Why not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage

Edwards, Bosede Iyiade; Cheok, Adrian David

doi:10.20944/preprints201712.0022.v1

Cited by 6 publications

(6 citation statements)

References 29 publications

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“…The shortage of teachers has become a topic for discussion across many contexts (Edwards and Cheok, 2018 ; Garcia and Weiss, 2019 ) and continues in a time where innovative technology becomes more of an imperative. Therefore, the demand for school teachers has increased exponentially and it has resulted in a necessity to recruit almost 69 million teachers to provide quality education (SDG 4) by 2030 (United Nations, 2015 ).…”

Section: Related Workmentioning

confidence: 99%

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

et al. 2020

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This research occurred in a special context where Kazakhstan's recent decision to switch from Cyrillic to the Latin-based alphabet has resulted in challenges connected to teaching literacy, addressing a rare combination of research hypotheses and technical objectives about language learning. Teachers are not necessarily trained to teach the new alphabet, and this could result in a challenge for children with learning difficulties. Prior research studies in Human-Robot Interaction (HRI) have proposed the use of a robot to teach handwriting to children (Hood et al., 2015; Lemaignan et al., 2016). Drawing on the Kazakhstani case, our study takes an interdisciplinary approach by bringing together smart solutions from robotics, computer vision areas, and educational frameworks, language, and cognitive studies that will benefit diverse groups of stakeholders. In this study, a human-robot interaction application is designed to help primary school children learn both a newly-adopted script and also its handwriting system. The setup involved an experiment with 62 children between the ages of 7-9 years old, across three conditions: a robot and a tablet, a tablet only, and a teacher. Based on the paradigm-learning by teaching-the study showed that children improved their knowledge of the Latin script by interacting with a robot. Findings reported that children gained similar knowledge of a new script in all three conditions without gender effect. In addition, children's likeability ratings and positive mood change scores demonstrate significant benefits favoring the robot over a traditional teacher and tablet only approaches.

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Section: Related Workmentioning

confidence: 99%

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

et al. 2020

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“…For instance, the EMOTE project developed a robot with empathic qualities, which could tutor primary school students on tasks related to geography and sustainable development (Serholt and Barendregt, 2016 ; Obaid et al, 2018 ; Alves-Oliveira et al, 2019 ), the L2TOR project developed a robot that could tutor preschool children on second language learning (Vogt et al, 2019 ), whereas the CoWriter project developed a robot in the role of a novice, which children could teach handwriting skills to (El-Hamamsy et al, 2019 ). The motivations behind these efforts range from explorations of robots as technologies for supporting children's learning [e.g., language learning (Kory-Westlund and Breazeal, 2019 )] and the development of targeted skills [e.g., self-regulated learning (Jones and Castellano, 2018 )], to a conception of robots as solutions to various educational challenges, such as teachers' workload (Movellan et al, 2005 ) and a global teacher shortage (Edwards and Cheok, 2018 ). While social robots may not be used on a regular basis in education at present (Selwyn, 2019 ), researchers and developers continue to design novel applications for robots that aim to support education in various ways.…”

Section: Introductionmentioning

confidence: 99%

Trouble and Repair in Child–Robot Interaction: A Study of Complex Interactions With a Robot Tutee in a Primary School Classroom

et al. 2020

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Today, robots are studied and expected to be used in a range of social roles within classrooms. Yet, due to a number of limitations in social robots, robot interactions should be expected to occasionally suffer from troublesome situations and breakdowns. In this paper, we explore this issue by studying how children handle interaction trouble with a robot tutee in a classroom setting. The findings have implications not only for the design of robots, but also for evaluating their benefit in, and for, educational contexts. In this study, we conducted video analysis of children's group interactions with a robot tutee in a classroom setting, in order to explore the nature of these troubles in the wild. Within each group, children took turns acting as the primary interaction partner for the robot within the context of a mathematics game. Specifically, we examined what types of situations constitute trouble in these child-robot interactions, the strategies that individual children employ to cope with this trouble, as well as the strategies employed by other actors witnessing the trouble. By means of Interaction Analysis, we studied the video recordings of nine group interaction sessions (n = 33 children) in primary school grades 2 and 4. We found that sources of trouble related to the robot's social norm violations, which could be either active or passive. In terms of strategies, the children either persisted in their attempts at interacting with the robot by adapting their behavior in different ways, distanced themselves from the robot, or sought the help of present adults (i.e., a researcher in a teacher role, or an experimenter) or their peers (i.e., the child's classmates in each group). In terms of the witnessing actors, they addressed the trouble by providing guidance directed at the child interacting with the robot, or by intervening in the interaction. These findings reveal the unspoken rules by which children orient toward social robots, the complexities of child-robot interaction in the wild, and provide insights on children's perspectives and expectations of social robots in classroom contexts.

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“…The substantial amount of time and effort invested by the different stakeholders to realise their different objectives could indicate at least two things: (1) there seems to exist a discrepancy between ideas and reality in relation to AI in education and (2) the hidden labour of human actors speaks against the time and cost-saving arguments with which AI in education is so often promoted (cf. Edwards & Cheok, 2017;Selwyn, 2019).…”

Section: Con Clus Ionmentioning

confidence: 99%

“…Global teacher shortage is often highlighted as the main reason for automating certain routine tasks together with arguments that machine learning AI can deliver mass individualisation in teaching and learning at a lesser expense than a teacher (cf. Edwards & Cheok, 2017;Selwyn, 2019). The perception of cost-effectiveness through automation alone will probably (but not necessarily) be enough to drive the implementation of machine learning technologies in classrooms (Rowe, 2019).…”

Section: Con Clus Ionmentioning

confidence: 99%

Still w(AI)ting for the automation of teaching: An exploration of machine learning in Swedish primary education using Actor‐Network Theory

Sperling

Stenliden

Nissen

et al. 2022

Euro J of Education

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Machine learning and other artificial intelligence (AI) technologies are predicted to play a transformative role in primary education, where these technologies for automation and personalization are now being introduced to classroom instruction. This article explores the rationales and practices by which machine learning and AI are emerging in schools. We report on ethnographic fieldwork in Sweden, where a machine learning teaching aid in mathematics, the AI Engine, was tried out by 22 teachers and more than 250 primary education students. By adopting an Actor-Network Theory approach, the analysis focuses on the interactions within the network of heterogeneous actors bound by the AI Engine as an obligatory passage point.The findings show how the actions and accounts emerging within the complex ecosystem of human actors compensate for the unexpected and undesirable algorithmic decisions of the AI Engine. We discuss expectations about AI in education, contradictions in how the AI Engine worked and uncertainties about how machine learning algorithms 'learn' and predict. These factors contribute to our understanding of the potential of automation and personalisation-a process that requires continued re-negotiations. | 585 SPERLING et al. 1 | INTRODUC TI ON VLADIMIR: A-. What are you insinuating? That we have come to the wrong place? ESTRAGON: He should be here. VLADIMIR: He did not say for sure he'd come. ESTRAGON: And if he does not come? VLADIMIR: We'll come back tomorrow. ESTRAGON: And then the day after tomorrow.

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Why not Robot Teachers: Artificial Intelligence for Addressing Teacher Shortage

Cited by 6 publications

References 29 publications

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

A Comparison of Social Robot to Tablet and Teacher in a New Script Learning Context

Trouble and Repair in Child–Robot Interaction: A Study of Complex Interactions With a Robot Tutee in a Primary School Classroom

Still w(AI)ting for the automation of teaching: An exploration of machine learning in Swedish primary education using Actor‐Network Theory

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