Universidade Federal de Alagoas Maceió, 11 de Julho de 2025

Resumo do Componente Curricular

Dados Gerais do Componente Curricular
Tipo do Componente Curricular: DISCIPLINA
Unidade Responsável: PROGRAMA DE PÓS-GRADUAÇÃO EM INFORMÁTICA (11.00.43.56.07)
Código: PPGI066
Nome: INTELIGÊNCIA ARTIFICIAL NA EDUCAÇÃO
Carga Horária Teórica: 45 h.
Carga Horária Prática: 15 h.
Carga Horária Total: 60 h.
Pré-Requisitos:
Co-Requisitos:
Equivalências:
Excluir da Avaliação Institucional: Não
Matriculável On-Line: Sim
Horário Flexível da Turma: Não
Horário Flexível do Docente: Sim
Obrigatoriedade de Nota Final: Sim
Pode Criar Turma Sem Solicitação: Não
Necessita de Orientador: Não
Exige Horário: Sim
Permite CH Compartilhada: Não
Permite Múltiplas Aprovações: Não
Quantidade de Avaliações: 3
Ementa/Descrição: Compreender o desenvolvimento paralelo entre a evolução das Ciências da Aprendizagem (como as pessoas aprendem) e a Inteligência Artificial (como as máquinas aprendem) no contexto do Ensino e Aprendizagem; Distinguir entre IA genérica e IA especificamente projetada para educação; Entender os conceitos fundamentais da IA, incluindo suas aplicações e seu impacto em nossas vidas diárias (como no desenho e implementação de políticas públicas); Compreender os fundamentos teóricos dos Sistemas de Tutoria Inteligente (ITS); Entender os conceitos de Mineração de Dados Educacionais (EDM) e Análise de Aprendizagem (LA); Aplicar vários tipos de dados para treinar tecnologias de IA; Utilizar uma estrutura para avaliar os potenciais riscos associados ao uso da IA na Educação; Analisar e testar tecnologias de IA para diversos propósitos, incluindo educação, entretenimento, automação de tarefas, recomendação e análise de comportamento, entre outros; Projetar um ITS básico adquirindo habilidades para converter atividades de resolução de problemas em modelos pedagógicos de ITS; Desenvolver modelos e empregar técnicas de EDM com implicações práticas tangíveis.
Referências: • Doroudi, S. (2022). The intertwined histories of artificial intelligence and education. International Journal of Artificial Intelligence in Education, 1-44. https://doi.org/10.1007/s40593-022-00313-2 ● Chapter 1 “Introduction” of the book Artificial Intelligence: A Modern Approach, 4th US ed., by Stuart Russell and Peter Norvig. Pages: 1 – 35. MINISTÉRIO DA EDUCAÇÃO UNIVERSIDADE FEDERAL DE ALAGOAS INSTITUTO DE COMPUTAÇÃO Av. Lourival Melo Mota, S/N, Tabuleiro do Martins, Maceió - AL, 57.072-970 PROGRAMA DE PÓS-GRADUAÇÃO EM INFORMÁTICA ● Yu, S. and Lu, Y., 2021. Chapter 2: An Overview of AI. An introduction to artificial intelligence in education. Springer. https://link.springer.com/chapter/10.1007/978-981-16- 2770-5_2 ● Davis, K., Christodoulou, J., Seider, S., & Gardner, H.(2011). The theory of multiple intelligences. In RJ Sternberg & SB Kaufman (Eds.), Cambridge Handbook of Intelligence. 485-503. https://doi.org/10.1017/CBO9780511977244.025 ● Saunders, L., & Wong, M. A. (2020). Chapter 3 - Learning Theories: Understanding How People Learn. Instruction in Libraries and Information Centers. https://iopn.library.illinois.edu/pressbooks/instructioninlibraries/chapter/learning- theoriesunderstanding-how-people-learn/ ● Cao, L., & Dede, C. (2023). Navigating A World of Generative AI: Suggestions for Educators. The Next Level Lab at Harvard Graduate School of Education. President and Fellows of Harvard College: Cambridge, MA https://nextlevellab.gse.harvard.edu/files/2023/08/Cao_Dede_final_8.4.23.pdf ● Miao, F. & Holmes, W. (2023) Guidance for generative AI in education and research. UNESCO Publishing. https://unesdoc.unesco.org/ark:/48223/pf0000386693 • Baker, R. S. (2021). Artificial intelligence in education: Bringing it all together. OECD Digital Education Outlook 2021 Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. https://doi.org/10.1787/f54ea644-en ● Isotani, S., Bittencourt, I. I., Challco, G. C., Dermeval, D., & Mello, R. F. (2023, June). AIED