Kid Space

Anderson, Glen J.; Panneer, Selvakumar; Shi, Meng; Marshall, Carl; Agrawal, Ankur; Chierichetti, Rebecca; Raffa, Giuseppe Maria; Sherry, John F.; Loi, Daria; Durham, Lenitra

doi:10.1145/3279981.3279986

Cited by 8 publications

(7 citation statements)

References 28 publications

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“…Our previous ethnographic research with dozens of parents showed that they acknowledged the technology's benefits for their young children's learning, however, they still worried about the screen time, lack of physical activity and lack of social interactions (Anderson et al., 2015; see also Ahearne et al., 2016; American Academy of Pediatrics, 2016; Blackwell et al., 2014; Wood et al., 2008). With the outbreak of the COVID‐19 pandemic, these concerns exponentially increased as children's screen time peaked—for some children reaching ~%500 increase (Eyimaya & Irmak, 2021; Parents Together, 2020).…”

Section: Introductionmentioning

confidence: 99%

“…Kid Space combines various sensing technologies and uses spoken dialogue to interact with children and understand individual progress to provide personalised learning experiences in a blended physical and digital environment (Anderson et al., 2018; Aslan, Agrawal, et al., 2022; Sahay et al., 2019). We previously tested Kid Space as an immersive, projected experience in an elementary school with promising initial results that showed high levels of task engagement with less screen time, increased physical activity and more social interactions (Aslan, Agrawal, et al., 2022) when compared to traditional PC‐based experiences.…”

Section: Introductionmentioning

confidence: 99%

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What is the impact of a multi‐modal pedagogical conversational AI system on parents' concerns about technology use by young children?

Aslan,

Durham,

Alyuz

et al. 2023

Brit J Educational Tech

Self Cite

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Previous research showed that the parents acknowledged the technology's benefits for their young children's learning, however, they are still worried about the extended screen time, lack of physical activity and lack of social interactions. To address these concerns, we developed Kid Space to enable pedagogically appropriate technology use for children in early childhood education by combining various sensing technologies with a multi‐modal conversational artificial intelligence system that can interact with children, understand individual progress and provide personalised learning experiences. To understand the impact of Kid Space on the parents' initial concerns about technology use by their young children, we conducted a multi‐method user study: (1) a quasi‐experimental design and (2) formative research method using an exploratory case study with a set of children and their parents experiencing Kid Space in their homes. The results show that after experiencing Kid Space with their children, the parents felt significantly less concerned about screen time, social interactions and physical activity and reported positive perceptions towards pedagogical value of Kid Space. Detailed analysis on the multi‐modal data quantitatively and qualitatively validated why Kid Space alleviated these concerns. Future research is needed to validate long‐term educational value of Kid Space and generate insights for improvement for next iterations. Practitioner notesWhat is already known about this topic Play‐based learning is critical for young children's education, but digital games create major concerns around extended screen time, lack of physical activity and lack of social interactions. Blending digital and physical spaces could support pedagogically appropriate technology use for young children. Towards this end, there are some exemplary studies in the state–of‐the‐art reporting positive educational outcomes as an effect of utilising such spaces. However, none of these studies supported children's most natural mode of communication in their interactions with the systems—speaking. Pedagogical conversational agents (PCAs) are promising, but they are tricky when it comes to young children's speech because of unique technical challenges resulted from how children use language and communicate with digital systems. What this paper adds To our best knowledge, Kid Space is one of the earliest implementations of a PCA with a multi‐modal artificial intelligence (AI) system utilising physical and digital learning manipulatives for maths learning with a focus on early childhood education. The key contributions of this paper are (1) the design and development of an end‐to‐end multi‐modal system enabling Wizard‐of‐Oz experimentation for initial evaluations with users, (2) the creation of a multi‐modal, in‐the‐wild labelled dataset with children–agent, children–parent and children–physical/digital space interactions enabling advancements for AI components for later evaluations with users and (3) the generation of rich insights from an initial research study on user perceptions and engagement as well as actionable findings to improve Kid Space experiences for next iterations and inform key design features for similar systems. Implications for practice and/or policy The results of the study implied a set of areas for improvement—or design features—for Kid Space and other similar pedagogical conversational systems developed for children's home usages: (1) easier setup and usage with optimised setup size addressing diverse space limitations at homes, (2) minimised latency between Oscar (the conversational pedagogical agent) and child interactions (eg, adding multimodal dialogue system to reduce the need for a human wizard), (3) more advanced personalisation, social (including more verbal interactions) and pedagogical skills for Oscar with increased contextual awareness (eg, sending children's engagement), (4) scalability and higher visual quality of content with diverse games and learning outcomes, (5) parental control features over Kid Space platform and Oscar (eg, time limit, content, etc.) and (6) accessibility features (eg, captions turned on for multilingual children) and support for neurodiversity.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

What is the impact of a multi‐modal pedagogical conversational AI system on parents' concerns about technology use by young children?

