H., Sadiq, S., Gašević, D., & Siemens, G. (2024). Impact of AI assistance on student agency. Computers & Education, 210, 104967. https://doi.org/10.1016/j.compedu.2023.104967
13. Farhang, Q., Hashemi, S. S. A., & Ghorianfar, S. M. (2023). Lesson plan and its importance in teaching process. International Journal of Current Science Research and Review, 6(8), 5901–5913. https://doi.org/10.47191/ijcsrr/V6-i8-57
14. Halaweh, M. (2023). ChatGPT in education: Strategies for responsible implementation. Contemporary Educational Technology, 15(2), ep421. https://doi.org/10.30935/cedtech/13036
15. Harmer, J. (2015). The practice of English language teaching (5th ed.). Pearson Education.
16. Hativa, N. (2000). Becoming a better teacher: A case of changing the pedagogical knowledge and beliefs of law professors. Instructional Science, 28(5), 491–523. https://doi.org/10.1023/A:1026521725494
17. Hitchcock, C., Meyer, A., Rose, D., & Jackson, R. (2002). Providing new access to the general curriculum: Universal design for learning. Teaching Exceptional Children, 35(2), 8–17. https://doi.org/10.1177/004005990203500201
18. Jenkins, J. (2007). English as a lingua franca: Attitude and identity. Oxford University Press.
19. Karaman, M. R., & Göksu, İ. (2024). Are lesson plans created by ChatGPT more effective? An experimental study. International Journal of Technology in Education, 7(1), 107–127. https://doi.org/10.46328/ijte.607
20. Kočková, P., Kiliánová, K., Slepankova, M., & Kostolányová, K. (2024). A new era of academic research powered by AI. EDULEARN24 Proceedings, Article 10774. https://doi.org/10.21125/edulearn.2024.1724
21. Koper, R., & Olivier, B. (2004). Representing the learning design of units of learning. Educational Technology & Society, 7(3), 97–111.
22. Koraishi, O. (2023). Teaching English in the age of AI: Embracing ChatGPT to optimize EFL materials and assessment. Language Education & Technology, 3(1), 55–72.
23. Landis, J. R., & Koch, G. G. (1977). The measurement of observer agreement for categorical data. Biometrics, 33(1), 159–174. https://doi.org/10.2307/2529310
24. Li, L., Chen, C. P., Wang, L., Liang, K., & Bao, W. (2023). Exploring artificial intelligence in smart education: Real-time classroom behavior analysis with embedded devices. Sustainability, 15(10), 1–24. https://doi.org/10.3390/su15107940
25. Littlewood, W. (2004). The task-based approach: Some questions and suggestions. ELT Journal, 58(4), 319–326.
26. Macalister, J., & Nation, I. S. P. (2019). Language curriculum design (2nd ed.). Routledge. https://doi.org/10.4324/9780429203763
27. Meyer, A., Rose, D. H., & Gordon, D. (2014). Universal design for learning: Theory and practice. CAST Professional Publishing.
28. Nguyen, A., Ngo, H. N., Hong, Y., Dang, B., & Nguyen, B. P. T. (2023). Ethical principles for artificial intelligence in education. Education and Information Technologies, 28(4), 4221–4241. https://doi.org/10.1007/s10639-022-11316-w
29. Petty, G. (2009). Teaching today: A practical guide (4th ed.). Nelson Thornes.
30. Pham, T. T., & Le, T. T. (2024). Exploring the impact of artificial intelligence on student creativity in Vietnamese tertiary EFL classrooms: Teacher perspectives. Jurnal Komunikasi Pendidikan, 8(2), 116–128. https://doi.org/10.32585/jurnalkomdik.v8i2.5052
31. Richards, J. C. (2020). Exploring emotions in language teaching. RELC Journal, 51(3), 225–239. https://doi.org/10.1177/0033688220944181
32. Richards, J. C., & Rodgers, T. S. (2014). Approaches and methods in language teaching (3rd ed.). Cambridge University Press.
33. Rose, D. H., Meyer, A., & Hitchcock, C. (Eds.). (2014). The universally designed classroom: Accessible curriculum and digital technologies. Harvard Education Press.
34. Rosenshine, B. (2012). Principles of instruction: Research-based strategies that all teachers should know. American Educator, 36(1), 12–19.
35. Schön, D. A. (1992). The reflective practitioner: How professionals think in action. Routledge. (Original work published 1983)
36. Seidlhofer, B. (2011). Understanding English as a lingua franca. Oxford University Press.
37. Snow, D. (2006). More than a native speaker: An introduction to teaching English abroad. Teachers of English to Speakers of Other Languages.
38. Tomlinson, C. A. (2001). How to differentiate instruction in mixed-ability classrooms (2nd ed.). ASCD.
39. Trust, T., Whalen, J., & Mouza, C. (2023). Editorial: ChatGPT: Challenges, opportunities, and implications for teacher education. Contemporary Issues in Technology and Teacher Education, 23(1), 1–23.
40. UNESCO. (2021). Recommendation on the ethics of artificial intelligence. UNESCO.
41. Van Den Berg, G., & Du Plessis, E. (2023). ChatGPT and generative AI: Possibilities for its contribution to lesson planning, critical thinking and openness in teacher education. Education Sciences, 13(10), Article 998. https://doi.org/10.3390/educsci13100998
42. Wang, T., Lund, B. D., Marengo, A., Pagano, A., Mannuru, N. R., Teel, Z. A., & Pange, J. (2023). Exploring the potential impact of artificial intelligence (AI) on international students in higher education. Applied Sciences, 13(11), Article 6716. https://doi.org/10.3390/app13116716
43. Ziqi, C., Xinhua, Z., Qi, L., & Wei, W. (2024). L2 students' barriers in engaging with form and content-focused AI-generated feedback in revising their compositions. Computer Assisted Language Learning, 1–21. https://doi.org/10.1080/09588221.2024.2422478