Palavras-chave
Esforço Generativo
Educador como Facilitador
Pedagogia Baseada em Erros
Fracasso Produtivo
Como Citar
Resumo
Contexto: Este estudo desenvolve o conceito de esforço generativo como uma estrutura pedagógica para a aprendizagem da língua inglesa mediada por inteligência artificial, considerando os riscos cognitivos associados à realização de tarefas por IA e à transferência de processos cognitivos para sistemas automatizados.
Objetivo: O estudo busca: (1) identificar barreiras psicológicas que podem influenciar o envolvimento dos estudantes com seus próprios recursos cognitivos durante atividades de escrita mediadas por IA; e (2) conceituar o papel do professor como mentor epistêmico, capaz de promover esforço generativo, pensamento divergente, agência do estudante e desenvolvimento linguístico significativo em ambientes de escrita apoiados por IA generativa.
Métodos: O estudo adota uma abordagem de síntese conceitual e teórica, integrando perspectivas da psicologia cognitiva, aquisição de segunda língua, ensino da língua inglesa e tecnologia educacional. A análise articula cinco tradições teóricas consolidadas: efeito de geração, dificuldades desejáveis, escrita para aprender, aprendizagem baseada em erros e transferência cognitiva para recursos externos. Essas perspectivas são examinadas em conjunto com a abordagem centrada no estudante prevista pela Política Nacional de Educação da Índia (NEP 2020), com o objetivo de compreender a relação entre esforço cognitivo e desenvolvimento linguístico significativo.
Resultados: A análise sugere que a dependência excessiva da eficiência algorítmica pode comprometer o engajamento cognitivo ao suprimir o esforço generativo inerente ao processo de aquisição de uma língua. A estrutura proposta, portanto, prioriza o esforço generativo e a autocorreção orientada em detrimento da simples conclusão de tarefas, reposicionando o professor como mentor epistêmico responsável por estimular o engajamento reflexivo, metacognitivo e crítico para além da denominada “armadilha da aparência de competência”. Em vez de rejeitar a automação, a proposta defende uma integração pedagogicamente mediada da IA, capaz de favorecer a aprendizagem generativa e, simultaneamente, limitar a terceirização de processos cognitivos. Os achados fornecem uma base para orientações didáticas preliminares, passíveis de adaptação aos níveis de proficiência dos estudantes, aos diferentes contextos institucionais e aos modelos de avaliação.
Conclusão: O estudo conceitua o esforço generativo como um construto integrador que reúne cinco tradições consolidadas da psicologia cognitiva para explicar a aprendizagem em contextos mediados por IA. Os conceitos de ciclo educacional da IA e armadilha da aparência de competência contribuem, ainda, para compreender as tensões emergentes entre desempenho assistido por IA, engajamento cognitivo e aprendizagem efetiva. Em conjunto, esses conceitos oferecem uma base teórica para compreender de que maneira ambientes educacionais mediados por inteligência artificial podem favorecer aprendizagens significativas sem substituir o esforço cognitivo essencial ao desenvolvimento linguístico.
Referências
Al-Khresheh, M. H. (2024). Bridging technology and pedagogy from a global lens: Teachers’ perspectives on integrating ChatGPT in English language teaching. Computers and Education: Artificial Intelligence, 6, Article 100218. https://doi.org/10.1016/j.caeai.2024.100218
Anderson, T. (2010). The effects of tiered corrective feedback on Second language academic writing (thesis). The effects of tiered corrective feedback on second language academic writing. University of British Columbia, Vancouver.
