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Implications of AutoGPT on Feedback in English Language Pedagogy: A Qualitative Inquiry into Teachers' Perspectives

Year 2024, Volume: 6 Issue: 2, 212 - 229, 30.09.2024
https://doi.org/10.38151/akef.2024.139

Abstract

Technological advancements in education offer innovative tools that significantly impact the teaching and learning processes. Among these innovations, artificial intelligence (AI)-supported tools such as AutoGPT promise revolutionary changes in the field of English Language Teaching (ELT). This study aims to investigate the integration of AutoGPT into feedback processes in ELT at a private school in Konya. The research seeks to explore the effects of AutoGPT on feedback mechanisms in ELT and to examine teachers’ perceptions and experiences regarding the use of this AI tool. Conducted from a basic qualitative research design, this study involved semi-structured interviews with English teachers who had at least two years of teaching experience and had used AutoGPT for feedback purposes. The interviews aim to uncover teachers' views on the effectiveness of AutoGPT and the challenges encountered. The data were analyzed using thematic analysis with MAXQDA 24 software, identifying key themes related to the advantages, limitations, and practical applications of AutoGPT in ELT. The findings reveal that teachers consider AutoGPT a valuable tool for providing quick and comprehensive feedback on student writing. It was highlighted that AutoGPT effectively addresses students' difficulties in understanding concepts, alleviates teachers' workload, and offers objective evaluations to save time. However, concerns about the excessive use of technology potentially reducing students' sense of responsibility were also expressed. This study indicates that experienced teachers are necessary for the effective use of AutoGPT in ELT, and in this context, the development of comprehensive AI training programs for teachers is proposed.

