The “iPad Kid” Epidemic: An Ethical Analysis of AI “Brain-Rot” and Content Farms

CSC 208: Ethics in Technology

Winter 2025

Introduction

Gone are the mornings hunkered in front of a tube TV for Sunday cartoons, or the sick days curled in bed watching the Disney Channel greats; kids’ media is a dead art. What remains is a boneyard of regurgitated content cud and an endless horizon of pure, unfiltered slop; AI slop, to be exact, derived from the content farms that endlessly churn episodes of shows with dead-eyed characters and untraceable plots. The zombies that feed off the sludge that wakes at their knees are toddling, whining, and stubby-handed: the rising Generation Alpha, or as many call them, the “iPad Kids”. Unfortunately, sites like YouTube Kids, which are often marketed under the guise of providing Gen Alpha parents with educational, kid-friendly solutions to cure boredom in the home, are unregulated, allowing content farms to post AI content en masse. Parents are none the wiser, and content farms know it. At best, a child might pick up a few bad habits from AI’s tendency to misinform, but there are far worse outcomes to behold. This ethical analysis and its findings aim to elucidate the dangers of these outcomes and suggest that AI content farms are ethically impermissible under the frameworks of care and Aristotelian ethics, from the vantage points of both the content farms themselves and the parents who provide children with access to AI-generated media. Further, this analysis emphasizes the victimization of children in a mediascape that prioritizes profit and complacency over education. In this essay, a full case study will be presented, which highlights a fictional (yet realistic) family and their reliance on AI content in the household. This case study will lend itself to analyses from two different ethical theories: care and Aristotelian ethics, which will then be critiqued, followed by a personal reflection on the writing process and overall coursework assigned throughout the quarter.   

Full Case Study Analysis

The concept of an “iPad kid” has transcended far beyond the original scope of the term’s definition. Where the “iPad kid” was once a child raised with access to a tablet or other handheld device, the phrase is now inextricably tied to the image of a drooling, glazed-eyed child, wiping snotty fingers onto a cracked iPad as they groggily watch, with sluggish pacificity, whatever content flashes across their screen. It is a terrifyingly dystopian sight to anyone who did not grow up this way to see the child, once representative of youthful vigor and playfulness, reduced to this caricature of what a kid is supposed to present in our society, who now sit in our restaurants and our third spaces. This term “iPad kid” has become so synonymous with this image that it has become an insult in Gen-Z culture: “You’re such an iPad kid.” 

There is one particularly concerning element to this image of the “iPad kid”: the content they consume. Much of this content is derived from social media, specifically YouTube Kids, a video platform designed to cater to young viewers, with restrictions enforced to protect children from consuming dangerous adult content. It works, most of the time (not all the time, mind, as content has managed to slip through the cracks of YouTube’s moderation systems). But what this feature does not protect children from, however, is the consumption of AI content. In an article from the New York Times, more than 40% of videos on YouTube Kids are now AI-generated (Lajka, 2026). This sounds benign on the surface, but when we consider the ramifications of AI bias, generative models’ proclivity toward quick cut content, and unsubstantive plots derived from careless content generation, we begin to see why the term “brain rot”, the idea of an idle form of media that can be consumed without critical thinking, one that “rots your brain”, is being associated with AI, and how these two concepts tie into this generalization of the “iPad kid”, which has become a euphemism for Generation Alpha: children born after the year 2010. 

Why do our AI models create this type of content? There are incredible movie scripts and books out there that are both substantive and defy bias, all of which have been fed into GenAI’s trawl of the internet’s floor. Surely, if AI models just referred back to these movies, they would be able to develop more meaningful content for our newest generation of consumers. The fundamental flaw in this reasoning lies in the philosophy of model collapse. To quote Shumailov and his research team, “'model collapse' (is) a degenerative process whereby, over time, models forget the true underlying data distribution, even in the absence of a shift in the distribution over time” (Shumailov et. al., 2024). While technical, Shumailov’s team vouches that as time goes on, an AI model will go through two phases of collapse: the first, early collapse, will cause an AI model to fall back on the heels of hegemonic data, producing what we are now seeing as bland, uninspired, run of the mill content, because that is all it is able to do: not in spite of the rare gems of innovative content we might unearth in the internet’s swells, but mimicking the internet’s slum of mediocre and sloppy work. 

