SavedAdd your journal

Journals / Sakarya University Journal of Education / 2026 / Volume: 15 Issue: 1

Evaluating the Efficacy of AI-Generated Inquiry-Based Lesson Plans in Science

Nurcan Kahraman(ULUDAG UNIVERSITY, BURSA SCHOOL OF EDUCATION)0000-0003-1009-3047
Gülbin Kıyıcı(MANISA CELAL BAYAR UNIVERSITY)0000-0002-5402-0117
Pages
—
Publication Date
2025-04-30
🏛️Bibliographic metadata and full text access for this article are provided via DergiPark.
DergiPark Record ↗
✨

AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The results suggest that the artificial intelligence-generated lesson plans were aligned with the course objectives, incorporated real-world contexts, and promoted an inquiry-based learning environment.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:This study aims to investigate the effectiveness of artificial intelligence in creating inquiry-based lesson plans for science education in the context of Turkey.
📊
Key Findings & Data:Then, the plans were evaluated by the researchers, considering the rubric of the Guidelines for Assessing Inquiry-based Lesson Plans developed by Borda-Carulla and Harmen (2012).
💡
Conclusion & Impact:The results suggest that the artificial intelligence-generated lesson plans were aligned with the course objectives, incorporated real-world contexts, and promoted an inquiry-based learning environment.
🔬Design:Research Article
📍Scope:2012 Verileri
⚙️Variables:AI-Generated · Lesson Planning · Science Education

Abstract

This study aims to investigate the effectiveness of artificial intelligence in creating inquiry-based lesson plans for science education in the context of Turkey. An exploratory design was used to achieve this goal. The subject matter of middle school science was treated as a case and ChatGPT was asked to create lesson plans by specifying the objectives and duration of the lessons. Then, the plans were evaluated by the researchers, considering the rubric of the Guidelines for Assessing Inquiry-based Lesson Plans developed by Borda-Carulla and Harmen (2012). The researchers of this study evaluated the lesson plans separately, and the internal consistency coefficient was calculated as 0.83. The results suggest that the artificial intelligence-generated lesson plans were aligned with the course objectives, incorporated real-world contexts, and promoted an inquiry-based learning environment. Despite these strengths, the results of this study also suggest that some areas need to be improved, such as providing structured feedback and fostering curiosity.

🔖

Cite This Article

Kahraman, N., Kıyıcı, G. (2025). Evaluating the Efficacy of AI-Generated Inquiry-Based Lesson Plans in Science. Sakarya University Journal of Education, Volume: 15 Issue: 1. https://doi.org/10.19126/suje.1463067

📄 Full Text PDF Viewer

📄

PDF görüntüleyici hazırlanıyor...

References (55)

