Adaptive Learning Assessment through the Use of Artificial Intelligence (AI) in Distance Education

Authors

DOI:

https://doi.org/10.18264/eadf.v16i1.2755

Keywords:

Personalized teaching, Individualized feedback, Remote education

Abstract

This study aimed to analyze the contributions of adaptive learning processes mediated by Artificial Intelligence (AI) tools, focusing on their potential application in Distance Education (DE). The investigation was carried out through a Systematic Literature Review (SLR), based on the PRISMA methodology, covering publications from 2020 to 2024 in the Scopus and Web of Science databases. Fourteen articles addressing the integration of AI in adaptive learning environments were selected. The most recurrent technologies in the studies included chatbots and virtual agents, which highlighted the benefits of personalized instruction and pointed out limitations. The results show that AI enhances instructional personalization, enables immediate and more individualized feedback, optimizes the monitoring of student progress, and supports the adaptation of content according to learners’ profiles and performance. However, the studies also identified limitations, such as difficulties in interpreting complex conceptual errors, as well as structural challenges, limited time for curricular adaptation, and the absence of clear regulatory guidelines. In conclusion, despite the challenges identified in applying such a recent technology, the findings indicate that the integration of AI and adaptive learning in Distance Education represents a growing and promising trend.

 

Keywords: Personalized teaching. Individualized feedback. Remote education. 

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Author Biographies

Naiara Lima Costa, Universidade Estadual Paulista

É graduada em Engenharia de Energia e Mestre em Engenharia Agrícola pela Universidade Federal da Grande Dourados, UFGD. Atualmente é doutorando em Ciência e Tecnologia de Materiais pela Universidade Estadual Paulista, Unesp. É especialista em  Processos Didático-Pedagógico para Cursos na Modalidade a Distância pela Universidade Virtual do Estado de São Paulo, UNIVESP.

Maria Fernanda Sua Rojas, Universidade Estadual de Campinas

Graduada em BIOLOGIA pela Universidad Industrial de Santander, UIS, Colômbia e doutoranda em Bioenergia na Universidade Estadual de Campinas, UNICAMP. É especialista em Processos Didático-Pedagógico para Cursos na Modalidade a Distância pela Universidade Virtual do Estado de São Paulo, UNIVESP.

Marina Schimidt, Universidade Estadual Paulista

Graduada em Gestão da Tecnologia da Informação pelo Centro Estadual de Educação Tecnológica Paula Souza, FATEC JAHU.  Licenciatura em Matemática e mestrado em Matemática Aplicada e Computacional pela Universidade Estadual Paulista, UNESP.  Doutoranda no Programa de Pós-Graduação em Engenharia Elétrica também pela UNESP. É especialista em Processos Didático-Pedagógico para Cursos na Modalidade a Distância pela Universidade Virtual do Estado de São Paulo, UNIVESP.

Juliana da Silva Amaral Bruno, Universidade Federal de São Carlos

Graduada em Fisioterapia pela Universidade Camilo Castelo Branco (2004).  Mestra e Doutora pelo Programa de Pós- Graduação em Biotecnologia (PPG Biotec) na Universidade Federal de São Carlos. É especialista em Processos Didático-Pedagógico para Cursos na Modalidade a Distância pela Universidade Virtual do Estado de São Paulo, UNIVESP.

Ana Lúcia Gabas Ferreira, Universidade de São Paulo

Professora Associada da Universidade de São Paulo (USP), possui ampla trajetória acadêmica e científica nas áreas de Engenharia de Alimentos e Ciências Ambientais. É Engenheira de Alimentos com mestrado (1998) e doutorado (2002) pela UNICAMP, além do título de Livre-Docente pela USP (2006). Iniciou sua carreira docente na Faculdade de Zootecnia e Engenharia de Alimentos (FZEA/USP) em 2002 e, desde 2013, integra o corpo docente da Escola de Engenharia de Lorena (EEL/USP), onde atua nos Programas de Pós-Graduação em Engenharia Química (PPGEQ) e Meio Ambiente e Desenvolvimento (PPGMAD). Coordena projetos voltados à sustentabilidade e inovação tecnológica, especialmente no reaproveitamento de resíduos agroindustriais e na produção de biocombustíveis.

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Published

2026-03-26

How to Cite

Costa, N. L., Rojas, M. F. S., Schimidt, M., Bruno, J. da S. A., & Ferreira, A. L. G. (2026). Adaptive Learning Assessment through the Use of Artificial Intelligence (AI) in Distance Education. EaD Em Foco, 16(1), e2755. https://doi.org/10.18264/eadf.v16i1.2755

Issue

Vol. 16 No. 1 (2026)

Section

Revisões

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