Percepciones de los profesores de lenguas extranjeras sobre el uso de una aplicación de IA generativa para el diseño de evaluaciones de lectura en el aula
Barra lateral del artículo
Contenido principal del artículo
La inteligencia artificial (IA) se ha convertido en una parte esencial de la evaluación en lenguas extranjeras. Las herramientas de inteligencia artificial se utilizan para la generación, calificación y retroalimentación automatizadas de ítems, mejorando el desarrollo, la administración, y la interpretación de evaluaciones en lenguas extranjeras a gran escala. Este estudio examina el uso de una herramienta de inteligencia artificial, ChatGPT, en la evaluación en lenguas y propone una forma para que los maestros utilicen la herramienta para simplificar la complejidad del lenguaje de los textos escritos (es decir, pasajes de lectura) y generar preguntas de comprensión para estudiantes de nivel básico de inglés (A1-A2). Siete profesores de inglés como lengua extranjera, que actualmente imparten clases de inglés como lengua extranjera de nivel básico, participaron en entrevistas individuales para discutir sus percepciones sobre ChatGPT y evaluar la calidad y adecuación del texto simplificado y de las preguntas. El estudio ilustra cómo se utilizó ChatGPT para generar el contenido de la evaluación y presenta las percepciones de los profesores, lo cual devela implicaciones del uso de herramientas de IA generativa para el diseño de evaluaciones de lectura en el aula.
Al Braiki, B., Harous, S., Zaki, N., & Alnajjar, F. (2020). Artificial intelligence in education and assessment methods. Bulletin of Electrical Engineering and Informatics, 9(5), 1998–2007. https://doi.org/10.11591/eei.v9i5.1984 DOI: https://doi.org/10.11591/eei.v9i5.1984
Attali, Y., & Burstein, J. (2006). Automated essay scoring with e-rater® V.2. Journal of Technology, Learning, and Assessment, 4(3). https://ejournals.bc.edu/index.php/jtla/article/view/1650
Attali, Y., Runge, A., LaFlair, G. T., Yancey, K., Goodwin, S., Park, Y., & Von Davier, A. A. (2022). The interactive reading task: Transformer-based automatic item generation. Frontiers in Artificial Intelligence, 5, e903077. https://doi.org/10.3389/frai.2022.903077 DOI: https://doi.org/10.3389/frai.2022.903077
Azzam, A. & Charles, T. (2024). A review of artificial intelligence in K-12 education. Open Journal of Applied Sciences, 14, 2088-2100. https://doi.org/10.4236/ojapps.2024.148137 DOI: https://doi.org/10.4236/ojapps.2024.148137
Belzak, W. C. M., Naismith, B., & Burstein, J. (2023). Ensuring fairness of human- and AI-generated test items. In N. Wang, G. Rebolledo-Mendez, V. Dimitrova, N. Matsuda, & O. C. Santos (Eds.), Artificial intelligence in education. Communications in computer and information science (Vol. 1831). Springer. https://doi.org/10.1007/978-3-031-36336-8_108 DOI: https://doi.org/10.1007/978-3-031-36336-8_108
Bengesi, S., El-Sayed, H., Sarker, M. K., Houkpati, Y., Irungu, J., & Oladunni, T. (2024). Advancements in generative AI: A comprehensive review of GANs, GPT, autoencoders, diffusion model, and transformers. IEEE Access, 12, 69812–69837. https://doi.org/10.1109/ACCESS.2024.3397775 DOI: https://doi.org/10.1109/ACCESS.2024.3397775
Bozkurt, A. (2024). Tell me your prompts and I will make them true: The alchemy of prompt engineering and generative AI. Open Praxis, 16(2), 111–118. https://doi.org/10.55982/openpraxis.16.2.661 DOI: https://doi.org/10.55982/openpraxis.16.2.661
Bunch, G. C., Walqui, A., & Pearson, P. D. (2014). Complex text and new common standards in the United States: Pedagogical implications for English learners. TESOL Quarterly, 48(3), 533–559. https://doi.org/10.1002/tesq.175 DOI: https://doi.org/10.1002/tesq.175
Celik, I., Dindar, M., Muukkonen, H., Järvelä, S. (2022). The promises and challenges of artificial intelligence for teachers: A systematic review of research. TechTrends, 66, 616–630. https://doi.org/10.1007/s11528-022-00715-y DOI: https://doi.org/10.1007/s11528-022-00715-y
