Desenho e Validação Psicométrica do Questionário de Percepção dos Professores do Uso de IA na Educação (TPU-AIED-Q)
DOI:
https://doi.org/10.24215/18509959.42.e4Palabras clave:
IA na educação, Validação psicométrica, Percepção docente, Análise fatorial, Alfa de CronbachResumen
O estudo teve como objetivo validar psicometricamente o Questionário de Percepção dos Professores do Uso de IA na Educação (TPU-AIED-Q), um instrumento destinado a avaliar as percepções dos docentes sobre a aplicação de IA no contexto educacional. Participaram da pesquisa 143 professores que responderam a um formulário eletrônico. Foram aplicados os testes KMO e de Esfericidade de Bartlett para verificar a adequação da amostra e a análise fatorial confirmatória (AFC) para validar a estrutura do questionário. Os resultados indicaram quatro componentes principais com altos valores de Alfa de Cronbach, assegurando a consistência interna das medidas. O estudo conclui que o TPU-AIED-Q é um instrumento válido e confiável para avaliar as percepções dos professores sobre o uso de IA na educação.
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[1] T. Baker and L. Smith, Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. London, UK: Centre for Education Research and Practice, 2019.
[2] N. Selwyn, Should Robots Replace Teachers? AI and the Future of Education. Cambridge, UK: Polity Press, 2019.
[3] O. Zawacki-Richter, V. I. Marín, M. Bond, and F. Gouverneur, “Systematic review of research on artificial intelligence applications in higher education – Where are the educators?,” Int. J. Educ. Technol. Higher Educ., vol. 16, no. 1, 2019.
[4] T. Teo, “Factors influencing teachers’ intention to use technology: Model development and test,” Computers & Education, vol. 57, no. 4, pp. 2432–2440, 2011.
[5] V. Venkatesh, M. G. Morris, G. B. Davis, and F. D. Davis, “User acceptance of information technology: Toward a unified view,” MIS Quarterly, vol. 27, no. 3, pp. 425–478, 2003.
[6] G. Guerino et al., “Nós vemos você: Compreendendo professores de matemática de escolas públicas brasileiras para projetar tecnologia educacional equitativa,” Rev. Bras. Inform. Educ., vol. 32, pp. 336–358, 2024. doi: 10.5753/rbie.2024.3649.
[7] R. F. DeVellis, Scale Development: Theory and Applications, 4th ed. Los Angeles, CA, USA: Sage, 2017.
[8] A. Field, Discovering Statistics Using IBM SPSS Statistics. London, UK: Sage, 2013.
[9] J. F. Hair, W. C. Black, B. J. Babin, and R. E. Anderson, Multivariate Data Analysis, 7th ed. Harlow, UK: Pearson Education, 2014.
[10] W. Holmes, M. Bialik, and C. Fadel, Artificial Intelligence in Education: Promises and Implications for Teaching and Learning. Boston, MA, USA: Center for Curriculum Redesign, 2019.
[11] R. Luckin, W. Holmes, M. Griffiths, and L. B. Forcier, Intelligence Unleashed: An Argument for AI in Education. London, UK: Pearson Education, 2016.
[12] J. F. Pane, E. D. Steiner, M. D. Baird, and L. S. Hamilton, How Does Personalized Learning Affect Student Achievement? Santa Monica, CA, USA: RAND Corporation, 2017.
[13] G. Siemens and R. S. J. D. Baker, “Learning analytics and educational data mining: Towards communication and collaboration,” in Proc. 2nd Int. Conf. Learning Analytics and Knowledge (LAK), New York, NY, USA: ACM, 2012, pp. 252–254.
[14] D. Long and B. Magerko, “What is AI literacy? Competencies and design considerations,” in Proc. CHI Conf. Human Factors in Computing Systems, New York, NY, USA: ACM, 2020, pp. 1–16. doi: 10.1145/3313831.3376727.
[15] D. M. West, The Role of AI in Education: Challenges and Opportunities. Washington, DC, USA: Brookings Institution, 2019.
[16] S. Isotani, I. I. Bittencourt, G. C. Challco, D. Dermeval, and R. F. Mello, “AIED unplugged: Leapfrogging the digital divide to reach the underserved,” in Artificial Intelligence in Education, N. Wang et al., Eds. Cham, Switzerland: Springer, 2023, vol. 1831. doi: 10.1007/978-3-031-36336-8_118.
[17] M. Warschauer and T. Matuchniak, “New technology and digital worlds: Analyzing evidence of equity in access, use, and outcomes,” Rev. Res. Educ., vol. 34, no. 1, pp. 179–225, 2010.
[18] P. A. Ertmer, “Addressing first- and second-order barriers to change: Strategies for technology integration,” Educ. Technol. Res. Dev., vol. 47, no. 4, pp. 47–61, 1999.
[19] J. E. Aoun, Robot-Proof: Higher Education in the Age of Artificial Intelligence. Cambridge, MA, USA: MIT Press, 2017.
[20] I. Etikan, S. A. Musa, and R. S. Alkassim, “Comparison of convenience sampling and purposive sampling,” Amer. J. Theor. Appl. Stat., vol. 5, no. 1, pp. 1–4, 2016. doi: 10.11648/j.ajtas.20160501.11.
[21] H. F. Kaiser, “The application of electronic computers to factor analysis,” Educ. Psychol. Meas., vol. 20, pp. 141–151, 1960.
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Derechos de autor 2026 Thomaz Edson Veloso da Silva
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