⚡ Quick Summary

This study developed and validated the Artificial Intelligence – Digital Life Balance Scale (AI-DLBS), a psychometric tool designed to assess the multidimensional impact of digital technologies on individuals’ well-being. The scale demonstrated a strong internal consistency and reliability, offering significant potential for mental health research and clinical practice.

🔍 Key Details

• 📊 Sample Size: 1,184 university students in Turkey
• 🧩 Dimensions Measured: 5 key dimensions of digital life balance
• ⚙️ Technology Used: ChatGPT-4 for scale construction
• 🏆 Model Fit Indices: RMSEA = 0.06, CFI = 0.90
• 🔍 Internal Consistency: Cronbach’s α = 0.68-0.87

🔑 Key Takeaways

• 📊 AI-DLBS is a reliable tool for assessing the impact of digital technologies on well-being.
• 💡 The scale measures psychological, social, physical, and academic effects of technology use.
• 👩‍🔬 Strong internal consistency indicates the scale’s reliability in various contexts.
• 🏆 The six-factor structure explains 60.83% of the variance in responses.
• 🌍 Implications for mental health include evaluating risks like anxiety and social isolation.
• 🔮 Future research should validate the scale across diverse populations.
• ⚖️ Ethical considerations include potential data bias risks in AI applications.

📚 Background

The rapid integration of digital technologies and artificial intelligence (AI) into daily life has raised concerns about their impact on individuals’ well-being. Understanding how these technologies affect psychological, social, physical, and academic aspects of life is crucial for developing effective interventions and policies. The AI-DLBS aims to fill this gap by providing a comprehensive assessment tool.

🗒️ Study

Conducted with a total of 1,184 university students in Turkey, this study utilized a convenience sampling method to gather data. The researchers employed ChatGPT-4 to construct a 40-item scale that measures five dimensions of digital life balance, focusing on the frequency and duration of digital device use, psychological and social effects, physical health impacts, academic performance, and technology access and dependency.

📈 Results

The exploratory and confirmatory factor analyses revealed a robust six-factor structure, explaining 60.83% of the variance in the data. The model fit indices were acceptable, with an RMSEA of 0.06 and a CFI of 0.90. The scale demonstrated strong internal consistency, with Cronbach’s α values ranging from 0.68 to 0.87, indicating its reliability for use in various settings.

🌍 Impact and Implications

The AI-DLBS presents significant potential for psychiatric research and clinical practice. By enabling mental health professionals to evaluate technology-related risks such as anxiety, social isolation, and dependency, the scale can inform the design of targeted interventions, including digital detox programs. The innovative use of AI in developing this scale also highlights the need to address ethical challenges, such as data bias risks, in future applications.

🔮 Conclusion

This study underscores the importance of assessing the impact of digital technologies on well-being through the AI-DLBS. With its demonstrated reliability and validity, the scale can serve as a valuable tool for mental health professionals and researchers. As we move forward, further validation across diverse populations will be essential to enhance its applicability and effectiveness in addressing the challenges posed by our increasingly digital lives.

💬 Your comments

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