This will get worse when the AI falls into late collapse, the second stage of Shumaliov’s team’s schema, where generated content becomes indistinguishable. The transition from early to late collapse can be attributed to AI itself, which cannibalizes its own “slop”, a term echoed by Zac Zimmer. He states that because this slop “populates much of the Internet, it is a virtual guarantee that future training data will be populated with some amount of “inference” generated by previous models” (Zimmer, 2026). Incredible movie scripts may exist, but AI is finding it harder and harder to sift through what is good because it has developed a bias toward its own content. Like an ouroboros, it will continue to nip at its tail, producing scripts for children’s TikToks and reels, then feeding itself on its own creations once they are posted and garner positive feedback. This creates a problem when, as Shumailov’s team reports, “over the generations, models tend to produce more probable sequences from the original data and start introducing their own improbable sequences, that is, errors.” (Shumailov et. al., 2024).

We’ve seen this behavior, and have even coined a term for it: hallucinations. AI will continue to provide misinformed answers to questions, then perpetuate that knowledge after the initial error is made. We’ve seen this through Google Gemini, where, in a Quartz article, journalist Maxwell Zeff hunted down these hallucinations. Some of them are funny; Gemini hallucinates a statistic, saying that humans spend “80% of their waking hours plotting revenge”. More concerningly, Gemini reported that Sandy Cheeks, a supporting character in SpongeBob SquarePants, died from a drug overdose on “cocaine, heroin, and alcohol” (Zeff, 2024). It’s frightening to think that a GenAI model like Gemini, which was responsible for attributing a brutal death to a lovable childhood character, is also in charge of creating short-form content for children, alongside other models that suffer from similar hallucinatory behavior. 

Currently, this type of content is generating some conflicting opinions. In a study conducted by Oanh and Duong, the opinions of Gen Z, the generation born between 1997 and 2012, expressed concerns with AI content appearing on their YouTube, TikTok, and Instagram feeds. Gen Z reported feeling distrustful of the content they saw. Participant 24 lamented this, saying, “sometimes, I can’t even tell if it’s real or fake—that’s scary.” (Oanh & Duong, 2025). Gen Z, did, however, have specific caveats where they praised AI, saying that some videos that showed clear creative intent hooked them in. Oanh and Duong stated that characteristics like novelty, creative AI use, and compelling storytelling tended to resonate with Gen Z audiences who consumed AI content. “If it’s all effects with no story, it’s like watching a fireworks show—you enjoy it for a second, but you don’t remember it afterward”, Participant 26 explained in their interview (Oanh & Duong, 2025). These opinions are nuanced, painting a picture of what AI content farms should post if they want to gain traction with young adults. But unfortunately, content farms cater to the most undeveloped minds, ones who are happy to watch bland, uninspired content. One of the participants of this study states, “It’s not just about (AI content) being flashy. The visuals need to serve a purpose. If it’s just chaos, I lose interest” (Oanh & Duong, 2025). 

Children do not discern, however, between flashy content and nuance. In fact, they gravitate toward it. Walsh, a parent of an “iPad kid”, coins this as “orientation response”: “Children are born into the world with a strong reactive attention system. Located deep within their brains, this system alerts them to and helps them focus on sudden changes or novelty in their environments… quick scene changes, loud noises or changes in music, vibrant colors, and visual stimuli all ignite a child’s “orientation response,” triggered by their hardwired reactive attention system (Walsh, 2019). It’s natural for children to seek out chaotic content, the very same that the Gen Z participants in Oanh and Duong’s study, because biology requires them to do so. Content farms capitalize on this hardwired behavior for monetary gain. First, it was through overstimulating yet somewhat-substantive videos like CocoMelon. Now, it’s through nonsensical “AI slop”, to reference Zimmer. 

