The rapid proliferation of digital technology has fundamentally transformed the domestic environment, introducing new variables into the complex equation of child-rearing. Among these variables, parental mobile phone addiction—often characterized by continuous partial attention and digital distraction—poses a significant risk to the quality of parent-child interactions. These interactions are foundational for healthy cognitive and socio-emotional maturation. Traditional observational methods in developmental psychology struggle to capture the real-time nuances of this digital interference and face severe ethical limitations when attempting to study the long-term impacts of neglectful behaviors. This paper proposes a novel computational framework that leverages large language models (LLMs) and advanced text mining techniques to safely simulate, quantify, and analyze the developmental consequences of parental mobile phone addiction. By decoupling conversational intent from attentional capacity, the proposed pipeline allows researchers to map simulated digital distraction to predicted developmental trajectories, offering a scalable, ethically sound approach to understanding modern family dynamics in the artificial intelligence era.

The rise of digital technology, particularly mobile phones, has fundamentally altered family dynamics. Parental mobile phone addiction poses a significant risk to the quality of parent-child interactions, which are crucial during early developmental stages.

Neugnot-Cerioli, Mathilde, & Laurenty, Olga Muss (2024). The Future of Child Development in the AI Era. Cross-Disciplinary Perspectives Between AI and Child Development Experts. https://arxiv.org/pdf/2405.19275v1 https://arxiv.org/pdf/2405.19275v1

Ye, Frank Tian-fang, & Gao, Xiaozi (2024). Simulating Family Conversations using LLMs: Demonstration of Parenting Styles. https://arxiv.org/pdf/2403.06144v1 https://arxiv.org/pdf/2403.06144v1

Sepahpour-Fard, Melody, Quayle, Michael, Schuld, Maria, & Yasseri, Taha (2023). Using word embeddings to analyse audience effects and individual differences in parenting Subreddits. https://arxiv.org/pdf/2303.12759v3 https://arxiv.org/pdf/2303.12759v3

Sepahpour-Fard, Melody, & Quayle, Michael (2022). How Do Mothers and Fathers Talk About Parenting to Different Audiences?: Stereotypes and Audience Effects: An Analysis of r/Daddit, r/Mommit, and r/Parenting Using Topic Modelling. https://doi.org/10.1145/3485447.3512138 https://doi.org/10.1145/3485447.3512138

Tajrian, Mehedi, Rahman, Azizur, Kabir, Muhammad Ashad, & Islam, Md Rafiqul (2024). Analysis of child development facts and myths using text mining techniques and classification models. Heliyon, 2024, 10 (17). https://doi.org/10.1016/j.heliyon.2024.e36652 https://doi.org/10.1016/j.heliyon.2024.e36652

Xu, Yongxin, Zhang, Ruizhe, Jiang, Xinke, Feng, Yujie, Xiao, Yuzhen, Ma, Xinyu, Zhu, Runchuan, Chu, Xu, Zhao, Junfeng, & Wang, Yasha (2024). Parenting: Optimizing Knowledge Selection of Retrieval-Augmented Language Models with Parameter Decoupling and Tailored Tuning. https://arxiv.org/pdf/2410.10360v3 https://arxiv.org/pdf/2410.10360v3

Lin, Ya-Fang, Li, Xiaotian, Huang, Wan-Hsuan, Prabavathi, Charan Pushpanathan, Cai, Jie, & Carroll, John M. (2025). Parental Collaboration and Closeness: Envisioning with New Couple Parents. https://doi.org/10.1145/3715336.3735837 https://doi.org/10.1145/3715336.3735837

Croeser, Sky, & Eckersley, Peter (2019). Theories of Parenting and their Application to Artificial Intelligence. Also in Proc. AI, Ethics and Society 2019. https://arxiv.org/pdf/1903.06281v1 https://arxiv.org/pdf/1903.06281v1

E., Derrick G., P-Y., Chen, T., van Leeuwen, V., Lariviere, & R, Sugimoto C. (2021). The academic motherload: Models of parenting engagement and the effect on academic productivity and performance. https://arxiv.org/pdf/2108.05376v1 https://arxiv.org/pdf/2108.05