References

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  • Ayan, A. D., & Erdemir, N. (2023). EFL teachers' perceptions of automated written corrective feedback and Grammarly. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi (AKEF), 5(3), 1183-1198. https://doi.org/10.38151/akef.2023.106
  • Ayaz, B., Ramazanoğlu, M., & Uluyol, Ç. (2023). Identification of the impact of differentiated digitally supported learning environments. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi (AKEF), 5(3), 853-867. https://doi.org/10.38151/akef.2023.88
  • Bitchener, J., & Ferris, D. (2012). Automatic feedback for language learners: What teachers need to know. TESOL Quarterly, 46(4), 830-849. https://doi.org/10.1002/tesq.46
  • Bolibekova, M. M. (2023). Use of modern pedagogical technologies in teaching English. International Journal of Literature and Languages, 3(5), 147-150. https://doi.org/10.37547/ijll/Volume03Issue05-29
  • Brown, J. (2018). Artificial intelligence in language assessment: Implications for English language teaching and research. Language Assessment Quarterly, 15(4), 401-421. https://doi.org/10.1080/15434303.2018.1509010
  • Brown, M., Smith, J., & Jones, L. (2020). The impact of AI on modern education. Journal of Educational Technology, 15(3), 235-250. https://doi.org/10.1016/j.jedt.2020.03.004
  • Bruguera, C., Guitert, M., & Romeu, T. (2022). Social media in the learning ecologies of communications students: Identifying profiles from students’ perspective. Education and Information Technologies, 27(9), 13113–13129. https://doi.org/10.1007/s10639-022-11021-3
  • Chiu, M., Li, Q., & Wu, X. (2023). Impact of artificial intelligence in students’ learning life. Education and Information Technologies, 28(3), 2507-2529. https://doi.org/10.1007/s10639-023-11019-8
  • Clark, R. E. (2001). Emerging technologies for learning and performance. Educational Technology Research and Development, 49(1), 53-74. https://doi.org/10.1007/BF02504986
  • Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Thousand Oaks, CA: Sage.
  • Dai, W., Lin, J., Jin, F., Li, T., Tsai, Y., Gasevic, D., & Chen, G. (2023). Can large language models provide feedback to students? A case study on ChatGPT. OSF Preprints. https://doi.org/10.35542/osf.io/hcgzj
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  • Demszky, D., & Liu, J. (2023). M-powering teachers: Natural language processing powered feedback improves 1:1 instruction and student outcomes. In Proceedings of the Tenth ACM Conference on Learning @ Scale (pp. 59-69). https://doi.org/10.1145/3573051.3593379
  • Ertmer, P. A. (1999). Addressing first-order and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47-61. https://doi.org/10.1007/BF02299597
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  • Jeon, J., & Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Education and Information Technologies, 28(12), 15873-15892. https://doi.org/10.1007/s10639-023-11019-8
  • Johnson, R., Thompson, L., & White, A. (2022). AI in education: Opportunities and challenges. Educational Review, 74(5), 742-761. https://doi.org/10.1080/00131911.2022.2059867
  • Gao, X. (2015). The impact of automated feedback on ESL writing apprehension. Journal of Technology and Chinese Language Teaching, 6(1), 22-35. https://doi.org/10.1142/S2302002215500039
  • Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95-105. https://doi.org/10.1016/j.iheduc.2004.02.001
  • Hasanein, A. M., & Sobaih, A. E. E. (2023). Drivers and consequences of ChatGPT use in higher education: Key stakeholder perspectives. European Journal of Investigation in Health, Psychology and Education, 13(11), 2599-2614. https://doi.org/10.3390/ejihpe13110181
  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://doi.org/10.1007/978-3-030-29965-7
  • Hubbard, P. (2008). Innovations in language learning technologies: Implications for language teacher education. Language Learning & Technology, 12(1), 3-8. https://doi.org/10.1016/j.linged.2008.07.003
  • Huy, A., Nguyen, X., Hou, X., & McLaren, B. M. (2023). Evaluating ChatGPT's decimal skills and feedback generation in a digital learning game. https://doi.org/10.48550/arXiv.2306.16639
  • Kebritchi, M., Lipschuetz, A., & Santiague, L. (2017). Issues and challenges for teaching successful online courses in higher education. Journal of Educational Technology Systems, 46(1), 4–29. https://doi.org/10.1177/0047239516661713
  • Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
  • Li, L., & Zhao, J. (2019). The role of artificial intelligence in language learning and teaching. Journal of Language Teaching and Research, 10(5), 1084-1091. https://doi.org/10.17507/jltr.1005.13
  • Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779
  • Lim, L., et al. (2023). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 20(1), 1-21. https://doi.org/10.1186/s41239-023-00394-7
  • Liu, H., Ma, W., Wu, T., & Xin, C. (2022). The study of feedback in writing from college English teachers and artificial intelligence platform based on mixed method teaching. Pacific International Journal, 5(4), 147–154. https://doi.org/10.52206/PIJ5802-050465
  • Merriam, S. B. (2013). Qualitative research: A guide to design and implementation. San Francisco: Jossey-Bass.
  • Mertens, D. M. (2014). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. SAGE Publications.
  • Mirici, H., & Sari, Ş. (2021). An investigation of interaction among willingness to communicate, academic achievement, and L2- self guides. International Online Journal of Education and Teaching (IOJET), 8(2), 653-661. https://doi.org/10.18298/ijlet.7022
  • Mirici, İ. H., & Sarı, Ş. (2021). Turkish EFL instructors’ feared selves while speaking English in different contexts. Journal of Language and Linguistic Studies, 17(Special Issue 2), 994-1011. https://doi.org/10.17263/jlls.927686
  • 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/s10639-023-11010-5
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  • Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). SAGE Publications.
  • Piaget, J. (1970). Piaget's theory. In P. Mussen (Ed.), Carmichael's manual of child psychology (pp. 703-732). Wiley. https://doi.org/10.1002/9780470147658.chpsy0416
  • Reeves, T. C., & Hedberg, J. G. (2003). Interactive learning systems evaluation. Educational Technology Publications. https://doi.org/10.1007/978-1-4419-1518-3_13
  • Sakai, N. (2023). Investigating the feasibility of ChatGPT for personalized English language learning: A case study on its applicability to Japanese students. https://doi.org/10.31219/osf.io/cv9f2
  • Sandholtz, J. H., Ringstaff, C., & Dwyer, D. C. (1997). Teaching with technology: Creating student-centered classrooms. Teachers College Press. https://doi.org/10.1080/10509685.1997.10403174
  • Sarı, Ş., & Mirici, İ. H. (2021). An investigation of non-native EFL instructors’ behavioral, emotional, and speech disorders. International Journal of Curriculum and Instruction, 13(3), 2888-2901. https://doi.org/10.37681/ijci.2021.13.3.7
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Social Science Computer Review, 37(4), 628-644. https://doi.org/10.1177/0894439319865793
  • Smith, R., & Anderson, J. (2021). The impact of AI on student engagement and learning outcomes. Computers & Education, 164, 104096. https://doi.org/10.1016/j.compedu.2021.104096
  • Squires, V. (2023). Thematic analysis. In Springer texts in education (pp. 463-468). Springer. https://doi.org/10.1007/978-3-031-04394-9_72
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AutoGPT'nin İngiliz Dili Pedagojisinde Geribildirim Üzerindeki Etkileri: Öğretmen Algıları Üzerine Nitel Bir Araştırma

Year 2024, Volume: 6 Issue: 2, 212 - 229, 30.09.2024
https://doi.org/10.38151/akef.2024.139