My ethical case study is hypothetical, but echoes very real ramifications of AI content farm consumption. The parents of three children, ages 2, 3, and 5, gave their children iPads for Christmas. It’s a steep price to pay, but they recognize the technology's longevity and think it might help calm the chaos in their household, since their children are constantly fighting amongst one another. Other parents have touted that their children can stare at their screens for hours without pestering their siblings or parents, and that the abundance of content on these iPads gives children technological literacy and access to educational kids' content. So the parents equip each iPad with YouTube Kids. For a while, the rowdy children are tempered; eerily so, as they begin to neglect their toys and spend longer and longer hours watching YouTube. The parents begin to notice their children acting strangely. First, it’s strange, innocuous phrases. Then, the children begin to apply what they're watching. After watching a video about cars, their three-year-old is convinced that green lights mean to turn right, not drive forward, which became apparent when the child began to spin circles during a game of “red light, green light”. Another begins pronouncing the state Connecticut, where their grandparents live, as “Conmecticut” (Sullivan, 2026). 

Everything comes to a head when the parents leave the three children alone to their devices at the kitchen table one morning, only to return to find their three-year-old attempting to shove their two-year-old sibling into the refrigerator. On their iPad, a video is playing on repeat. It has generated over 61 million views, showcasing a series of quick-cut AI clips featuring anthropomorphic cats. It starts with the unnamed child cat pouring bleach into its mother’s drink. Upon drinking it, she throws up rainbows and faints onto the kitchen floor. Upon waking, she becomes enraged, and throws her child into the kitchen freezer. When her presumed husband comes home and asks where the child is, he runs to the freezer to find that the child is frozen into a solid block of ice. The video ends with him breaking the ice with a hammer (PARALLELGALAXY-TV, 2025). After the situation diffuses, the parents quickly confiscate all iPads, deciding to return to their old way of life… but the damage has already been done. The three children become incredibly irritable, throwing tantrums and refusing to comply with any parental requests unless they are given screen time. Because the parents are used to the silence and peace that the iPads provide, they give the devices back after attempting to implement screen time applications and further restrict content. 

The stakes of this ethical dilemma are no less than the development of our upcoming generation, Generation Alpha, who are most directly impacted by this type of AI content. If AI content remains unchecked, we may begin to see scenarios similar to our ethical dilemma play out, where misinformation guides how children interact with the real world in more insidious ways, to the degree that digital literacy and maturation are stunted. This will, of course, affect the children of our future generations, but may inadvertently lead to the positive revision of Section 230 or other laws protecting companies or creators from pushing misinformative and dangerous content, and may even redefine the current parenting trend of treating iPads as toys. The affected stakeholders are parents, AI companies, content creators, and most importantly, children. From an ethical perspective, this case marks the precipice that the entertainment industry and AI companies currently teeter upon. AI content farms are currently in the “wild west” of their formation, in that no laws truly hold them accountable. AI, while further developed, is in a similar state. Cases like these will define how we treat AI media creation and consumption. 

Our ethical case study also considers fairness and justice. While YouTube Kids and other content farms are readily accessible, they are relied on more heavily by lower-income families, who may not have money for a babysitter or nanny. According to a census conducted by Common Sense Media, “Children in lower-income households are spending nearly two hours more with screens than children from higher-income households.” In a way, these devices offer a cheap stand-in for parent-child quality time, a must when considering that many low-income families rely on a single parent or parents working several minimum wage jobs to get by. It’s strange to consider, since these devices are costly on the front-end, but parents’ opinions on these technologies often outweigh cost. Because many lower-income families are disconnected from the dangers of social media because of a lack of exposure, “Black and Hispanic/Latinx, as well as lower-income parents, are more likely to say the media their kids are viewing has an educational benefit.” (Rideout et. al., 2020). These devices are bought because parents see them as an educational resource, not a source for misinformation and dangerous content. Because lower-income families are more likely to supply children with these technologies, they are at greater risk of being exposed to malicious content; an especially problematic cause-and-effect when considering many of the hegemonic biases apparent in AI generation directly attack individuals of minority groups.

In the coming assignments, we will explore how AI content farms and model collapse fare when tested under different ethical paradigms.

