Work

A Proposed AI-Based Framework for Child Online Protection in the Sultanate of Oman

Year: 2026

Type: conference-paper

Abstract: Children aged 10–18 in Oman face a growing cyber threat landscape—cyberbullying, online grooming, phishing, and harmful content—yet no AI-based protection framework exists that addresses the Omani Arabic dialect context. This paper makes two contributions. First, it presents empirical evidence of the online threat environment facing Omani children, drawn from structured surveys administered to 75 participants (40 children, 35 parents), revealing widespread threat exposure and a statistically significant parental awareness gap (χ² = 7.84, p = 0.020). Second, it proposes a conceptual AI-based protection framework leveraging AraBERT fine-tuned on an annotated Omani Arabic corpus, and defines the evaluation methodology—using Precision, Recall, and F1-Score against keyword-filtering baselines—for planned future deployment. Target benchmark thresholds are derived from comparable Arabic NLP studies. The paper concludes with evidence-based policy recommendations for the MTCIT and the Ministry of Education.

Source: Zenodo (CERN European Organization for Nuclear Research)

DOI: 10.5281/zenodo.20252143

Authors: Al-Shaibi, Marwa Juma, Al-Maamariya, Sheikha Asad, Al-Araimi, Sarah Zuhair

Institutions: University of Technology and Applied Sciences, Muscat, University of Technology and Applied Sciences, University of Technology and Applied Sciences-Suhar

Language: English

Topic: Hate Speech and Cyberbullying Detection

Subfield: Artificial Intelligence

Field: Computer Science

Domain: Physical Sciences

SDG: Peace, justice, and strong institutions

Open Access status: green

OpenAlex ID: W7161499726

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