Abstract

Eğitimdeki teknolojik gelişmeler, öğretim ve öğrenme süreçlerini önemli ölçüde etkileyen yenilikçi araçlar sunmaktadır. Bu yenilikler arasında yer alan AutoGPT gibi yapay zekâ destekli araçlar, İngilizce Dil Eğitimi (ELT) alanında devrim niteliğinde değişiklikler vaat etmektedir. Bu çalışma, Konya ilinde bulunan özel bir okulda AutoGPT’nin İngilizce dil öğretiminde geri bildirim süreçlerine entegrasyonunu incelemeyi amaçlamaktadır. Araştırmada, AutoGPT’nin ELT’deki geri bildirim mekanizmaları üzerindeki etkileri ve öğretmenlerin bu yapay zekâ aracını kullanma konusundaki algılarının ve tecrübelerinin keşfedilmesini Eğitimdeki teknolojik gelişmeler, öğretim ve öğrenme süreçlerini önemli ölçüde etkileyen yenilikçi araçlar sunmaktadır. Bu yenilikler arasında yer alan AutoGPT gibi yapay zekâ destekli araçlar, İngilizce Dil Eğitimi (ELT) alanında devrim niteliğinde değişiklikler vaat etmektedir. Bu çalışma, Konya ilinde bulunan özel bir okulda AutoGPT’nin İngilizce dil öğretiminde geri bildirim süreçlerine entegrasyonunu incelemeyi amaçlamaktadır. Araştırmada, AutoGPT’nin ELT’deki geri bildirim mekanizmaları üzerindeki etkileri ve öğretmenlerin bu yapay zekâ aracını kullanma konusundaki algılarının ve tecrübelerinin keşfedilmesini hedeflemektedir. Temel nitel araştırma deseni ile yürütülen bu çalışmada, en az iki yıllık öğretim deneyimi olan ve AutoGPT’yi geri bildirim amacıyla kullanmış İngilizce öğretmenleri ile yarı yapılandırılmış görüşmeler yapılmıştır. Görüşmeler, AutoGPT’nin etkinliği ve karşılaşılan zorluklar konusundaki öğretmen görüşlerini ayrıntılı olarak ortaya çıkarmayı amaçlamaktadır. Veriler, MAXQDA 24 yazılımı kullanılarak tematik analiz yöntemiyle analiz edilmiş ve AutoGPT’nin ELT’deki avantajları, sınırlamaları ve pratik uygulamalarıyla ilgili temel temalar belirlenmiştir. Bulgular, öğretmenlerin AutoGPT’yi özellikle öğrenci yazılarını değerlendirme konusunda hızlı ve kapsamlı geri bildirim sağlama aracı olarak değerli bulduklarını ortaya koymuştur. AutoGPT’nin, öğrencilerin kavramları anlamakta yaşadıkları zorlukları gidermede etkili bir geri bildirim sağladığı, ayrıca öğretmenlerin iş yükünü hafiflettiği ve objektif değerlendirmeler sunarak zaman kazandırdığı vurgulanmıştır. Bununla birlikte, teknolojinin aşırı kullanımının öğrencilerin sorumluluklarını azaltabileceği yönündeki endişeler de dile getirilmiştir. Bu çalışmada, AutoGPT’nin ELT’de etkili bir şekilde kullanılabilmesi için deneyimli öğretmenlerin gerekli olduğu belirtilmiştir; bu bağlamda eğitimciler için kapsamlı yapay zekâ eğitim programlarının geliştirilmesi önerilmektedir.