Care Ethics in AI Content Farms

The first ethical theory this paper aims to apply to the provided scenario is feminist ethics. Feminist ethics, more recently labeled “care ethics” by Nel Noddings, emphasizes the “cared-for”, a type of moral recipient with whom we enter a relationship. Care ethics encourages us to consider how our actions lend themselves to our inclinations toward “natural caring”, a term coined by Noddings that suggests our relationships with one another are reciprocal and must be nurtured. Because care ethics has carved its way into the educational sector and holds dependents in high regard, this ethical theory will provide valuable context for how the three children discussed in the case study prompt should be treated as “cared-for” individuals, as well as how all moral agents in this scenario, both the parents and the content farms, should act with the “cared-for” in mind. Note that in this section, I have chosen to refer to feminist ethics as care ethics, to broaden the applicability of the theory as Noddings originally intended, but I do recognize here that this theory is grounded in feminist ideals developed by Carol Gilligan. In Introduction to Ethical Theories: A Procedural Approach, Birsch outlines four steps on how to critically analyze ethical scenarios under the care ethics framework. Below, these steps have been applied to the case study in part one. 

The first step when enacting the procedure of care ethics is to consider the action under consideration. Based on the given scenario, there are two actions that we must study. First, we have the parents, who made a choice to give their children devices based on motivations to pacify a bustling household. In a way, they are the ones who provided dangerous content to their children by creating an initial dependency on the iPads. Their decision not to vet the content their children consumed led to developmental stunting and eventually, a dangerous situation that could have been deadly, if not caught in time. While their intentions were pure, if not a bit detached from the needs of their children, their decision to provide their children with devices opened the floodgates for what was to come (the incident with their child pushing their other child into the refrigerator). 

Equally impermissible in this situation are the content creators, who created AI content consumed by the three children. The content farm’s goal was to make a monetary profit off the engagement and investment of child consumers. They effectively manipulated the minds of the three children in this scenario by creating dangerous yet addictive content to consume, all with just a click of a few buttons. Further, they leeched off the parents’ neglect, using it to put money in their pockets. 

Some may argue that there is a third action that we must account for: the morally impermissible act of the child shoving their sibling into a refrigerator. While the child should be held accountable for their actions to some extent, because several other stakeholders are manipulating the children in this scenario for personal gain, it can be argued that the child’s moral agency is reduced. The three children are victims of a system that exploits their natural desire to seek out overstimulating content, with upbringings that might contribute to how they interpret and act on the content they consume in the real world. Even though the child committed an act that went against care ethics when they pushed their sibling into the refrigerator, because the child is a dependent themselves who has yet to fully develop into a functioning adult that can discern right from wrong, we still consider the guilty child as the “cared-for” in this scenario, while also acknowledging the fault in their actions. For that reason, I will be focusing on the decisions of the parents and the content farm in this section.

Now that we have our two actions, the second step in the moral theory of care ethics is to consider the three elements of ethical caring: apprehending the other’s reality, engrossment, and motivational displacement. Based on these three elements, we can then consider what is best for the cared-for. 

First, for the caregiver to apprehend the cared-for’s reality, they must place themselves in the shoes of the cared-for and consider their needs. Both the content farms and the parents are able to apprehend the cared-for’s needs, in this case, the needs of the children, far more than the parents in this scenario. In fact, the content farm apprehends the children’s needs too well. This is better understood when we understand the innate needs of children during the 2-5 stage of development. Children do not innately crave device time; they crave affection and stimulation. At the beginning of the provided scenario, it is noted that the children acted out in the household by fighting with each other, and this is the initial reason that devices were provided. While the parents didn’t recognize this at the time, in-fighting is a form of communication, albeit poor, to the parents that a need wasn’t being met, and that the children were understimulated to a degree that they bided their time by fighting amongst one another, likely to garner the attentions or affections of an otherwise absent set of parents. In some cases, in-fighting amongst siblings is beneficial, as it creates conflict resolution skills and opens the child up to develop empathy as they grow into teenagers and adults. But instead of redirecting this behavior when it became toxic by providing opportunities for healthy interaction amongst the three siblings, encouraging time in solitude when appropriate, or spending more time with their children, the parents gave the three children devices, in the hopes that technology would parent for them. 