Chapelle, C. A., & Chung, Y. R. (2010). The promise of NLP and speech processing technologies in language assessment. Language Testing, 27(3), 301–315. https://doi.org/10.1177/0265532210364405 DOI: https://doi.org/10.1177/0265532210364405
Chattopadhyay, S., Shankar, S., Gangadhar, R. B., & Kasinathan, K. (2018). Applications of artificial intelligence in assessment for learning in schools. In J. Keengwe (Ed.), Handbook of research on digital content, mobile learning, and technology integration models in teacher education (pp. 185–206). IGI Global. https://doi.org/10.4018/978-1-5225-2953-8.ch010 DOI: https://doi.org/10.4018/978-1-5225-2953-8.ch010
Chaudhry, M. A., & Kazim, E. (2022). Artificial Intelligence in Education (AIEd): A high-level academic and industry note 2021. AI Ethics 2, 157–165. https://doi.org/10.1007/s43681-021-00074-z DOI: https://doi.org/10.1007/s43681-021-00074-z
Chen, L., Zechner, K., Yoon, S.-Y., Evanini, K., Wang, X., Loukina, A., Tao, J., Davis, L., Lee, C. M., Ma, M., Mundkowsky, R., Lu, C., Leong, C. W., & Gyawali, B. (2018). Automated scoring of nonnative speech using the SpeechRaterSM v. 5.0 engine. ETS Research Report, 18(10), 1–31. https://doi.org/10.1002/ets2.12198 DOI: https://doi.org/10.1002/ets2.12198
Choi, I., Hao, J., Deane, P., & Zhang, M. (2021). Benchmark keystroke biometrics accuracy from high-stakes writing tasks. ETS Research Report, 21(15), 1-13. https://doi.org/10.1002/ets2.12326 DOI: https://doi.org/10.1002/ets2.12326
Chukharev-Hudilainen, E., & Ockey, G. J. (2021). The development and evaluation of Interactional Competence Elicitor for oral language assessments. ETS Research Report, 21(6). https://doi.org/10.1002/ets2.12319 DOI: https://doi.org/10.1002/ets2.12319
Clarisó R., & Cabot, J. (2023). Model-driven prompt engineering. In ACM/IEEE 26th International Conference on Model Driven Engineering Languages and Systems Proceedings (pp. 47–54). MODELS. https://doi.org/10.1109/MODELS58315.2023.00020 DOI: https://doi.org/10.1109/MODELS58315.2023.00020
Cote Parra, G., & López, A. A. (2024). Examining the assessment practices of foreign language novice teachers. Profile: Issues in Teachers’ Professional Development, 26(1). 97–113. https://doi.org/10.15446/profile.v26n1.106384 DOI: https://doi.org/10.15446/profile.v26n1.106384
Cotos, E. (2023). Automated feedback on writing. In O. Kruse, C. Rapp, C. M. Anson, K. Benetos, E. Cotos, A. Devitt, & A. Shibani (Eds.), Digital writing technologies in higher education: Theory, research, and practice (pp. 347–364). Springer. https://doi.org/10.1007/978-3-031-36033-6_22 DOI: https://doi.org/10.1007/978-3-031-36033-6_22
Crossley, S. A., Allen, D., & McNamara, D. S. (2012). Text simplification and comprehensible input: A case for an intuitive approach. Language Teaching Research, 16(1), 89–108. https://doi.org/10.1177/1362168811423456 DOI: https://doi.org/10.1177/1362168811423456
Crossley, S. A., Yang, H. S., & McNamara, D. S. (2014). What's so simple about simplified texts? A computational and psycholinguistic investigation of text comprehension and text processing. Reading in a Foreign Language, 26(1), 92–113. http://hdl.handle.net/10125/66686
DiCerbo, K. (2021). Why not go all-in with artificial intelligence? In R. A. Sottilare, & J. Schwarz (Eds.), Adaptive instructional systems: Design and evaluation (pp. 361–369). International Conference on Human-Computer Interaction. Springer. https://doi.org/10.1007/978-3-030-77857-6_25 DOI: https://doi.org/10.1007/978-3-030-77857-6_25
Dunkel, P. A. (1999). Considerations in developing or using second/foreign language proficiency computer-adaptive tests. Language Learning & Technology, 2(2), 77–93. http://dx.doi.org/10125/25044
