How Can AI-Enhanced Case-based Learning Improve Problem-Solving in Cyberbullying Education? : A Literature Review

Mihnatuz Zahroh; Andi Kristanto; Utari Dewi

doi:10.33394/jtp.v10i2.14704

How Can AI-Enhanced Case-based Learning Improve Problem-Solving in Cyberbullying Education? : A Literature Review

Mihnatuz Zahroh, Andi Kristanto, Utari Dewi

Abstract

Cyberbullying poses a significant challenge to students’ psychological well-being, academic success, and social interactions. Traditional educational models struggle to equip students with the problem-solving skills to address this issue effectively. Existing research highlights the benefits of case-based learning (CBL) in developing problem-solving, yet little is known about how AI-enhanced CBL can specifically support cyberbullying education. This study conducts a literature review to analyze the potential of AI-enhanced CBL in strengthening students’ problem-solving skills in cyberbullying scenarios. Using a thematic synthesis approach, relevant studies from 2020 to 2025 were reviewed, focusing on AI applications in cyberbullying education, the effectiveness of CBL in fostering problem-solving skills, and AI-enhanced CBL's role in improving student problem-solving. Findings indicate that AI-enhanced CBL offers interactive case simulations, real-time feedback, and adaptive learning pathways, leading to improved analytical reasoning and decision-making in cyberbullying situations. Integrating AI, particularly large language models like ChatGPT-4, enhances engagement and scalability while fostering problem-solving abilities. These insights have significant implications for educators, policymakers, and researchers seeking to implement AI-driven learning models that prepare students for the complexities of digital interactions.

Keywords

AI-enhanced learning, Case-based Learning (CBL), Cyberbullying education, Problem-solving skills, ChatGPT-4, Digital safety, Active learning, Educational technology.

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DOI: https://doi.org/10.33394/jtp.v10i2.14704