The parents’ decision to give the children devices as a response to their arguments is clear proof that the parents did not fully comprehend their children’s reality. The parents give the children the devices because they are frustrated with how the children interact with one another, and it is insinuated that they are frustrated with how parenting is a hands-on role, but fail to innately recognize that understimulation and parental absence were the source of the problem. Furthering this point, the parents did not consider healthy ways in which stimulation could have been provided, in ways that might have legitimized the children’s needs for parental affection and interaction. They did not consider how technology is an addictive form of media consumption, nor did they research healthy ways to introduce their children to content consumption (i.e., educational broadcasts like PBS Kids), which would have fulfilled a need for stimulation while also apprehending a way of life for their children that would allow them to develop into healthy media consumers as they grew; instead, they set their children up to be social media addicts. In the scenario, the parents speak with other parents of iPad children, who speak on how these devices are educational and cater toward developing media literacy, and yet we see no effort made on behalf of the parents to create this digital environment for their child. A parent who apprehended their child’s reality would conduct this research, then set screen time limits to develop healthy use habits, which would recognize the child’s needs while also protecting them from overexposure.

On the other hand, the content farm far more intuitively understands a child’s need to be stimulated; in fact, they capitalize on this knowledge. Videos are created with stimulation in mind, often to the degree that these videos become overstimulating for children. Content farms make money when parents do not apprehend their child’s reality, because it gives them a chance to step in and parent the child themselves, often in insidious ways. When a parent gives their child an iPad with unregulated child protection systems in place, content farms are able to make a profit. While the content farm understands the child’s needs more than the parent, similar to the parents in this scenario, the content farm fails to orient itself in ways that benefit the child (like vetting videos to stop the spread of misinformation or safeguarding videos to keep dangerous ideologies and practices, like placing children in refrigerators, from being posted). 

Both caregivers must then consider engrossment, which is a moral agent’s desire to uplift the cared-for’s well-being. Here, we see the parents attempt to uplift their children’s well-being by providing devices to curb sibling fighting. While misguided, the parents in this scenario believe they are helping curate a more peaceful household. Because they did not conduct research into children’s media and safe sites, they are unaware that the content they are providing their children with is unsafe, but they are once again misguided under the principle that the videos their children are watching are educational in nature, as other parents have discussed. From an outside perspective, this attempt at engrossment is shallow, and may be influenced by other factors, as we discuss later in the section.

Meanwhile, the content farms have very little interest in engrossing themselves in the lives of the cared-for. Because of the hands-off nature of platforms like YouTube Kids, content farms have little stake when it comes to the livelihoods of their viewers. If a video makes money, content farms will post it. Because media platforms naturally post videos that are polarizing, divisive, or shocking, content farms will gravitate toward that type of content (which is exactly why the video in the ethical case study scenario has more than 60 million views), without considering how this type of content may influence younger viewers.

Motivational displacement asks the moral agent to consider if the well-being of the cared-for is the caregiver’s highest priority. For both the parents and the content farms, this is not the case. For content farms, this is obviously the case, as they fail to consider the well-being of their child viewers in the first place. Clearly, their priority is to make a profit, which misaligns with any attempts at motivational displacement on their end. For the parents, this is more ambiguous, but judging by how the parents are more concerned with sibling conflict and its relation to “keeping the peace” in the household, instead of figuring out the actual underlying reasons why sibling conflict may be repeatedly occurring, we can assume that the parents care more about pacificity then their children’s needs, especially when we consider how quickly they decided iPads were the solution without conducting any research or finding any middle ground for their children’s use. 

The third step asks us to consider if the moral agent who is performing the action is harming the cared-for. Very clearly, both the content farms and the parents are harming the children in this scenario. The parents are harming the cared-for by providing unrestricted access to the internet, which allows children to access dangerous or misinforming content. The content farms are harming the cared for by creating this dangerous content, which they post for profit without any consideration for their viewer’s ages. 

Finally, in step four, an ethical conclusion is developed based on what is best for the cared-for’s well-being, and if this conclusion interferes with the cared-for’s basic well-being. It’s easy to see that both the parents and the content farms are acting in the cared-for’s best interest, and that both conclusions they reached directly interfere with the cared-for’s well-being. Both are complicit in how the refrigerator incident transpired in this scenario. While it is admirable that the parents have pledged to do more research and limit screen time for their children, this action is too little too late, as the children will now most likely demand unregulated use, similar to how they demanded renewed screen time after the refrigerator incident. As we saw, the parents ultimately caved and returned the children’s screen time, and there is a high likelihood they will cave again, opening their household once again to the negative influences of unmoderated internet access. 