Ebe, A. E. (2012). Supporting the reading development of middle school English language learners through culturally relevant texts. Reading & Writing Quarterly, 28(2), 179–198. https://doi.org/10.1080/10573569.2012.651078 DOI: https://doi.org/10.1080/10573569.2012.651078
Erbaggio, P., Gopalakrishnan, S., Hobbs, S., & Liu, H. (2012). Enhancing student engagement through online authentic materials. The International Association for Language Learning Technology, 42(2), 27–51. https://doi.org/10.17161/iallt.v42i2.8511 DOI: https://doi.org/10.17161/iallt.v42i2.8511
Feuerriegel, S., Hartmann, J., Janiesch, C., & Zschech, P. (2024). Generative AI. Business & Information Systems Engineering, 66, 111–126. https://doi.org/10.1007/s12599-023-00834-7 DOI: https://doi.org/10.1007/s12599-023-00834-7
Foltz, P., Streeter, L., Lochbaum, K., & Landauer, T. (2013). Implementation and applications of the intelligent essay assessor. In M. D. Shermis, & J. Burstein (Eds.), Handbook of automated essay evaluation, current applications and new directions (pp. 68–88). Routledge.
García-Peñalvo, F. J., & Vázquez-Ingelmo, A. (2023). What do we mean by GenAI? A systematic mapping of the evolution, trends, and techniques involved in generative AI. International Journal of Interactive Multimedia and Artificial Intelligence, 8(4), 7–16. https://doi.org/10.9781/ijimai.2023.07.006 DOI: https://doi.org/10.9781/ijimai.2023.07.006
Gardner, J., & Yuan, L. (2021). Artificial intelligence in educational assessment: ‘Breakthrough? Or buncombe and ballyhoo?’. Journal of Computer Assisted Learning, 37(5), 1207–1216. https://doi.org/10.1111/jcal.12577 DOI: https://doi.org/10.1111/jcal.12577
González-Calatayud, V., Prendes-Espinosa, P., & Roig-Vila, R. (2021). Artificial intelligence for student assessment: A systematic review. Applied Sciences, 11(12), 5467. https://doi.org/10.3390/app11125467 DOI: https://doi.org/10.3390/app11125467
Grabe, W. (2009). Reading in a second language. Cambridge University Press. DOI: https://doi.org/10.1093/oxfordhb/9780195384253.013.0006
Hoffman, J. V., & Schallert, D. L. (Eds.). (2004). The texts in elementary classrooms. Routledge. https://doi.org/10.4324/9781410611086 DOI: https://doi.org/10.4324/9781410611086
Hopfenbeck, T. N., Zhang, Z., Sun, S. Z., Robertson, P., & McGrane, J. A. (2023). Challenges and opportunities for classroom-based formative assessment and AI: A perspective article. Frontiers in Education, 8, e1270700. https://doi.org/10.3389/feduc.2023.1270700 DOI: https://doi.org/10.3389/feduc.2023.1270700
Kamalov, F., Santandreu Calonge, D., & Gurrib, I. (2023). New era of artificial intelligence in education: Towards a sustainable multifaceted revolution. Sustainability, 15(16), 12451. https://doi.org/10.3390/su151612451 DOI: https://doi.org/10.3390/su151612451
Kar, S., Roy, C., Das, M., Mullick, S., & Saha, R. (2023). AI horizons: Unveiling the future of generative intelligence. International Journal of Advanced Research in Science, Communication and Technology (IJARSCT), 3(1), 387–391. https://doi.org/10.48175/ijarsct-12969 DOI: https://doi.org/10.48175/IJARSCT-12969
Kjell, O., Giorgi, S., & Schwartz, H. A. (2023). The text-package: An R-package for analyzing and visualizing human language using natural language processing and transformers. Psychological Methods, 28(6), 1478–1498. https://doi.org/10.1037/met0000542 DOI: https://doi.org/10.1037/met0000542
Mena Octavio, M., González Argüello, M. V., & Pujolà, J.-T. (2024). ChatGPT as an AI L2 teaching support: A case study of an EFL teacher. Technology in Language Teaching & Learning, 6(1), 1142. https://doi.org/10.29140/tltl.v6n1.1142 DOI: https://doi.org/10.29140/tltl.v6n1.1142
Mislevy, R., Chapelle, C. A., Chung, Y.‐R., Xu, J. (2008). Options for adaptivity in computer‐assisted language learning and assessment. In C. A. Chapelle, Y.‐R. Chung, & J. Xu (Eds.), Towards adaptive CALL: Natural language processing for diagnostic language assessment (pp. 9‐24). Iowa State University.