  1. Açıkgöz, D., Sağır, Ş. U., & Ozan, F. (2018). Araştırma sorgulamaya dayalı öğretim hakkında öğretmen tutumları ölçek uyarlama çalışması. Uluslararası Türk Eğitim Bilimleri Dergisi, 6(11), 11–14.
  2. Barrow, L. H. (2006). A brief history of inquiry: From Dewey to standards. Journal of Science Teacher Education, 17(3), 265–278. https://doi.org/10.1007/s10972-006-9008-5
  3. Borda-Carulla, S., & Harmen, W. (2012). Tools for enhancing inquiry in science education.
  4. Brennan, R. L., & Prediger, D. J. (1981). Coefficient kappa: Some uses, misuses, and alternatives. Educational and Psychological Measurement, 41(3), 687–699. https://doi.org/10.1177/001316448104100307
  5. Can, G. (1998). Fen bilgisi öğretiminde ölçme ve değerlendirme. Ş. Yaşar (Ed.), Fen bilgisi öğretimi.
  6. Chen, L., Chen, P., & Lin, Z. (2020). Artificial intelligence in education: A review. IEEE Access, 8, 75264–75278. https://doi.org/10.1109/ACCESS.2020.2988510
  7. Cleovoulou, Y., & Beach, P. (2019). Teaching critical literacy in inquiry-based classrooms: Teachers' understanding of practice and pedagogy in elementary schools. Teaching and Teacher Education, 83, 188–198. https://doi.org/10.1016/j.tate.2019.04.012
  8. Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.).
  9. Cooper, G. (2023). Examining science education in ChatGPT: An exploratory study of generative artificial intelligence. Journal of Science Education and Technology, 32(3), 444-452. https://doi.org/10.1007/s10956-023-10039-y
  10. Cooper, J. M. (2011). The effective teacher. In J. M. Cooper (Ed.), Classroom teaching skills (9th ed.).
  11. Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches. Sage Publications.
  12. Cullern, K. A. (2014). Leading curriculum change: Developing inquiry-based teaching and learning in a primary school [Doctoral dissertation, University of Nottingham].
  13. Çelik, I. (2023). Towards Intelligent-TPACK: An empirical study on teachers' professional knowledge to ethically integrate artificial intelligence (AI)-based tools into education. Computers in Human Behavior, 138, Article 107468. https://doi.org/10.1016/j.chb.2022.107468
  14. Çelik, I., Dindar, M., Muukkonen, H., & Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66(4), 616–630. https://doi.org/10.1007/s11528-022-00715-y
  15. Degenhart, H. S. (2010). Relationship of inquiry-based learning elements on changes in middle school students' science, technology, engineering, and mathematics (STEM) beliefs and interests [Doctoral dissertation, Texas A & M University].
  16. Dobber, M., Zwart, R., Tanis, M., & van Oers, B. (2017). Literature review: The role of the teacher in inquiry-based education. Educational Research Review, 22, 194–214. https://doi.org/10.1016/j.edurev.2017.09.002
  17. Fibonacci. (2012). Inquiry in mathematics education. http://fibonacci.uni-bayreuth.de/resources/resources-for-implementing-inquiry.html
  18. Güler, B., & Sahin, M. (2018). The inquiry-based learning from pre-service science teachers' perspective. Kastamonu Eğitim Dergisi, 26(5), 1561-1569. https://doi.org/10.24106/kefdergi.2146
  19. Hamilton, M. L., Smith, L., & Worthington, K. (2008). Fitting the methodology with the research: An exploration of narrative, self-study and auto-ethnography. Studying Teacher Education, 4(1), 17–28. https://doi.org/10.1080/17425960801976321
  20. Havuz, C., & Karamustafaoğlu, S. (2016). Fen bilgisi öğretmen adaylarının araştırma-sorgulamaya dayalı öğrenme algılarının incelenmesi. Amasya Üniversitesi Eğitim Fakültesi Dergisi, 5(1), 233–247.
  21. Hüner, S. B. (2018). Sokratik sorgulama temelli etkinliklerin hayat bilgisi dersinde başarı ve kalıcılığa etkisinin incelenmesi: Bir eylem araştırması [Doktora tezi, İstanbul Üniversitesi].
  22. Inoue, N., Asada, T., Maeda, N., & Nakamura, S. (2019). Deconstructing teacher expertise for inquiry-based teaching: Looking into consensus building pedagogy in Japanese classrooms. Teaching and Teacher Education, 77, 366-377. https://doi.org/10.1016/j.tate.2018.10.016
  23. Jerrim, J., Oliver, M., & Sims, S. (2019). The relationship between inquiry-based teaching and students' achievement. New evidence from a longitudinal PISA study in England. Learning and Instruction, 61, 35–44. https://doi.org/10.1016/j.learninstruc.2018.12.004
  24. Johnson, L., Smith, R., Willis, H., Levine, A., & Haywood, K. (2017). The 2017 horizon report. EDUCAUSE Learning Initiative.
  25. Jonsson, A., & Svingby, G. (2007). The use of scoring rubrics: Reliability, validity and educational consequences. Educational Research Review, 2, 130–144. https://doi.org/10.1016/j.edurev.2007.05.002
  26. Kayacan, K., & Selvi, M. (2017). Öz düzenleme faaliyetleri ile zenginleştirilmiş araştırma-sorgulamaya dayalı öğretim stratejisinin kavramsal anlamaya ve akademik öz yeterliğe etkisi. Kastamonu Eğitim Dergisi, 25(5), 1771-1786.
  27. Keçeci, G. (2014). Araştırma ve sorgulama dayalı fen öğretiminin öğrencilerin bilimsel süreç becerilerine ve tutumlarına etkisi [Doktora tezi, Fırat Üniversitesi].
  28. Keselman, A. (2003). Supporting inquiry learning by promoting normative understanding of multivariable causality. Journal of Research in Science Teaching, 40(9), 898–921. https://doi.org/10.1002/tea.10115
  29. Khan, I. A. (2011). Lesson planning for teaching English. Elixir Social Studies, 37, 3958-3964.