In summary, the parents do not fully apprehend their children’s reality, but are practicing very misguided engrossment. The content farms, meanwhile, apprehend the children’s reality, but do not practice engrossment. Neither the parents nor the content farms practice motivational displacement. Both moral agents are harming the cared-for, and both conclusions that the moral agents arrived at based on their personal goals (for the parents, to keep peace, and for the content farms, to make money) directly interfere with the cared-for’s basic well-being. Neither the parents nor the content farms are acting in line with what care ethics dictates, and it can be assumed that, because this is the case, both are failing to nurture their relationships with the children in this scenario. Parents have an obligation to protect their children, just as content creators have an obligation to protect their viewers, and both parties in this scenario are grossly failing at fulfilling these roles. We can see this neglect through how the relationship between child and parent, as well as the relationship between child and content creator, has ultimately been neglected in ways that negatively impact the child.

Aristotelian Ethics in AI Content Farms

The second ethical theory this paper aims to apply to the provided scenario is Aristotelian ethics. Derived from the ancient Greek philosopher, this theory, also known as virtue ethics, is outlined by several key concepts that attempt to give purpose to human existence, which aptly cover the core values behind Aristotle’s ideas. Aristotle argues that purpose is not innate; rather, it is obtained through arete: a way of life that asks individuals to seek a purpose and fulfill it. This is done by following a lifestyle that reflects the Golden Mean, a middle ground between the deficiencies and excesses of vices (one example is courage: too much is considered recklessness, while too little is cowardice. Individuals are encouraged to strive for a middle ground between these two negative character traits). Phronesis, or practical wisdom, is an innate form of knowledge that allows us to do the right thing at the right time. To reach the golden mean, one must use Phronesis; simply knowing what course of action is best is not enough. If all of this is done, an individual will reach eudaimonia, a state of immense happiness and feelings of fulfillment. Aristotle argues that eudaimonia and the journey to attain it are the purposes of life. 

Because of this theory’s more narrow scope, we can apply concepts like arete and the Golden Mean to our scenario in very specific ways. In Introduction to Ethical Theories: A Procedural Approach, Birsch outlines three steps on how to critically analyze ethical scenarios under the virtue ethics framework. Below, these steps have been applied to the case study in part one. 

The first step is to identify the action under consideration. While this situation has already been outlined both in the full case summary and the analysis of care ethics, the situation will be briefly identified once more for cohesion. Two specific actions raise red flags according to Aristotle’s virtue ethics: the parents’ decision to provide their children with technology, and the content farm’s decision to produce dangerous content. 

Once again, we must put into question whether the child is a moral agent in this scenario. Aristotle may hesitate to label them as such. In order to be a moral agent, Aristotle argues that you must be able to practice phronesis, or practical wisdom, as discussed above. Children are unable to do this due to a lack of life experience. As they develop, this wisdom is nurtured by parents and shaped by the world around them. With this in mind, we will consider the agency of the child while also bearing in mind their dependency on their parents and their lack of phronesis.

The second step is to determine the relevant virtue, vices, and the golden mean. First, the relevant virtue for the parents and their decision to give their children technology would fall under parental responsibility. As parents, they have a duty to protect their dependents from harm, whether in the digital or physical world. A lapse in protection is negligence, and an excess of protection is authoritarianism. The golden mean between these two vices would be authoritativeness, where parents protect a child from danger while entrusting them the independence to explore unfamiliar settings on their own. Below, we see this golden mean visualized linearly. The arete is stated at the top of the graph in italics, with the deficiency, standard, and excess of the golden mean listed underneath. The star visually represents the mean, while the dot represents where the parents fall on this scale. In step three, we will discuss why the dot was placed in the deficiency zone.

Second, the relevant virtue for the content farms and their decision to create harmful content would be freedom of expression. Too much of this is anarchy (while this is a broad term, it is used here in reference to how a complete lack of TOS or regulation leads to hate speech and misinformation) while too little is censorship. A middle ground between the two is content moderation, where freedom of speech is exercised to create content, while also enacting restrictions that safeguard children from dangerous content consumption. Below, we see this golden mean visualized linearly. The arete is stated at the top of the graph in italics, with the deficiency, standard, and excess of the golden mean listed underneath. The star visually represents the mean, while the dot represents where the content farm falls on this scale. In step three, we will discuss why the dot was also placed in the deficiency zone.