Unplugged: Leapfrogging the Digital Divide to Reach the Underserved. In International Conference on Artificial Intelligence in Education (pp. 772-779). Cham: Springer Nature Switzerland. https://doi.org/10.1007/978-3-031-36336-8_118 ● Aleven, V., McLaughlin, E. A., Glenn, R. A., & Koedinger, K. R. (2017). Chapter 24: Instruction based on adaptive learning technologies. In R. E. Mayer & P. Alexander (Eds.), Handbook of research on learning and instruction (2nd ed., pp. 522-560). New York: Routledge. https://doi.org/10.4324/9781315736419 ● Chapter 3: Personalisation of learning: Towards hybrid human-AI learning technologies. OECD Digital Education Outlook 2021 : Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots. https://doi.org/10.1787/2cc25e37-en ● Li, S., & Gu, X. (2023). A Risk Framework for Human-centered Artificial Intelligence in Education: Based on Literature Review and Delphi–AHP Method. Educational Technology & Society, 26(1), 187-202. https://doi.org/10.30191/ETS.202301_26(1).0014 ● Baker, R. S., & Hawn, A. (2022). Algorithmic bias in education. International Journal of Artificial Intelligence in Education, 1052–1092. https://doi.org/10.1007/s40593-021-00285- 9 • Pammer-Schindler, V., & Rosé, C. (2022). Data-related ethics issues in technologies for informal professional learning. International Journal of Artificial Intelligence in Education, 609–635. https://link.springer.com/article/10.1007/s40593-021-00259-x ● VanLehn, K. (2006). The behavior of tutoring systems. International Journal of Artificial Intelligence in Education, 16(3), 227-265 https://learnlab.org/opportunities/summer/readings/06IJAIED.pdf ● Luckin, R; Holmes, W; (2016) Intelligence Unleashed: An argument for AI in Education. UCL Knowledge Lab: London, Uk. https://discovery.ucl.ac.uk/id/eprint/1475756/ ● Nye, B. D. (2015). Intelligent tutoring systems by and for the developing world: A review of trends and approaches for educational technology in a global context. International Journal of Artificial Intelligence in Education, 25(2), 177-203. https://doi.org/10.1007/s40593-014-0028-6 ● Romero, C., Ventura, S., & García, E. (2008). Data mining in course management systems: Moodle case study and tutorial. Computers & Education, 51(1), 368-384. https://doi.org/10.1016/j.compedu.2007.05.016 ● Bittencourt, I. I., Chalco, G., Santos, J., Fernandes, S., Silva, J., Batista, N., ... & Isotani, S. (2023). Positive Artificial Intelligence in Education (P-AIED): A Roadmap. International Journal of Artificial Intelligence in Education, 1-61. https://link.springer.com/article/10.1007/s40593-023-00357-y ● Ifenthaler, D. (2021). Learning analytics for school and system management. OECD Digital Education Outlook 2021 Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots: Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots, 161. https://doi.org/10.1787/d535b828-en ● Unicef. (2018). Early warning systems for students at risk of dropping out. Unicef Series on Education Participation and Dropout Prevention, 2. https://www.allinschool.org/reports/early-warning-systems-students-at-risk-dropping-out ● Haimovich, F., Vazquez, E., & Adelman, M. (2021). Scalable early warning systems for school dropout prevention: Evidence from a 4.000-school randomized controlled trial (No. 285) https://elibrary.worldbank.org/doi/pdf/10.1596/1813-9450-9685 ● Chapter 1: Frontiers of smart education technology: Opportunities and challenges. OECD Digital Education Outlook 2021 : Pushing the Frontiers with Artificial Intelligence, Blockchain and Robots ● Miao, F., Holmes, W., Huang, R., & Zhang, H. (2021). AI and education: A guidance for policymakers. UNESCO Publishing. ● Koedinger, K. R., Booth, J. L., & Klahr, D. (2013). Instructional complexity and the science to constrain it. Science, 342(6161), 935-937.

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