Children Conversational Training Data for Machine Learning

While I have written quite a bit about the potential uses of a chatbot in educating young children, I am not the first person to ever get the idea. Indeed, the limitations in this specific application do not seem to be idea-based primarily, but instead based on other practical factors.

One such limitation, at the very least a limitation for smaller entities and startups creating chatbots, is a lack of publicly available annotated conversations (training data) by young children. Such data is essential to train NLP tools to correctly identify the meaning behind early childhood language. Without the data, any chatbot geared towards young children would not be very useful, since without understanding the the purpose of the child’s words, it would fail to give an adequate response no matter how well thought out that response is itself.

While there are many pieces of children conversational data lying around the internet, several factors make many inapplicable for practical usage. First are university ethics guidelines, which usually state that conversational data from children must be collected specifically for research as opposed to being simply sold to research as an afterthought. Then, such data must be cleaned up and/or transcribed, which is again harder in the case of messy/unintelligible children. In addition, with children, small age differences have big implications for speech. Hence, it’s essential that any dataset has metadata including child age (or be limited to a small age bracket altogether). Gender could potentially be relevant as well.

“A surprisingly small number of corpora have been produced which specifically contain child and/or teenage language”

Children Online: a survey of child language and CMC corpora (Baron, Rayson, Greenwood, Walkerdine and Rashid)

Even accounting for these challenges, one study finds that a “surprisingly small number of corpora have been produced which specifically contain child and/or teenage language.” It is worth noting that this study’s focus was skewed by their specific application of “protection of children online” and their status as a British university, meaning that datasets that were otherwise pretty valid but were mostly of Americans had that listed as a con, when in reality, it might be a good thing to have a chatbot most fluent in a relatively generic, American vernacular. However, on the flip side, it might not have emphasized enough the lack of younger-child focused datasets (many were broadly K-12 or only late teen).

One corpora that I found separately but was also mentioned in the study was CHILDES, a database of children primarily 5 and younger. It stood out to me for the breadth of data and the precise age-range for the conversations, while not finding the low amounts of British English speakers to be as much of an initial problem as the researchers did. I will certainly explore this corpora further and start training with it.

Looking Behind the Surface for Child-Oriented Chatbots

Previously, I mentioned how a chatbot designed for children has to treat its interactions fundamentally differently than one made for adults. The exigence of a communication between adult and robots, in most cases, “I need help” or “I was re-directed here instead of human support”, is different from the exigence of most child-robot communications, where a child can’t be reasonably expected to try to get anything out of what he or she probably sees as a conversation with a robotic friend. However, this makes the job of a child-oriented chatbot all the more challenging when attempting to deal with or otherwise account for emotional issues of a child.

Of course, this somewhat applies to normal chatbots. One previous example was Woebot, aimed at psychological health. However, the website mentions that Woebot establishes “a bond with users that appears to be non-inferior to the bond created between human therapists and patients.” This implies that at least in part, Woebot gauges emotion due to the patient explicitly stating his/her emotions as would happen in a therapist / patient relationship. Indeed, the exigence of the bot is being downloaded specifically for the purposes of mental health.

Child-oriented chatbots wouldn’t have this same luxury. Even disregarding the fact that not many children I know can adequately express their feelings if they wanted to, if a chatbot adopts a persona of a friend or mentor, it would be more difficult to establish a need to express feelings since children wouldn’t talk to the bot in a non-casual way. While a chatbot can always just ask “how are you feeling?”, this most likely wouldn’t yield accurate results all of the time (imagine asking this question yourself). Instead, a chatbot would have to imply emotions based on the language used.

Given adequately labelled data, natural language models can identify both stress levels and emotion in text. However, it’s unclear if the same method used in the study can be used for the language of young children, especially since with a decreased vocabulary (meaning less emotionally-charged meanings), a lot of human ability to interpret the emotions of young children (for me anyways) is based around non-verbal cues and vocal inflections that can’t be fed into a chatbot.