References

  • Anderson, T. (2016). Effective feedback strategies in the online learning environment. The American Journal of Distance Education, 30(2), 103-117. https://doi.org/10.1080/08923647.2016.1153290
  • Ayan, A. D., & Erdemir, N. (2023). EFL teachers' perceptions of automated written corrective feedback and Grammarly. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi (AKEF), 5(3), 1183-1198. https://doi.org/10.38151/akef.2023.106
  • Ayaz, B., Ramazanoğlu, M., & Uluyol, Ç. (2023). Identification of the impact of differentiated digitally supported learning environments. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi (AKEF), 5(3), 853-867. https://doi.org/10.38151/akef.2023.88
  • Bitchener, J., & Ferris, D. (2012). Automatic feedback for language learners: What teachers need to know. TESOL Quarterly, 46(4), 830-849. https://doi.org/10.1002/tesq.46
  • Bolibekova, M. M. (2023). Use of modern pedagogical technologies in teaching English. International Journal of Literature and Languages, 3(5), 147-150. https://doi.org/10.37547/ijll/Volume03Issue05-29
  • Brown, J. (2018). Artificial intelligence in language assessment: Implications for English language teaching and research. Language Assessment Quarterly, 15(4), 401-421. https://doi.org/10.1080/15434303.2018.1509010
  • Brown, M., Smith, J., & Jones, L. (2020). The impact of AI on modern education. Journal of Educational Technology, 15(3), 235-250. https://doi.org/10.1016/j.jedt.2020.03.004
  • Bruguera, C., Guitert, M., & Romeu, T. (2022). Social media in the learning ecologies of communications students: Identifying profiles from students’ perspective. Education and Information Technologies, 27(9), 13113–13129. https://doi.org/10.1007/s10639-022-11021-3
  • Chiu, M., Li, Q., & Wu, X. (2023). Impact of artificial intelligence in students’ learning life. Education and Information Technologies, 28(3), 2507-2529. https://doi.org/10.1007/s10639-023-11019-8
  • Clark, R. E. (2001). Emerging technologies for learning and performance. Educational Technology Research and Development, 49(1), 53-74. https://doi.org/10.1007/BF02504986
  • Creswell, J. W. (2014). Research design: Qualitative, quantitative, and mixed methods approaches (4th ed.). Thousand Oaks, CA: Sage.
  • Dai, W., Lin, J., Jin, F., Li, T., Tsai, Y., Gasevic, D., & Chen, G. (2023). Can large language models provide feedback to students? A case study on ChatGPT. OSF Preprints. https://doi.org/10.35542/osf.io/hcgzj
  • Dan, Y., et al. (2023). ChatGPT has entered the classroom: How LLMs could transform education. Nature. https://www.nature.com/articles/s41586-023-04529-9
  • Demszky, D., & Liu, J. (2023). M-powering teachers: Natural language processing powered feedback improves 1:1 instruction and student outcomes. In Proceedings of the Tenth ACM Conference on Learning @ Scale (pp. 59-69). https://doi.org/10.1145/3573051.3593379
  • Ertmer, P. A. (1999). Addressing first-order and second-order barriers to change: Strategies for technology integration. Educational Technology Research and Development, 47(4), 47-61. https://doi.org/10.1007/BF02299597
  • Ertmer, P. A., & Ottenbreit-Leftwich, A. T. (2010). Teacher technology change: How knowledge, beliefs, and culture shape technology integration. Journal of Technology and Teacher Education, 18(3), 321-340. https://doi.org/10.1080/10566201003784606
  • Jeon, J., & Lee, S. (2023). Large language models in education: A focus on the complementary relationship between human teachers and ChatGPT. Education and Information Technologies, 28(12), 15873-15892. https://doi.org/10.1007/s10639-023-11019-8
  • Johnson, R., Thompson, L., & White, A. (2022). AI in education: Opportunities and challenges. Educational Review, 74(5), 742-761. https://doi.org/10.1080/00131911.2022.2059867
  • Gao, X. (2015). The impact of automated feedback on ESL writing apprehension. Journal of Technology and Chinese Language Teaching, 6(1), 22-35. https://doi.org/10.1142/S2302002215500039
  • Garrison, D. R., & Kanuka, H. (2004). Blended learning: Uncovering its transformative potential in higher education. The Internet and Higher Education, 7(2), 95-105. https://doi.org/10.1016/j.iheduc.2004.02.001
  • Hasanein, A. M., & Sobaih, A. E. E. (2023). Drivers and consequences of ChatGPT use in higher education: Key stakeholder perspectives. European Journal of Investigation in Health, Psychology and Education, 13(11), 2599-2614. https://doi.org/10.3390/ejihpe13110181
  • Hattie, J., & Timperley, H. (2007). The power of feedback. Review of Educational Research, 77(1), 81-112. https://doi.org/10.3102/003465430298487
  • Holmes, W., Bialik, M., & Fadel, C. (2019). Artificial intelligence in education: Promises and implications for teaching and learning. Center for Curriculum Redesign. https://doi.org/10.1007/978-3-030-29965-7