Aquino, M. (2026). From Freire’s critical pedagogy to the age of artificial intelligence: Continuities, ruptures, and new educational challenges. Science Insights Education Frontiers, 33(2), 5387–5401. https://eric.ed.gov/?id=EJ1506336
Assis Hornay, P. M. (2021). Cognitive load theory and the English language instruction. ACCENTIA: Journal of English Language and Education, 1(1), 43–55. https://doi.org/10.37598/accentia.v1i1.1015
Bangert-Drowns, R. L., Hurley, M. M., and Wilkinson, B. (2004). The effects of school-based writing-to-learn interventions on academic achievement: A meta-analysis. Review of Educational Research, 74(1), 29–58. https://doi.org/10.3102/00346543074001029
Barr, N., Pennycook, G., Stolz, J. A., and Fugelsang, J. A. (2015). The brain in your pocket: Evidence that smartphones are used to supplant thinking. Computers in Human Behavior, 48, 473–480. https://doi.org/10.1016/j.chb.2015.02.029
Bertsch, S., Pesta, B. J., and McDaniel, M. A. (2007). The generation effect: A meta-analytic review. Memory & Cognition, 35, 201–210. https://doi.org/10.3758/BF03193441
Bjork, E. L., and Bjork, R. A. (2011). Making things hard on yourself, but in a good way: Creating desirable difficulties to enhance learning. In M. A. Gernsbacher, & J. R. Pomerantz (Eds.), Psychology and the real world: Essays illustrating fundamental contributions to society, Worth Publishers, 2nd ed., 55–64. https://bjorklab.psych.ucla.edu/wp-content/uploads/sites/13/2016/04/EBjork_RBjork_2011.pdf
Butterfield, B., and Metcalfe, J. (2001). Errors committed with high confidence are hypercorrected. Journal of Experimental Psychology: Learning, Memory, and Cognition, 27(6), 1491–1494. https://doi.org/10.1037/0278-7393.27.6.1491
Corder, S. P. J. (1967) The Significance of Learner’s Errors. International Review of Applied Linguistics, No. 4, 161-170. https://doi.org/10.1515/iral.1967.5.1-4.161
Dwivedi, Y. K., et al. (2023). Opinion Paper: “So what if ChatGPT wrote it?” Multidisciplinary perspectives on opportunities, challenges and implications of generative conversational AI for research, practice and policy. International Journal of Information Management, 71, 102642. https://doi.org/10.1016/j.ijinfomgt.2023.102642
Edelouei, S. (2025). Factors influencing language teachers’ judgements and decision-making about the use of generative AI tools: A systematic review. Teaching and Teacher Education, 171, 1–14. https://doi.org/10.1016/j.tate.2025.105333
Emig, J. (1977). Writing as a mode of learning. College Composition and Communication, 28(2), 122–128. https://openlab.citytech.cuny.edu/fywpd/files/2019/01/emig-writing-as-a-mode-of-learning.pdf
Ganai, P. A., and Naikoo, I. A. (2024). An analysis of grammatical errors in academic essay writing among the students of Jazan University. International Journal of Arts and Humanities, 2(1), 10–25. https://doi.org/10.61424/ijah.v2.i2.68
Government Offices of Sweden. (2024, February 8). Government investing in more reading time and less screen time. https://www.government.se/articles/2024/02/government-investing-in-more-reading-time-and-less-screen-time/
Guo, J., et al. (2024). Harnessing artificial intelligence in generative content for enhancing motivation in learning. Learning and Individual Differences, 116, Article 102547. https://doi.org/10.1016/j.lindif.2024.102547
Gupta, D. S., and Chatterjee, J. (2023). Transforming English language teaching: A study of the National Education Policy 2020. Journal of English Language Teaching, 65(4), 11–17. https://journals.eltai.in/index.php/jelt/article/view/JELT650403/982
Hamouda, A. (2011). A study of students’ and teachers’ preferences and attitudes towards correction of classroom written errors in the Saudi EFL context. English Language Teaching, 4(3), 128–141. https://doi.org/10.5539/elt.v4n3p128
Jose, B., Cherian, J., Verghis, A. M., Varghise, S. M., Mumthas, S., and Joseph, S. (2025). The cognitive paradox of AI in education: Between enhancement and erosion. Frontiers in Psychology, 16, Article 1550621. https://doi.org/10.3389/fpsyg.2025.1550621
Kapur, M. (2008). Productive failure. Cognition and Instruction, 26(3), 379–424. https://doi.org/10.1080/07370000802212669
Kapur, M. (2016). Examining productive failure, productive success, unproductive failure, and unproductive success in learning. Educational Psychologist, 51(2), 289–299. https://doi.org/10.1080/00461520.2016.1155457
Kasneci, E., et al. (2023). ChatGPT for good? On opportunities and challenges of large language models for education. Learning and Individual Differences, 103, 102274. https://doi.org/10.1016/j.lindif.2023.102274
Keith, N., and Frese, M. (2008). Effectiveness of error management training: A meta-analysis. Journal of Applied Psychology, 93(1), 59–69. https://doi.org/10.1037/0021-9010.93.1.59
Kiefer, M., and Velay, J.-L. (2016). Writing in the digital age. Trends in Neuroscience and Education, 5(3), 77–81. https://doi.org/10.1016/j.tine.2016.07.008