The Simple Things of Education

Sometimes, nurturing and educating a child can seem more like an enigma or a theoretical ideal than an achievable goal. This is felt on an individual level, when thousands of books, magazine, and commentators float around offering contradictory advice, but it could also seem that way on a societal level. It’s a common fact that the socioeconomic status of a child is the top predictor in his or her educational outcome. Coupled with how intrinsic inequality in SES is in our society, it could seem impossible to fix educational deficiencies on a societal level.

However, despite these massive and seemingly overwhelming correlations, there are many simple actions that can be done on a personal parent to child level that have huge impacts on life outcomes. A post-WWII study in Britain, tracking some 14,000 babies born in 1946, unveiled several basic actions that led to disproportionately successful outcomes for their recipients. These include talking and listening to kids, teaching letters and numbers, reading to kids, and maintaining a regular bedtime.

Perhaps some of the given suggestions, such as taking children on excursions outside, could only be achieved through more active parenting. However, many of these can be supplanted with external help. A chatbot (such as the one I am developing) can remind children of bedtimes easily using pre-existing notification concepts. A little more development, and it can incorporate teaching of letters and read stories. Using contextual AI, it has a good shot of talking and listening in a roughly similar manner to an adult.

Much of this sentiment is echoed in the development of the new mentor robot Moxie. Already, it functions much like an actual human both physically and in social interactions. However, if we focus all our attention on visibly sci-fi concepts such as these, we risk losing the idea of new technology allowing us to reduce inequities and improve educational outcomes for everybody through increased accessibility. Indeed, first developed as a specialist solution for kids with special needs, Moxie is now on the general market for a price of $1699, on the market for kids whose parents can afford to spend that kind of money on unproven technologies that have a history of failing.

Chatbots in Childhood Education

Advanced chatbots, using conversational, contextual AI, have versatile and promising applications. Chatbots have many advantages inherent within an automated platform, including promptness of response, scalability and accessibility. Recent advances have also increased the amount of personalization a chatbot can offer. This is especially evident when compared to existing communication methods such as mass emails and push notifications sent out to every user of an app.

This has been proven in traditionally human fields such as mental care. A company called Woebot created a chatbot that provides “continuous emotional support” to its users. It forges a personalized connection to the user to glean useful information for human specialists and to help deal with symptoms of stress and anxiety. This allows for human-like support that isn’t limited by doctor availability or cost.

Higher education institutions have also begun to use chat bots. Georgia State University rolled out a bot that helped students with enrolling and getting to college, decreasing the amount of admitted students who didn’t show up by 19%. Response levels from students were much higher with the bot than with email reminders. Chatbot platform Acquire identifies several different functions a chatbot can perform in education: providing information about school, administrative support, offering reminders and assistance, tutoring, and engaging students.

A chatbot built for younger students would most likely focus on the third, fourth, and fifth functions due to the nature of elementary schooling. As mentioned in a previous post, simple nudges and reminders are especially essential for younger children. In addition, children are less likely to be able to navigate information sources on their own. Communication-based chatbots are naturally easier interfaces for anyone, especially children, to use. Being able to answer basic questions and guide exploration would be a major benefit in and of itself in the education of young children.

Of course, a potential chatbot would have to be tailored for younger children specifically. It has to use grade-appropriate wording, while ideally selectively using new vocabulary to promote linguistic growth. There’s also a higher barrier to reach in terms of chatbot personality: a child would have little intrinsic motivation to keep on talking to a chatbot if it doesn’t act like a human. These are challenges that must be addressed in any chat bot dedicated towards childhood education.

What Changes a Child’s Attention Span

A child’s attention span — how long he or she can spend on a task before getting distracted — increases with age. In a 1990 study of young children in play, researchers found that a child’s duration of “focused attention” on the toys correlated with the age of the child. Older children focused more on problem solving and were less distracted by other physical movements. This means that the increase in attention span was not only due to the intrinsic development of the older child, but also the increased complexity of his activities.

In addition, the conditions in which these activities are presented also impact attention span. When frequently asked to “stay on task”, preschoolers paid less attention to distractions and more attention on the task itself. Simply repeating instructions to focus has a great effect on a child’s attention span, and can be implemented in any classroom or environment.