Aslan,

Durham,

Alyuz

et al. 2023

Brit J Educational Tech

Self Cite

View full text Add to dashboard Cite

show abstract

“…To conduct our experiments, we use the BANK-ING (Casanueva et al, 2020) and CLINC (Larson et al, 2019) datasets similar to the DAC work (Zhang et al, 2021). We also use another dataset called KidSpace that includes utterances from a Multimodal Learning Application for 5-to-8 years-old children (Sahay et al, 2019;Anderson et al, 2018). We hope to utilize this system to label future utterances into relevant intents.…”

Section: Resultsmentioning

confidence: 99%

Semi-supervised Interactive Intent Labeling

Sahay¹,

Okur²,

Hakim³

et al. 2021

Proceedings of the Second Workshop on Data Science With Human in the Loop: Language Advances

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Building the Natural Language Understanding (NLU) modules of task-oriented Spoken Dialogue Systems (SDS) involves a definition of intents and entities, collection of task-relevant data, annotating the data with intents and entities, and then repeating the same process over and over again for adding any functionality/enhancement to the SDS. In this work, we showcase an Intent Bulk Labeling system where SDS developers can interactively label and augment training data from unlabeled utterance corpora using advanced clustering and visual labeling methods. We extend the Deep Aligned Clustering (Zhang et al., 2021) work with a better backbone BERT model, explore techniques to select the seed data for labeling, and develop a data balancing method using an oversampling technique that utilizes paraphrasing models. We also look at the effect of data augmentation on the clustering process. Our results show that we can achieve over 10% gain in clustering accuracy on some datasets using the combination of the above techniques. Finally, we extract utterance embeddings from the clustering model and plot the data to interactively bulk label the samples, reducing the time and effort for data labeling of the whole dataset significantly.

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“…Kid Space is an advanced, centralized projection device that creates multi-modal interactivity and intelligently projects AR content across surfaces using a visible agent to help with learning by playing [98]. The initial study showed that children were involved actively with the projected character during a math exercise.…”

Section: Multi-agent Systemsmentioning

confidence: 99%

“…From all of the above studies discussed, there are only a few that have considered agents (as shown in Figure 3); these are presented in Section 4.11 such as Kid Space [98,99] for problem-based learning. Machine Learning Unity Agents are pretty new and have not been effectively implemented in any AR studies for education, as discussed in detail in Section 6.3.…”

Section: Agentsmentioning

confidence: 99%

Current Challenges and Future Research Directions in Augmented Reality for Education

Iqbal

Mangina

Campbell

2022

MTI

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The progression and adoption of innovative learning methodologies signify that a respective part of society is open to new technologies and ideas and thus is advancing. The latest innovation in teaching is the use of Augmented Reality (AR). Applications using this technology have been deployed successfully in STEM (Science, Technology, Engineering, and Mathematics) education for delivering the practical and creative parts of teaching. Since AR technology already has a large volume of published studies about education that reports advantages, limitations, effectiveness, and challenges, classifying these projects will allow for a review of the success in the different educational settings and discover current challenges and future research areas. Due to COVID-19, the landscape of technology-enhanced learning has shifted more toward blended learning, personalized learning spaces and user-centered approach with safety measures. The main findings of this paper include a review of the current literature, investigating the challenges, identifying future research areas, and finally, reporting on the development of two case studies that can highlight the first steps needed to address these research areas. The result of this research ultimately details the research gap required to facilitate real-time touchless hand interaction, kinesthetic learning, and machine learning agents with a remote learning pedagogy.

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Kid Space

Cited by 8 publications

References 28 publications

What is the impact of a multi‐modal pedagogical conversational AI system on parents' concerns about technology use by young children?

What is the impact of a multi‐modal pedagogical conversational AI system on parents' concerns about technology use by young children?

Semi-supervised Interactive Intent Labeling

Current Challenges and Future Research Directions in Augmented Reality for Education

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