Klein, P. D., and Boscolo, P. (2016). Trends in research on writing as a learning activity. Journal of Writing Research, 7(3), 311–350. https://doi.org/10.17239/jowr-2016.07.03.01
Krashen, S. D. (1982). Principles and practice in second language acquisition. Pergamon Press. http://www.sdkrashen.com/content/books/principles_and_practice.pdf
Mangen, A., et al. (2015). Handwriting versus keyboard writing: Effect on word recall. Journal of Writing Research, 7(2), 227–247. https://doi.org/10.17239/jowr-2015.07.02.1
Mayer, R. E. (Ed.). (2014). The Cambridge handbook of multimedia learning (2nd ed.). Cambridge University Press. https://doi.org/10.1017/CBO9781139547369
Mayer, R. E., et al. (2014). Multimedia learning in a second language: A cognitive load perspective. Applied Cognitive Psychology, 28(5), 653–660. https://doi.org/10.1002/acp.3050
Mehdi, M. F. (2018). Analysis of errors made by first-year secondary school students in writing English sentences: A case study of Libyan EFL students. International Journal of Linguistics, 10(6), 206–226. https://doi.org/10.5296/ijl.v10i6.14134
Metcalfe, J. (2017). Learning from errors. Annual Review of Psychology, 68, 465–489. https://doi.org/10.1146/annurev-psych-010416-044022
Ministry of Education. (2020). National Education Policy 2020. Government of India. https://www.education.gov.in/sites/upload_files/mhrd/files/NEP_Final_English_0.pdf
Mosier, K. L., and Skitka, L. J. (1996). Human decision makers and automated decision aids: Made for each other? In R. Parasuraman and M. Mouloua (Eds.), Automation and human performance: Theory and applications, Lawrence Erlbaum Associates, 201–220. https://www.researchgate.net/publication/230601064_Human_Decision_Makers_and_Automated_Decision_Aids_Made_for_Each_Other
Raimes, A. (1983). Techniques in Teaching Writing. In the series: Teaching Techniques in English as a Second Language, Oxford University Press: New York, 1-164. ISBN:9780194341318
Risko, E. F., and Gilbert, S. J. (2016). Cognitive offloading. Trends in Cognitive Sciences, 20(9), 676–688. https://doi.org/10.1016/j.tics.2016.07.002
Runco, M. A. (2023). AI can only produce artificial creativity. Journal of Creativity, 33(3), Article 100063. https://doi.org/10.1016/j.yjoc.2023.100063
Runco, M. A. (2025). The misleading definition of creativity suggested by AI must be kept out of the classroom. Education Sciences, 15(9), Article 1141. https://doi.org/10.3390/educsci15091141
Sarkar, S. (2026). Mapping Interlanguage through Error Analysis: A PEN Framework for Pedagogical Intervention in Rural ESL Contexts. Euro-Global Journal of Linguistics and Language Education, 251-266. https://doi.org/10.69760/egjlle.2602029
Slamecka, N. J., and Graf, P. (1978). The generation effect: Delineation of a phenomenon. Journal of Experimental Psychology: Human Learning and Memory, 4(6), 592–604. https://doi.org/10.1037/0278-7393.4.6.592
Sriram, R. (2020, April 13). The neuroscience behind productive struggle. Edutopia. https://www.edutopia.org/article/neuroscience-behind-productive-struggle/
Sumakul, D. T., Hamied, F. A., and Sukyadi, D. (2022). Artificial intelligence in EFL classrooms: Friend or foe? LEARN Journal: Language Education and Acquisition Research Network, 15(1), 232–256. https://so04.tci-thaijo.org/index.php/LEARN/article/view/256723
Suzuki, W., Nassaji, H., and Sato, K. (2019). The effects of feedback explicitness and type of target structure on accuracy in revision and new pieces of writing. System, 81, 135–145. https://doi.org/10.1016/j.system.2018.12.017
Swain, M. (1995). Three functions of output in second language learning. In G. Cook, & B. Seidlhofer (Eds.), Principle and practice in applied linguistics: Studies in honour of H. G. Widdowson, Oxford: Oxford.
Sweller, J., Ayres, P., and Kalyuga, S. (2011). Cognitive load theory in perspective. In Cognitive load theory, pp. 237–242. Springer. https://doi.org/10.1007/978-1-4419-8126-4_18
Tlili, A., Shehata, B., Adarkwah, M. A., Bozkurt, A., Hickey, D. T., Huang, R., and Agyemang, B. (2023). What if the devil is my guardian angel: ChatGPT as a case study of using chatbots in education. Smart Learning Environments, 10(1), 1–24. https://doi.org/10.1186/s40561-023-00237-x
Ulla, M. B., Perales, W. F., and Busbus, S. O. (2023). “To generate or stop generating response”: Exploring EFL teachers’ perspectives on ChatGPT in English language teaching in Thailand. Learning: Research and Practice, 1–15. https://doi.org/10.1080/23735082.2023.2257252
Umar, U. (2024). Advancements in English language teaching: Harnessing the power of artificial intelligence. FLIP: Foreign Language Instruction Probe, 3(1), 29–42. https://doi.org/10.54213/flip.v3i1.402
Wei, L. (2023). Artificial intelligence in language instruction: Impact on English learning achievement, L2 motivation, and self-regulated learning. Frontiers in Psychology, 14, Article 1261955. https://doi.org/10.3389/fpsyg.2023.1261955
Zhai, X. (2023). ChatGPT and AI: The game changer for education. 67, 16-17 (1-4). https://doi.org/10.13140/RG.2.2.31107.37923