  • Hubbard, P. (2008). Innovations in language learning technologies: Implications for language teacher education. Language Learning & Technology, 12(1), 3-8. https://doi.org/10.1016/j.linged.2008.07.003
  • Huy, A., Nguyen, X., Hou, X., & McLaren, B. M. (2023). Evaluating ChatGPT's decimal skills and feedback generation in a digital learning game. https://doi.org/10.48550/arXiv.2306.16639
  • Kebritchi, M., Lipschuetz, A., & Santiague, L. (2017). Issues and challenges for teaching successful online courses in higher education. Journal of Educational Technology Systems, 46(1), 4–29. https://doi.org/10.1177/0047239516661713
  • Kohnke, L., Moorhouse, B. L., & Zou, D. (2023). ChatGPT for language teaching and learning. RELC Journal, 54(2), 537–550. https://doi.org/10.1177/00336882231162868
  • Li, L., & Zhao, J. (2019). The role of artificial intelligence in language learning and teaching. Journal of Language Teaching and Research, 10(5), 1084-1091. https://doi.org/10.17507/jltr.1005.13
  • Liang, W., Yuksekgonul, M., Mao, Y., Wu, E., & Zou, J. (2023). GPT detectors are biased against non-native English writers. Patterns, 4(7), 100779. https://doi.org/10.1016/j.patter.2023.100779
  • Lim, L., et al. (2023). Generative AI and the future of higher education: A threat to academic integrity or reformation? Evidence from multicultural perspectives. International Journal of Educational Technology in Higher Education, 20(1), 1-21. https://doi.org/10.1186/s41239-023-00394-7
  • Liu, H., Ma, W., Wu, T., & Xin, C. (2022). The study of feedback in writing from college English teachers and artificial intelligence platform based on mixed method teaching. Pacific International Journal, 5(4), 147–154. https://doi.org/10.52206/PIJ5802-050465
  • Merriam, S. B. (2013). Qualitative research: A guide to design and implementation. San Francisco: Jossey-Bass.
  • Mertens, D. M. (2014). Research and evaluation in education and psychology: Integrating diversity with quantitative, qualitative, and mixed methods. SAGE Publications.
  • Mirici, H., & Sari, Ş. (2021). An investigation of interaction among willingness to communicate, academic achievement, and L2- self guides. International Online Journal of Education and Teaching (IOJET), 8(2), 653-661. https://doi.org/10.18298/ijlet.7022
  • Mirici, İ. H., & Sarı, Ş. (2021). Turkish EFL instructors’ feared selves while speaking English in different contexts. Journal of Language and Linguistic Studies, 17(Special Issue 2), 994-1011. https://doi.org/10.17263/jlls.927686
  • 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/s10639-023-11010-5
  • Pankiewicz, M., & Baker, R. S. (2023). Large language models (GPT) for automating feedback on programming assignments. In Proceedings of the International Conference on Artificial Intelligence in Education (pp. 301-310). Springer. https://doi.org/10.1007/978-3-031-08156-9_34
  • Patton, M. Q. (2015). Qualitative research & evaluation methods: Integrating theory and practice (4th ed.). SAGE Publications.
  • Piaget, J. (1970). Piaget's theory. In P. Mussen (Ed.), Carmichael's manual of child psychology (pp. 703-732). Wiley. https://doi.org/10.1002/9780470147658.chpsy0416
  • Reeves, T. C., & Hedberg, J. G. (2003). Interactive learning systems evaluation. Educational Technology Publications. https://doi.org/10.1007/978-1-4419-1518-3_13
  • Sakai, N. (2023). Investigating the feasibility of ChatGPT for personalized English language learning: A case study on its applicability to Japanese students. https://doi.org/10.31219/osf.io/cv9f2
  • Sandholtz, J. H., Ringstaff, C., & Dwyer, D. C. (1997). Teaching with technology: Creating student-centered classrooms. Teachers College Press. https://doi.org/10.1080/10509685.1997.10403174
  • Sarı, Ş., & Mirici, İ. H. (2021). An investigation of non-native EFL instructors’ behavioral, emotional, and speech disorders. International Journal of Curriculum and Instruction, 13(3), 2888-2901. https://doi.org/10.37681/ijci.2021.13.3.7
  • Selwyn, N. (2019). Should robots replace teachers? AI and the future of education. Social Science Computer Review, 37(4), 628-644. https://doi.org/10.1177/0894439319865793
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There are 53 citations in total.

Details

Primary Language English
Subjects Instructional Technologies, Teacher Education and Professional Development of Educators
Journal Section Articles
Authors

Erdem Demirbek 0009-0009-9955-2326

Feyza Nur Ekizer 0000-0003-0568-5355

Early Pub Date September 29, 2024
Publication Date September 30, 2024
Submission Date April 2, 2024
Acceptance Date August 21, 2024
Published in Issue Year 2024 Volume: 6 Issue: 2

Cite

APA Demirbek, E., & Ekizer, F. N. (2024). Implications of AutoGPT on Feedback in English Language Pedagogy: A Qualitative Inquiry into Teachers’ Perspectives. Ahmet Keleşoğlu Eğitim Fakültesi Dergisi, 6(2), 212-229. https://doi.org/10.38151/akef.2024.139

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