Finally, we have the children in this scenario. The virtue at stake here is media consumption. Too much consumption leads to overconsumption, while a lack thereof is disconnectedness. This deficiency, when we consider that the moral agent is the child, is not inherently as much of a vice as the scale may make it out to be. While adults are reliant on devices to keep up with friends, family, and work, children are at liberty to live a very hands-off life, with most of their interaction being face-to-face, on the playground, or at school. Children do not have obligations, so the term “disconnectedness” does not hold the same weight that it would if this scale were being used to measure an adult moral agent. Many parents would argue that, because of the open nature of the internet, complete disconnectedness should be the standard for child social media use, not a deficiency. The optimal mean for a child operating on this scale would be moderated use, where parents step in to teach their child what types of technology use are safe, and which ones they are prohibited from using. Below, we see this golden mean visualized linearly. The arete is stated at the top of the graph in italics (even though it is counterintuitive to label a child as a moral agent with an arete, because they do not have the phronesis to attain such an arete), with the deficiency, standard, and excess of the golden mean listed underneath. The star visually represents the mean, while the dot represents where the child falls on this scale. In step three, we will discuss why the dot was also placed in the excess zone.

The third and final step asks us to reach a moral conclusion. For all three moral agents, we must consider their arete in order to determine if they are operating in ways that fulfill their arete and lead them toward eudaimonia. In the case of the parents, their arete is to provide protection and care for their children. The content farm’s arete is to produce content that makes them money, but also stimulates children in a safe, educational way. As discussed, the child does not have an arete, as they are still under the care of their parents. This makes the consideration of a child’s media consumption and its relevance to any moral conclusion moot, as media consumption becomes the parents’ responsibility if they want to fulfill their own arete. While it is true that the children are engaging in the excess vice of overconsumption in how their brains have adopted the malpractices they have seen on their devices (more benign, like incorrect pronunciations of states, to more grave, shoving a sibling into a refrigerator), the fault lies with the dependent’s caregivers for not regulating digital media consumption, especially at such an early age. 

In both the case of the parents and the content farm, we can attribute their actions in the scenario to the vice of deficiency. In the case of the parents, they are failing to fulfill their arete of parental responsibility by allowing their children to swing into the vice of excess in their rate of consumption. The ideal parent would monitor their child’s device for any misuse and teach their child about digital literacy. The parents in this scenario do none of this and give unrestricted device access to their children without much second thought. The content farms, similarly, fall into a deficient state of freedom of speech, this being anarchy, by allowing the perpetuation of hateful rhetoric, dangerous content, and misinformation on sites strictly meant for child consumption. When we consider that model collapse continues to cannibalize this type of content and perpetuate its often unsafe ideals, we see the golden mean continue to creep farther toward anarchy than censorship. An ethical content farm (as paradoxical as it sounds) would produce content with themes and quality appropriate for a child consumer. If a content farm caters its content toward children, it must be particularly selective with the type of content it produces.  

In summary, both the parents and the content farm fall into the deficiency zone of their respective golden means relative to their individual arete. While the arete of the child was considered, it was then noted that because children do not have fully developed arete, their efficacy as a moral agent is diminished, if not completely removed; any arete identified would fall on the shoulders of the parents, whose duty is to help nurture a child into developing their own arete as they reach adulthood. Both the parents and the content farm have a duty to do this, and both do so in a negligent manner. 

Critique of Analyses

Despite clear differences in Aristotelian and Feminist ethics, both analyses concluded that both moral agents in this scenario, the parents and the content farm, are accountable for their own actions and, in some way, have acted unethically. The ways that these analyses arrived at this conclusion are nuanced, but actions can be taken to strengthen the content further.

We are asked to consider whether another ethical analysis would have strengthened this paper’s argument, but I am inclined to disagree, even though Feminist and Aristotelian ethics offer unique perspectives to the table. For reference, feminist ethics opens a door into a discussion about child development and the importance of parental presence during formative years, while Aristotelian ethics focuses more on the moral agents themselves and their duty to their arete, or their higher purpose in life. While the argument can be made that this paper may have benefited from a contrasting ethical theory that holds the parents or the content farm in a positive light, like egoism (a theory that both parties play into perfectly, seeing as they act with their own self-interest in mind), this framework fails to acknowledge a society built on the caring for and nurturing of children. To remove Feminist or Aristotelian ethics would lessen the strength of this paper’s argument on the impermissibility of the moral agents’ actions in relation to the child victim.

