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Enhancing Digital Safety: Exploring Parental Control Mechanisms for Secure Online Experiences
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Author(s): Mukesh Kumar (Advanced Centre of Research & Innovation (ACRI), Department of Computer Application, Chandigarh School of Business, Chandigarh Group of Colleges Jhanjeri, Mohali, 140307, Punjab, India), Mirza Shuja (School of Computer Application, Lovely Professional University, Phagwara, Punjab, India), Bilal Ahmed (School of Computer Application, Lovely Professional University, Phagwara, Punjab, India), Tanya Gera (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India), Deepak Thakur (Chitkara University Institute of Engineering and Technology, Chitkara University, Punjab, India)and Vivek Bhardwaj (School of Computer Science and Engineering, Manipal University Jaipur, India)
Copyright: 2026
Pages: 24
Source title:
Integrating Parental Consent and Child Engagement With Digital Protection Rules
Source Author(s)/Editor(s): Romil Rawat (LabGeoInf-Research LABoratory in GEOmatics and INFormation systems, National Research Council in Italy, Rome, Italy), Sanjaya Kumar Sarangi (Utkal University, India), A. Samson Arun Raj (Karunya Institute of Technology and Sciences, India), Janet Olivia Richmond (Karunya Institute of Technology and Sciences, India)and Purvee Bhardwaj (Rabindranath Tagore University, India)
DOI: 10.4018/979-8-3373-2716-7.ch014
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Abstract
As children engage more with digital platforms, they face increasing risks such as cyberbullying, exposure to inappropriate content, and online grooming. Cases like Meta's 2023 investigation into the exploitation of minors highlight the pressing need for stronger online safety tools. This study proposes an AI-based parental control framework that goes beyond basic keyword filtering. Leveraging Natural Language Processing (NLP) and Transformer-based models like BERT and GPT-4, the system enables real-time detection of harmful content and adaptive moderation. Trained using the 2023 Cyberbullying Detection Dataset and YouTube's Open Content Moderation Dataset, the model effectively classifies and responds to harmful interactions on social media and messaging platforms. Testing showed an 89% accuracy rate in identifying harmful content and a 40% boost in real-time intervention over traditional tools. The study underscores the promise of AI-driven safety solutions while stressing ethical use and balanced regulation to protect both privacy and well-being.
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