A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

Zhai, Xuesong; Chu, Xiaoyan; Chai, Ching Sing; Jong, Morris Siu‐Yung; Starčič, Andreja Istenic; Spector, Michael; Liu, Jia‐Bao; Jing, Yuan; Li, Yan

doi:10.1155/2021/8812542

Cited by 382 publications

(173 citation statements)

References 104 publications

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“…Artificial intelligence (AI) technology is applied to promote learning and teaching effectiveness (Lim, 2020). AI technology is being used to monitor teaching processes and to perform academic analysis and academic level diagnosis (Zhai et al, 2021). Thus, reconstructing an AI-assisted learning environment with interactive and smart learning has become a new task in education.…”

Section: Introductionmentioning

confidence: 99%

Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Zhang²,

Li³

et al. 2022

Front. Psychol.

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Artificial intelligence (AI) technology has been widely applied in many fields. AI-assisted learning environments have been implemented in classrooms to facilitate the innovation of pedagogical models. However, college students' willingness to accept (WTA) AI-assisted learning environments has been ignored. Exploring the factors that influence college students' willingness to use AI can promote AI technology application in higher education. Based on the Unified Theory of Acceptance and Use of Technology (UTAUT) and the theory of perceived risk, this study identified six factors that influence students' willingness to use AI to analyze their relationships with WTA AI-assisted learning environments. A model including six hypotheses was constructed to test the factors affecting students' WTA. The results indicated that college students showed “weak rejection” of the construction of AI-assisted learning environments. Effort expectancy (EE), performance expectancy (PE), and social influence (SI) were all positively related to college students' WTA AI-assisted learning environments. Psychological risk (PR) significantly negatively influenced students' WTA. The findings of this study will be helpful for carrying out risk communication, which can promote the construction of AI-assisted learning environments.

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Section: Introductionmentioning

confidence: 99%

Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Zhang²,

Li³

et al. 2022

Front. Psychol.

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“…In a review about the AI in education, Zhai et al (2021) identified that the ethics of AI are relevant in education. However, our participants' concerns focused more on social skills and related ethical concerns and not so much on practical solutions.…”

Section: Discussionmentioning

confidence: 99%

Pre-service teachers’ concerns about social robots in the classroom: A model for development

Starčič¹,

Bratko²,

Rosanda³

2021

E&SD

Self Cite

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Social robots are being tested in the educational arena with current thinking in two main directions. One is arguing for the benefits of robots in affective and efficient instruction and is more teachercentered. Within the second, more student-centered oriented, proponents of human uniqueness are raising long-term concerns. Teacher-centeredness and student-centeredness form pedagogical beliefs underpinning teachers’ attitudes guiding technology integration. Limited research has explored teachers’ underlying beliefs and attitudes to social robots, with some presenting mixed feelings identifying some concerns with some identifying more positive attitudes. Preservice education is critical in forming beliefs, and this paper presents a qualitative study of Slovene preservice pre-primary school and primary classroom pre-service teachers’ attitudes and underlying beliefs. Students were asked to reflect on their perception of social robotic educational technology in which they would highlight at their own discretion the positive, neutral and negative aspects. Students’ reflections predominantly expressed concerns. The research model was designed in part, drawing from participants reflections and on related studies. Previous studies indicated the concerns teachers hold about robotic technology, but lacked a more holistic model. We built a threefold model distinguishing instructional, social-emotional, and legal concerns. Our findings differ from related studies because they identified participants’ negative attitudes and a clear rejection of robot technology with a human-like appearance and social skills in the classroom. Previous studentcentered studies reported on single groups of concerns within specific contexts without developing a holistic view relating diverse concerns in one picture. Related teacher-centered studies were arguing for refinements anticipating robot’s social intelligence affordance in the classroom. The participants in our study are not rejecting social robots as such, but in their view, the robot is not granted the status of a social entity capable of engaging in student-centered teaching and taking care of child wellbeing and development. The findings of our study call for action and informed robot development, taking into consideration teachers as co-designers.

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“…The use of AI technologies to enhance students' learning experiences has gained increasing popularity, especially in the last decade (Chassignol et al, 2018;Aslan et al, 2019;Jia et al, 2020;Baker, 2021;Zhai et al, 2021;Zhang and Aslan, 2021). Intelligent gamebased learning systems (Lester et al, 2013;Richey et al, 2021) present significant benefits for practicing math concepts in smart spaces (Pires et al, 2019;Sun et al, 2021), specifically for early childhood education (Skene et al, 2022).…”

Section: Related Workmentioning

confidence: 99%

“…Investigating Artificial Intelligence (AI) systems that can help children in their learning process has been a challenging yet exciting area of research (Chassignol et al, 2018;Zhai et al, 2021). Utilizing Natural Language Processing (NLP) for building educational games and applications has gained popularity in the past decade (Lende and Raghuwanshi, 2016;Cahill et al, 2020).…”

Section: Introductionmentioning

confidence: 99%

NLU for Game-based Learning in Real: Initial Evaluations

Okur¹,

Sahay²,

Nachman³

2022

Preprint

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Intelligent systems designed for play-based interactions should be contextually aware of the users and their surroundings. Spoken Dialogue Systems (SDS) are critical for these interactive agents to carry out effective goal-oriented communication with users in real-time. For the real-world (i.e., in-the-wild) deployment of such conversational agents, improving the Natural Language Understanding (NLU) module of the goal-oriented SDS pipeline is crucial, especially with limited task-specific datasets. This study explores the potential benefits of a recently proposed transformer-based multi-task NLU architecture, mainly to perform Intent Recognition on small-size domain-specific educational game datasets. The evaluation datasets were collected from children practicing basic math concepts via play-based interactions in game-based learning settings. We investigate the NLU performances on the initial proof-of-concept game datasets versus the real-world deployment datasets and observe anticipated performance drops in-the-wild. We have shown that compared to the more straightforward baseline approaches, Dual Intent and Entity Transformer (DIET) architecture (Bunk et al., 2020) is robust enough to handle real-world data to a large extent for the Intent Recognition task on these domain-specific in-the-wild game datasets.

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A Review of Artificial Intelligence (AI) in Education from 2010 to 2020

Cited by 382 publications

References 104 publications

Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Exploring Factors of the Willingness to Accept AI-Assisted Learning Environments: An Empirical Investigation Based on the UTAUT Model and Perceived Risk Theory

Pre-service teachers’ concerns about social robots in the classroom: A model for development

NLU for Game-based Learning in Real: Initial Evaluations

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