Children’s attention spans are also dependent upon how big each activity is. Sites such as claim that breaking a task into small pieces can keep children engaged more. This is corroborated in a 2010 study, which showed that young children allocated attention similarly to adults with small arrays of information. This means that children don’t have smaller attention spans because of inefficiencies in their memory allocation, but simply because they don’t have as much working memory to work with.

When dealing with younger children, it’s important to note the “why” behind general principles of education. It’s easy to assume that the reason of breaking up activities for children is simply to make each activity fit into smaller attention spans. However, this would miss activities that are short in duration, but are too complex to keep a child’s attention regardless. These nuances must be kept in consideration.

The Winners and Losers in Childhood Development

In a 2013 study at Stanford University, 48 infants of diverse economic socioeconomic status (SES) were tracked from 18 – 24 months of age and measured in language proficiency. Researchers found that at 18 months, children of higher SES were already significantly better off in vocabulary and language processing efficiency, and by 24 months, there developed “a 6-month gap between SES groups in processing skills critical to language development” (Fernald 2013).

This could easily be linked to the 1995 Hart and Risley Study, which found that children from professional families heard a significantly greater quantity of words per hour on average (2153 words) than those from working class (1251) or welfare-recipient (616) families, leading to the former having larger vocabularies. (Hart 1995). They reach the stark conclusion that the “most important aspect of children’s language experience is quantity”, though they note that children in professional families were encouraged more and discouraged less than their counterparts in less well-off families.

This gap is not just in vocabulary. According to an article published in Psychophysiology, “SES disparities in neurocognitive functioning have been shown across the domains of language, EF, memory, and social-emotional processing on both the behavioral and neurobiological levels” (Ursache 2017). They note a couple possibilities why these disparities could exist. As previously mentioned, language stimulation is a major distinction between households of different SES. In addition, the stresses of poverty could lead to “inconsistent, unpredictable, and non responsive parenting behaviors”, harming emotional development, and also lead to less time and energy being spent on supportive parenting.

Even though educational systems cannot solve the root issue of socioeconomic disparity, our preliminary understanding still points towards certain behaviors that would help solve the problem. In the classroom, educators can introduce more new words in their day to day speech as well as help parents to do the same (Colker 2014). Attention needs to be paid towards consistency and responsiveness in educator-child interactions, especially if these qualities are lacking in those of the parent and child.

The “Right” Kind of Nudge in Education

The effect of nudges – small changes in a child’s behavior that could lead to bigger change – cannot be overstated in a young child’s education and development. This outcome of promoting behavioral change is intrinsically powerful, shaping the course of a child’s development through momentary choices.

One example is the reminder: reminding a child of alternative choices is said to “promote children’s cognitive flexibility, as well as children’s engagement in and enjoyment of [a] task” according to a publication by Li Qu and Jing Y. Ong. This study analyzes how the effects of reminders are dictated by who gave them. It assigned groups of children to perform a task with either a child partner or an adult, with or without reminders. It finds that reminders given by adults actually decrease the intrinsic motivation and cognitive flexibility of the subject while reminders given by peers performed better. While the study has limitations, notably that it doesn’t measure how these children deal with later situations in a developmental fashion, it still illustrates one key point: the delivery and context around nudges matter as much as what the nudges themselves advocate for. As the researchers note, both the testers and the partners gave similar alternative choices, but the older testers might have been taken more seriously resulting in more distraction for the child, or the child could have simply been feeling more pressure from the adult than a peer (I know I would feel the same).

This is why thinking about the context and methodology of nudges is important. It inevitably affects what their subjects would think of them. Developmental psychologist Junlei Li focuses on nudging educators themselves into “enriching human relationships”. He emphasizes reinforcing what educators are already doing instead of what they are not but ought to be doing. This is a clear choice in the context of the previous study in which focusing on alternatives as an authority figure proved to be a distraction. While it seems like either option would accomplish the same end goal in promoting a certain behavior, one better takes into account the context of the nudge-giver as an authority figure in determining how to compose the nudge. This more effectively changes the behavior of the recipient.