Morandín-Ahuerna, F. (2022). What is artificial intelligence? International Journal of Research Publication and Reviews, 3(12), 1947–1951. https://doi.org/10.55248/gengpi.2022.31261 DOI: https://doi.org/10.55248/gengpi.2022.31261
Moxon, S. (2021). Exploring the effects of automated pronunciation evaluation on L2 students in Thailand. IAFOR Journal of Education: Language Learning in Education, 9(3). https://doi.org/10.22492/ije.9.3.03 DOI: https://doi.org/10.22492/ije.9.3.03
O’Sullivan, B. (2023). Reflections on the application and validation of technology in language testing. Language Assessment Quarterly, 20(4-5), 501–511. https://doi.org/10.1080/15434303.2023.2291486 DOI: https://doi.org/10.1080/15434303.2023.2291486
Owan, V. J., Abang, K. B., Idika, D. O., Etta, E. O., & Bassey, B. A. (2023). Exploring the potential of artificial intelligence tools in educational measurement and assessment. Eurasia Journal of Mathematics, Science and Technology Education, 19(8), em2307. https://doi.org/10.29333/ejmste/13428 DOI: https://doi.org/10.29333/ejmste/13428
Petersen, S. E., & Ostendorf, M. (2007, October) Text simplification for language learners: A corpus analysis [Conference paper]. Speech and Language Technology in Education (SLaTE 2007), Farmington, Pennsylvania USA. https://doi.org/10.21437/SLaTE.2007-20 DOI: https://doi.org/10.21437/SLaTE.2007-20
Purpura, J. E., Davoodifard, M., & Voss, E. (2021). Conversion to remote proctoring of the Community English Language Program Online Placement Exam at Teachers College, Columbia. University. Language Assessment Quarterly, 18(1), 42–50, https://doi.org/10.1080/15434303.2020.1867145 DOI: https://doi.org/10.1080/15434303.2020.1867145
Ramakrishnan, S., Bishnoi, M. M., Joghee, S., Jijitha, S., & Kumar, A. (2024). Social engineering: Role of teachers in cohabitation of AI with education [Conference paper]. 2nd International Conference on Cyber Resilience (ICCR), Dubai, United Arab Emirates. https://doi.org/10.1109/ICCR61006.2024.10532897 DOI: https://doi.org/10.1109/ICCR61006.2024.10532897
Ramdurai, B., & Adhithya, P. (2023). The impact, advancements and applications of generative AI. SSRG International Journal of Computer Science and Engineering, 10(6), 1-8. https://doi.org/10.14445/23488387/IJCSE-V10I6P101 DOI: https://doi.org/10.14445/23488387/IJCSE-V10I6P101
Rets, I., & Rogaten, J. (2020). To simplify or not? Facilitating English L2 users' comprehension and processing of open educational resources in English using text simplification. Journal of Computer Assisted Learning, 37(3), 705–717. https://doi.org/10.1111/jcal.12517 DOI: https://doi.org/10.1111/jcal.12517
Rizvi, S., Waite, J., & Sentance, S. (2023). Artificial intelligence teaching and learning in K-12 from 2019 to 2022: A systematic literature review. Computers and Education: Artificial Intelligence, 4, e100145. https://doi.org/10.1016/j.caeai.2023.100145 DOI: https://doi.org/10.1016/j.caeai.2023.100145
Saldaña, J. (2009). The coding manual for qualitative researchers. Sage.
