| Row ID | Full name in Chinese/中文全名 | Full name in English/英文全名 | Gender/性別 | Current Year of Study/目前就讀的年級 |
|---|---|---|---|---|
| 1 | 江寶兒 | KONG PO YI | Female/女性 | Grade 9 (Secondary 3)/中學九年級（中三） |
A significant problem encountered in school settings is distraction caused by mobile phones. Students are constantly bombarded with notifications, social media updates, and messages, diverting their attention from lessons and study. This affects learning by reducing focus, hindering information retention, and decreasing overall academic performance.
This issue extends beyond the classroom, impacting family life as well. Students may prioritize screen time over family interactions and homework, leading to conflicts and strained relationships. In the community, this constant connectivity can hinder face-to-face social skills development and engagement in local activities. Ultimately, the pervasive presence of mobile phones creates a barrier to effective learning, healthy relationships, and meaningful community involvement.
To combat phone distractions in schools, an AI-driven solution can be implemented. AI, using classroom cameras with strict privacy protocols, analyzes student engagement to identify phone-related distractions. A computational thinking-based system then prioritizes students based on distraction levels, triggering personalized interventions such as reminders to use phone lockers. For persistent issues, temporary app restrictions can be implemented with consent. Furthermore, AI can personalize learning content to individual interests, reducing the urge for external distractions. This approach combines AI for detection, computational thinking for tailored responses, and programming for implementation, mirroring AI regulation principles by ethically using technology to enhance learning environments.
The proposed AI-driven solution is optimal because it offers a multi-layered, personalized, and data-driven approach to addressing phone distractions. Unlike blanket policies that can be ineffective or punitive, this solution uses AI to identify the students who are genuinely struggling with focus, allowing for targeted interventions. This minimizes disruption for students who are already managing their phone use responsibly.
Computational thinking ensures the interventions are appropriate to the level of distraction, avoiding overreach. Personalized learning addresses the root cause of some distractions by making the learning experience more engaging. Crucially, the solution incorporates privacy safeguards and parental consent, aligning with ethical AI principles outlined in the PDF.
Furthermore, the data collected can be used to continuously improve the system and inform teaching strategies. This proactive, adaptive, and ethical approach makes it superior to simpler, less nuanced solutions.
Personal Information Collection Statement (PICS)/收集個人資料聲明:
1. The personal data collected in this form will be used for activity-organizing, record keeping and reporting only. The collected personal data will be purged within 6 years after the event.
2. Please note that it is obligatory to provide the personal data required.
3. Your personal data collected will be kept by the LTTC and will not be transferred to outside parties.
4. You have the right to request access to and correction of information held by us about you. If you wish to access or correct your personal data, please contact our staff at lttc@eduhk.hk.
5. The University’s Privacy Policy Statement can be access at https://www.eduhk.hk/en/privacy-policy.
- I have read and agree to the competition rules and privacy policy/我已閱讀並同意比賽規則和隱私權政策。