  30. Krajcik, J. S., & Czerniak, C. M. (2014). Teaching science in elementary and middle school: A project-based approach. Routledge.
  31. Liang, Q., de la Torre, J., & Law, N. (2021). Do background characteristics matter in children's mastery of digital literacy? A cognitive diagnosis model analysis. Computers in Human Behavior, 122, Article 106850. https://doi.org/10.1016/j.chb.2021.106850
  32. Luckin, R., Holmes, W., Griffiths, M., & Forcier, L. B. (2016). Intelligence unleashed: An argument for AI in education. Pearson Education.
  33. Millî Eğitim Bakanlığı. (2018). Fen bilimleri dersi öğretim programı (İlkokul ve ortaokul 3, 4, 5, 6, 7 ve 8. sınıflar).
  34. Moskal, B. M., & Leydens, J. A. (2000). Scoring rubric development: Validity and reliability. Practical Assessment Research & Evaluation, 7(10). http://PAREonline.net/getvn.asp?v=7&n=10
  35. National Research Council. (2000). Inquiry and the national science education standards: A guide for teaching and learning. The National Academies Press. https://doi.org/10.17226/9596
  36. OpenAI. (2023). ChatGPT: Optimizing language models for dialogue. https://openai.com/blog/chatgpt/
  37. Pender, H. L., Bohl, L., Schönberger, M., & Knopf, J. (2022). An AI-based lesson planning software to support competency-based learning. International Conference on Higher Education Advances, 2022-June, 1033–1041. https://doi.org/10.4995/HEAd22.2022.14599
  38. Saka, T. (2018). 5. Sınıf fizik konularının öğretimine yönelik rehberli araştırma sorgulamaya dayalı öğretmen kılavuz materyali geliştirilmesi ve değerlendirilmesi [Doktora tezi, Trabzon Üniversitesi].
  39. Sarıoğlan, A. B., Can, Y., & Gedik, İ. (2016). 6. Sınıf fen bilimleri ders kitabındaki etkinliklerin araştırma-sorgulamaya dayalı öğrenme yaklaşımına uygunluğunun değerlendirilmesi. Abant İzzet Baysal Üniversitesi Eğitim Fakültesi Dergisi, 16(3), 1004–1025.
  40. Sergis, S., Sampson, D. G., Rodríguez-Triana, M. J., Gillet, D., Pelliccione, L., & de Jong, T. (2019). Using educational data from teaching and learning to inform teachers' reflective educational design in inquiry-based STEM education. Computers in Human Behavior, 92, 724-738. https://doi.org/10.1016/j.chb.2017.12.014
  41. Suduc, A. M., Bizoi, M., & Gorghiu, G. (2015). Inquiry-based science learning in primary education. Procedia-Social and Behavioral Sciences, 205, 474-479. https://doi.org/10.1016/j.sbspro.2015.09.044
  42. Şahingöz, S. (2017). An investigation of Turkish middle school science teachers' pedagogical orientations towards direct and inquiry instructional approaches [Doctoral dissertation, Western Michigan University].
  43. Teig, N., Scherer, R., & Nilsen, T. (2018). More isn't always better: The curvilinear relationship between inquiry-based teaching and student achievement in science. Learning and Instruction, 56, 20–29. https://doi.org/10.1016/j.learninstruc.2018.02.006
  44. Thaiposri, P., & Wannapiroon, P. (2015). Enhancing students' critical thinking skills through teaching and learning by inquiry-based learning activities using social network and cloud computing. Procedia-Social and Behavioral Sciences, 174, 2137-2144.
  45. UNESCO. (2024). AI competency framework for students. United Nations Educational, Scientific and Cultural Organization. https://doi.org/10.54675/JKJB9835
  46. Ünal, A. (2018). Araştırma-sorgulamaya dayalı ve sosyal ağ destekli kimya laboratuvarı etkinliklerinin fen bilimleri öğretmen adaylarının algı, tutum ve başarıları üzerine etkisi [Doktora tezi, Kastamonu Üniversitesi].
  47. Ünal, Ş. (2015). Biyoloji dersi çevre konularının öğretiminde yaşam temelli yaklaşıma dair örnek olay inceleme ve araştırma sorgulama temelli bilim öğrenme öğretim yöntemlerinin etkisi [Doktora tezi, Atatürk Üniversitesi].
  48. 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
  49. Varlu, B., & Sağır, Ş. U. (2019). Araştırma sorgulamaya dayalı öğretimin ortaokul öğrencilerinin fen başarısı, sorgulama algısı ve üstbiliş farkındalığına etkisi. GUJGEF, 39(2), 703–725. https://doi.org/10.17152/gefad.407417
  50. Villardón-Gallego, L. (2016). Inquiry-based learning in pre-service training for secondary education counselors. Procedia - Social and Behavioral Sciences, 217, 65–73. https://doi.org/10.1016/j.sbspro.2016.02.028
  51. Yaşar, Ş., & Duban, N. (2009). Students' opinions regarding to the inquiry-based learning approach. Elementary Education Online, 8(2), 457–475. https://ilkogretim-online.org/index.php/pub/article/view/2469
  52. Yerlikaya, A. (2019). 7. sınıf vücudumuzdaki sistemler ünitesinin araştırma sorgulama temelli öğreniminin programdaki öğrenme alanlarına etkisi [Doktora tezi, On Dokuz Mayıs Üniversitesi].
  53. Yetişir, M. I. (2016). Rehberli araştırma-sorgulamaya dayalı fizik öğretimi: Öğretmen adaylarının akademik başarıları ve uygulama hakkındaki görüşlerinin incelenmesi. Ankara University Journal of Faculty of Educational Sciences, 49(1), 159–182. https://doi.org/10.1501/Egifak_0000001379
  54. Yıldırım, E., & Yıldırım, Ö. (2020). Investigation of lesson planning competencies of primary and secondary school teachers. Milli Eğitim, 49(228), 7–37.
  55. Zawacki-Richter, O., Marín, V. I., Bond, M., & Gouverneur, F. (2019). Systematic review of research on artificial intelligence applications in higher education – where are the educators? International Journal of Educational Technology in Higher Education, 16(1). https://doi.org/10.1186/s41239-019-0171-0