Author
Listed:
- Teresa Chara-De lo Rios
- Beymar Solis-Trujillo
- Jhon Perez-Ruiz
- María Aquije-Mansilla
Abstract
In an era where technology facilitates both the generation of information and misinformation, it is crucial to equip students with critical thinking skills. This study aims to systematically review the role of artificial intelligence (AI) in fostering critical thinking, exploring its effectiveness, methodologies, and implications in educational contexts. A systematic literature review was conducted following PRISMA guidelines. Relevant peer-reviewed articles published in the last decade were sourced from databases such as Scopus, Web of Science, and IEEE Xplore. The inclusion criteria focused on studies that analyze AI-driven tools, techniques, and interventions designed to enhance critical thinking in students. The findings indicate that AI-based approaches, including machine learning algorithms, natural language processing, and intelligent tutoring systems, can support the development of critical thinking by providing personalized feedback, facilitating argument analysis, and detecting misinformation. However, challenges such as ethical concerns, biases in AI models, and accessibility issues remain significant barriers. The study provides insights for educators, policymakers, and AI developers on how to effectively integrate AI-driven tools into educational curricula. It also highlights the need for interdisciplinary collaboration to ensure that AI fosters rather than hinders critical thinking development. AI has the potential to enhance critical thinking skills in educational settings, but its implementation must be carefully designed to address ethical and technical challenges. Further research is needed to assess long-term impacts and to develop more inclusive and unbiased AI-based educational frameworks.
Suggested Citation
Teresa Chara-De lo Rios & Beymar Solis-Trujillo & Jhon Perez-Ruiz & María Aquije-Mansilla, 2025.
"Systematic review of critical thinking using artificial intelligence,"
Edelweiss Applied Science and Technology, Learning Gate, vol. 9(3), pages 990-1001.
Handle:
RePEc:ajp:edwast:v:9:y:2025:i:3:p:990-1001:id:5405
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