That being said, there is a third moral agent that could have been considered throughout the course of this paper: the AI tools themselves. One weakness of the analysis is that these tools, or the companies that created them, were not presented. Even though AI tools and their ability to act as a moral agent (similar to children) are ethically dubious, a paragraph on how AI operates on common-crawled data, making them unreliable creators for the content farms to rely on, may strengthen the negligence of the content farms while also shifting blame to the companies that let these tools run unfettered.

As far as what may strengthen the already existing content within the analyses, the Feminist ethics section would benefit from more discussion on how content farms apprehend the children’s situation. This would be a great section to dive into how monetization works, and how there are entire digital cultures dedicated to teaching individuals how to make a profit on videos using visual rhetoric. Overstimulation is one of these tactics. On the other hand, this would also be a great section to mention AI, as suggested in the last paragraph. There is a level of plausible deniability that creators can claim when they use AI, because AI naturally gravitates toward these quick cuts that overstimulate a child’s mind, simply because it cannot generate longer-form content. Similarly, there is room here to mention other ways that content farms pull children in outside of overstimulation, like using franchised characters, different colors, or leveraging the algorithm so that their videos are viewed as frequently as possible. 

Regarding the Aristotelian ethics section, while strong, more analysis of the graphs would help strengthen the overall argument. Because this framework only has 3 steps, there is plenty of room for more analysis. While the charts offer a powerful visualization of the golden mean, in the content farm section, a more detailed description of why content farms fall in the deficient zone on the content moderation golden mean scale and what this means for their viewers would help strengthen the overall conclusion of the section.

Overall, while the analyses work, more commentary on content farms would help even out the conclusions. This paper aims to tie in concerns regarding model collapse, but this is only very briefly touched on in one of the analyses, and content farms give this paper an opening to discuss this issue and other issues regarding the ethical loopholes that content farms weaponize in their day-to-day operations. 

Conclusion

AI content farms are not just ethically impermissible, but their creation and provision actively neglect a child’s basic needs for healthy stimulation and parental connection. Through the framework of care ethics, it was discovered that parents, while attempting to engross themselves in the lives of their children, do not fully understand the dangers of AI-generated content (which is impervious to bias, misinformation, and at times, the generation of dangerous material). Subversively, content farms actively use AI models to generate harmful content, then choose to post anyway for profit, a decision that clearly showcases a neglect to engross themselves in their viewers’ livelihoods. Both approaches fail the cared-for in this situation, making the content farm and the parents’ actions ethically impermissible. Equally, under the framework of Aristotelian ethics, there is a clear neglect to perform their arete, with the parents leaning into negligence and the content farms leaning into anarchy. The two outcomes of these two ethical analyses insinuate that, unfortunately, providing and creating AI content for child-oriented content farms is inherently neglectful. While “iPad kids” are often tagged with a bad reputation, these attitudes should instead be cast onto the content farms themselves or the parents who provide children with access to this type of media. Overall, both parties should be held to a higher standard to help educate a future generation through meaningful connections with human-vetted content. There is a particular exigency to regulate content farms and child access to AI content on the federal level (or, if this fails, create education programs for parents), seeing as content farms are currently left unfettered outside of company policies and terms of service. This becomes an issue when considering how children are naturally addicted to overstimulating patterns like those found in AI videos; videos that contribute to an epidemic of misinformation. AI content should be limited, and yet no matter where you turn, you’ll always see a kid with an iPad at a restaurant, or a stroller, or even commenting an excess of emojis in an AI video comment section, much like the one discussed in the case study provided. But not all hope is lost: to great success, we’ve already seen countries like Australia ban social media for children under 16 (Livingstone, 2026), and measures like these would not only work wonders for stateside children but may provide a much-needed push to curb AI content farms for good. If that happens, perhaps kids’ media might not be a dead art after all. Children shouldn’t have to settle for AI content, and once we cut the slop from our kids’ algorithms, the “iPad kid” will be free to be a kid again: carefree, bright-eyed, and most importantly, human. 

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