The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Adaptive Learning in Engineering Courses: How Artificial Intelligence (AI) Can Improve Academic Outcomes
Abstract
This qualitative research article explores the impact of Artificial Intelligence (AI) on enhancing academic performance in engineering courses, focusing on adaptive learning systems. The study highlights the evolution of digital technologies in education, emphasizing the applicability of AI in personalizing and adapting learning. Analyzing the integration of AI in engineering education, the study unveils benefits such as teaching customization and early detection of student difficulties. Concurrently, challenges are discussed, including ethical issues like data privacy and algorithmic biases. The research, adopting a qualitative approach, is grounded in an integrative literature review, considering recent studies on the application and impact of AI in higher education. The findings suggest that AI holds significant potential for transforming engineering education, provided its implementation is accompanied by ethical considerations and proper educator preparation.
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“Slomp et al. [12] also mention the potential of GenAI models for personalized learning but caution against privacy violations, biased output, and other ethical misuse of the technology.”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [12]
In the citing paper
“Slomp et al. [12] also mention the potential of GenAI models for personalized learning but caution against privacy violations, biased output, and other ethical misuse of the technology.”
From Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda (2025) · cites this paper as [12]
Citations
Cites
The integrative review: updated methodology
Whittemore, R., & Knafl, K. (2005) · Journal of Advanced Nursing · doi:10.1111/j.1365-2648.2005.03621.x · Cited by 10,241
The 2 Sigma Problem: The Search for Methods of Group Instruction as Effective as One-to-One Tutoring
BLOOM, B. S. (1984) · Educational Researcher · doi:10.3102/0013189x013006004 · Cited by 2,734 · cited 1×
“"[...] individualized learning is that which adapts to the individual characteristics of the learner, allowing them to progress at their own pace and according to their own capabilities" [5].”
BackgroundStudents' voices on generative ai: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · arXiv · doi:10.48550/arxiv.2305.00290 · Cited by 2,504 · cited 4×
“The identified benefits include the facilitation of adaptive and personalized learning [14], the provision of personalized feedback to students [13], [14], support for research and data analysis [14], automated administrative services [14], and the development of innovative educational materials…”
BackgroundInfluentialStudents’ voices on generative AI: perceptions, benefits, and challenges in higher education
Chan, C. K. Y., & Hu, W. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00411-8 · Cited by 2,301 · cited 4×
“The identified benefits include the facilitation of adaptive and personalized learning [14], the provision of personalized feedback to students [13], [14], support for research and data analysis [14], automated administrative services [14], and the development of innovative educational materials…”
BackgroundInfluentialWriting Integrative Literature Reviews: Using the Past and Present to Explore the Future
Torraco, R. J. (2016) · Human Resource Development Review · doi:10.1177/1534484316671606 · Cited by 2,031
A comprehensive AI policy education framework for university teaching and learning
Chan, C. K. Y. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00408-3 · Cited by 1,577 · cited 1×
“Finally, [19] proposes a comprehensive educational policy framework to integrate AI in higher education, addressing pedagogical, governance, and operational issues.”
BackgroundAI-generated feedback on writing: insights into efficacy and ENL student preference
Escalante, J., Pack, A., & Barrett, A. (2023) · International Journal of Educational Technology in Higher Education · doi:10.1186/s41239-023-00425-2 · Cited by 589
The role of ChatGPT in higher education: Benefits, challenges, and future research directions
(2023) · Journal of Applied Learning & Teaching · doi:10.37074/jalt.2023.6.1.29 · Cited by 561 · cited 6×
“…[14], the provision of personalized feedback to students [13], [14], support for research and data analysis [14], automated administrative services [14], and the development of innovative educational materials and assessments [14], [15], demonstrating the value of these technologies in education.”