Satya, C. B. V. V. (2024). Generative AI: Evolution and its future. International Journal for Multidisciplinary Research (IJFMR), 6(1), 1–6. https://doi.org/10.36948/ijfmr.2024.v06i01.12046 DOI: https://doi.org/10.36948/ijfmr.2024.v06i01.12046
Shin, D., & Lee, J. H. (2024). AI-powered automated item generation for language testing. ELT Journal 78(4), 446-452. https://doi.org/10.1093/elt/ccae016 DOI: https://doi.org/10.1093/elt/ccae016
Sidhu, B. K. (2022). Generative artificial intelligence: Unveiling the potential and challenges. International Journal of Science and Research (IJSR), 13(4). https://doi.org/10.21275/sr24414234432 DOI: https://doi.org/10.21275/SR24414234432
Spall, S. (1998). Peer debriefing in qualitative research: Emerging operational models. Qualitative Inquiry, 4(2), 280–292. https://doi.org/10.1177/107780049800400208 DOI: https://doi.org/10.1177/107780049800400208
Strauss, A., & Corbin, L (1990). Basics of grounded theory methods. Sage.
Voss, E., Cushing, S. T., Ockey, G. J., & Yan, X. (2023). The use of assistive technologies including generative AI by test takers in language assessment: A debate of theory and practice. Language Assessment Quarterly, 20(4–5), 520–532. https://doi.org/10.1080/15434303.2023.2288256 DOI: https://doi.org/10.1080/15434303.2023.2288256
Wang, P. (2019). On defining artificial intelligence. Journal of Artificial General Intelligence, 10(2), 1–37. https://doi.org/10.2478/jagi-2019-0002 DOI: https://doi.org/10.2478/jagi-2019-0002
Warankar, M., & Patil, R. (2024). Generative artificial intelligence. International Journal of Scientific Research in Engineering and Management (IJSREM), 8(4), 1–7. https://doi.org/10.55041/ijsrem31146 DOI: https://doi.org/10.55041/IJSREM31146
Wu, J., Huang, Z., Hu, Z., & Lv, C. (2022). Toward human-in-the-loop AI: Enhancing deep reinforcement learning via real-time human guidance for autonomous driving. Engineering, 21, 75–91. https://doi.org/10.1016/j.eng.2022.05.017 DOI: https://doi.org/10.1016/j.eng.2022.05.017
Xi, X. (2010). Automated scoring and feedback systems: Where are we and where are we heading? Language Testing, 27(3), 291-300. https://doi.org/10.1177/0265532210364643 DOI: https://doi.org/10.1177/0265532210364643
Xi, X. (2023). Advancing language assessment with AI and ML–Leaning into AI is inevitable, but can theory keep up? Language Assessment Quarterly, 20(4-5), 357–376. https://doi.org/10.1080/15434303.2023.2291488 DOI: https://doi.org/10.1080/15434303.2023.2291488
Yang, X. (2024). Linguistically responsive formative assessment for emergent bilinguals: exploration of an elementary teacher’s practice in a math classroom. International Multilingual Research Journal 19(1), 67-90. https://doi.org/10.1080/19313152.2024.2339757 DOI: https://doi.org/10.1080/19313152.2024.2339757
Youn, S. J. (2023). Test design and validity evidence of interactive speaking assessment in the era of emerging technologies. Language Testing, 40(1), 54–60. https://doi.org/10.1177/02655322221126606 DOI: https://doi.org/10.1177/02655322221126606
Zechner, K., & Hsieh, C. N. (2024). Automated scoring and feedback for spoken language. In M. D. Shermis, & J. Wilson (Eds.), The Routledge International Handbook of automated essay evaluation (pp. 141–160). Routledge. https://doi.org/10.4324/9781003397618 DOI: https://doi.org/10.4324/9781003397618-10
Zormanová, L. (2024). The attitudes of Czech teachers towards the use of artificial intelligence in schools. Horyzonty Wychowania, 23(65), 31–41. https://doi.org/10.35765/hw.2024.2365.05 DOI: https://doi.org/10.35765/hw.2024.2365.05
Esta obra está bajo una licencia internacional Creative Commons Atribución-NoComercial-SinDerivadas 4.0.