Access to the internet is more available than ever before for small children and adolescents, along with an increasing number of channels for using Generative Artificial Intelligence (GAI). For parents of children and teens, this is a new frontier with innovative tools, terminology, and effects that test the integrity of existing parental mediation strategies for modern media. The lack of research aimed at parental awareness of GAI or how this tech can influence children’s well-being led us to fill this current gap and gain valuable insights for future use. The present study explores the current state of parental awareness regarding GAI and its effects on the well-being of children and what mediation strategies parents employ to mitigate these effects. By using the Parental Mediation Theory (PMT) as a theoretical framework, patterns gathered through conducting semi-structured interviews with parents (N=10) are identified with thematic analysis. Through these interviews, themes are uncovered that shed light on how parents perceive GAI in the context of the effects that such technology has on their children, as well as how it could impact their children’s well-being in the future. The conclusion of this study reveals that while most parents know about GAI, many parents are not aware of the less-familiar effects of this technology being used for media manipulation, chatbot companionship or educational assignments that can have a potentially negative impact on the well-being of their children. Stemming from the PMT, a new parental mediation strategy emerged from an analysis of the collected data. This strategy is called ‘planned mediation’ and it serves to proactively protect children from GAI and its less-familiar effects, rather than responding reactively with the mediation strategies that currently exist.
Identifer | oai:union.ndltd.org:UPSALLA1/oai:DiVA.org:hj-64877 |
Date | January 2024 |
Creators | Abel, Chandler, Magnusson, Marie |
Publisher | Jönköping University, Tekniska Högskolan |
Source Sets | DiVA Archive at Upsalla University |
Language | English |
Detected Language | English |
Type | Student thesis, info:eu-repo/semantics/bachelorThesis, text |
Format | application/pdf |
Rights | info:eu-repo/semantics/openAccess |
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