BackgroundInfluentialA GAMIFICAÇÃO APLICADA EM AMBIENTES DE APRENDIZAGEM
Fardo, M. L. (2013) · RENOTE · doi:10.22456/1679-1916.41629 · Cited by 130
Inteligência Artificial e sociedade: avanços e riscos
Sichman, J. S. (2021) · Estudos Avançados · doi:10.1590/s0103-4014.2021.35101.004 · Cited by 94
Pack, A. (2023) · Teaching English With Technology · doi:10.56297/buka4060/vrro1747 · Cited by 69
Greiner, C., Peisl, T. C., Höpfl, F., & Beese, O. (2023) · Education Sciences · doi:10.3390/educsci13090865 · Cited by 29
INTELIGÊNCIA ARTIFICIAL E SEUS IMPACTOS NA EDUCAÇÃO: UMA REVISÃO SISTEMÁTICA
da Silva, K. R., Barbosa, L. S. D. O., Botelho, W. L., Pinheiro, J. M. B., Peixoto, I. D. S., & de Menezes, I. V. C. B. (2023) · RECIMA21 - Revista Científica Multidisciplinar · doi:10.47820/recima21.v4i11.4353 · Cited by 22
Triantafyllou, S. A. (2023) · Lecture notes in networks and systems · doi:10.1007/978-3-031-35311-6_18 · Cited by 19
Sistemas de Recomendação de Recursos Educacionais: conceitos, técnicas e aplicações
Costa, E. D. B., Aguiar, J. J. B., & Magalhães, J. (2013) · Anais ... Jornada de Atualização em Informática na Educação · doi:10.5753/cbie.jaie.2013.57 · Cited by 15
Artificial Intelligence and Online Engineering
Auer, M., El-Seoud, S. A., & Karam, O. H. (2022) · Lecture notes in networks and systems · doi:10.1007/978-3-031-17091-1 · Cited by 9
Métodos para Análise de Sentimentos no Twitter
Araújo, M., Gonçalves, P., & Benevenuto, F. (2013) · Cited by 5
Bramer, M., & Stahl, F. T. (2022) · Lecture notes in computer science · doi:10.1007/978-3-031-21441-7 · Cited by 5
Fernandes, C. E. L., Ribeiro, A. P. D. M., & Vasconcelos, F. H. L. (2022) · Conexões - Ciência e Tecnologia · doi:10.21439/conexoes.v16i0.2282 · Cited by 3
Avila, A., & Días, M. A. (2013) · Proceedings of International Conference on Engineering and Computer Education · doi:10.14684/icece.08.2013.255-258 · Cited by 2
Adaptive Kevin: A Multipurpose AI Assistant for Higher Education
Gonzalez-Bonorino, A., & Lauría, E. J. M. (2023) · Communications in computer and information science · doi:10.1007/978-3-031-40501-3_7 · Cited by 2
Perspectivas tecnológicas para o aprimoramento de chatbots educacionais em AIML
Paz, F. J., Silveira, C., Krassmann, A. L., & Tarouco, L. M. R. (2017) · Revista Iberoamericana de Tecnología en Educación y Educación en Tecnología · doi:10.24215/18509959..p · Cited by 2
A influência da inteligência artificial na educação
de Andrade, J. L. (2019) · Revista Científica Multidisciplinar Núcleo do Conhecimento · Cited by 1
Research works printed but not linked: 4
- Júnior, 2023, Aprendizagem adaptativa com inteligência artificial: Uma proposta para a educação
- 10.2307/1175554 · DOI · Paper page
- Creswell, 2007, Projeto de pesquisa: métodos qualitativo, quantitativo e misto
- Dias, 2004, Desenvolvimento de objectos de aprendizagem para plataformas colaborativas, 3
Cited by
Sorted by citations
Generative AI and Academic Integrity in Higher Education: A Systematic Review and Research Agenda
Bittle, K., & El-Gayar, O. (2025) · Information · doi:10.3390/info16040296 · Cited by 305
“Slomp et al. [12] also mention the potential of GenAI models for personalized learning but caution against privacy violations, biased output, and other ethical misuse of the technology.”
Hasanah, N. A., Aziza, M. R., Junikhah, A., Arif, Y. M., & Garcia, M. B. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3373-0122-8.ch016 · Cited by 16
Al-Shamaileh, O., Hammady, R., Abdelrahman, M., & Mubin, O. (2026) · Computers in Human Behavior Reports · doi:10.1016/j.chbr.2026.100980 · Cited by 6
Chasokela, D., & Hlongwane, J. (2025) · Advances in computational intelligence and robotics book series · doi:10.4018/979-8-3693-5623-4.ch012 · Cited by 1
Kane, D., Goodridge, W., Ahlstrom, L., Lawanto, O., Harper, M., & McCall, C. (2025) · ASEE Annual Conference & Exposition · doi:10.18260/1-2--55915 · Cited by 1
Metadata
- Authors
- Slomp, E. M., Ropelato, D., Bonatti, C., & da Silva, M. D.
- Affiliations
- Universidade Federal de Santa Catarina (BR)
- UFSC - Universidade Federal de Santa Catarina,Engenharia, Gestão e Mídia do Conhecimento - PPGEGC,Florianópolis,SC,Brazil
- Venue
- 2024 IEEE World Engineering Education Conference (EDUNINE)
- Publisher
- IEEE
- Published
- 2024-03-10
- Pages
- 1-6
- OpenAlex
- W4395070188
- Cited by
- 8
- References
- 23
- Citations by year
- 2026: 2 · 2025: 6
- FWCI
- 3.05
- Type
- conference paper
- Language
- en
- Open access
- closed
- Locations
- IEEE World Engineering Education Conference (EDUNINE) (publishedVersion)
- Topics
- Social Sciences › Education › Artificial Intelligence in Education
- Physical Sciences › Computer Science › Artificial Intelligence › Intelligent Tutoring Systems and Adaptive Learning
- Physical Sciences › Computer Science › Computer Science Applications › Online Learning and Analytics
- Keywords
- artificial intelligence in education, adaptive learning systems, engineering education, academic performance, personalized learning, data privacy, algorithmic bias, teacher preparation
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