Artificial intelligence in education: The three paradigms

Ouyang, Fan; Jiao, Pengcheng

doi:10.1016/j.caeai.2021.100020

Cited by 224 publications

(156 citation statements)

References 32 publications

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“…With the development of computer science and computational technologies, automatic, adaptive, and efficient AI technologies have been widely applied in various academic fields. Artificial Intelligence in Education (AIEd), as an interdisciplinary field, emphasizes applying AI to assist instructor's instructional process, empower student's learning process, and promote the transformation of educational system (Chen et al, 2020;Holmes et al, 2019;Hwang et al, 2020;Ouyang & Jiao, 2021). First, AIEd has potential to enhance instructional design and pedagogical development in the teaching processes, such as accessing students' performance automatically (Wang et al, 2011;Zampirolli et al, 2021), monitoring and tracking students' learning (Berland et al, 2015;Ji & Han, 2019), and predicting at-risk students (Hellings & Haelermans, 2020;Lamb et al, 2021).…”

Section: Literature Reviewmentioning

confidence: 99%

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Ouyang

2022

IJ STEM Ed

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Background The application of artificial intelligence (AI) in STEM education (AI-STEM), as an emerging field, is confronted with a challenge of integrating diverse AI techniques and complex educational elements to meet instructional and learning needs. To gain a comprehensive understanding of AI applications in STEM education, this study conducted a systematic review to examine 63 empirical AI-STEM research from 2011 to 2021, grounded upon a general system theory (GST) framework. Results The results examined the major elements in the AI-STEM system as well as the effects of AI in STEM education. Six categories of AI applications were summarized and the results further showed the distribution relationships of the AI categories with other elements (i.e., information, subject, medium, environment) in AI-STEM. Moreover, the review revealed the educational and technological effects of AI in STEM education. Conclusions The application of AI technology in STEM education is confronted with the challenge of integrating diverse AI techniques in the complex STEM educational system. Grounded upon a GST framework, this research reviewed the empirical AI-STEM studies from 2011 to 2021 and proposed educational, technological, and theoretical implications to apply AI techniques in STEM education. Overall, the potential of AI technology for enhancing STEM education is fertile ground to be further explored together with studies aimed at investigating the integration of technology and educational system.

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Section: Literature Reviewmentioning

confidence: 99%

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

Ouyang

2022

IJ STEM Ed

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“…This shows that errors occur in the learning process (Altun-Yalçn et al, 2011;Helms, 2014). Teachers still use a paradigm in teaching, namely teachers dominate the learning process so that in the learning process they still use conventional methods with students only coming, sitting, listening to the material after returning home, even though in the industrial revolution 4.0 people have proven the quality of modern learning, one of which is artificial intelligence methods and digital learning (Audunson & Shuva, 2016;Ouyang & Jiao, 2021).…”

Section: Resultsmentioning

confidence: 99%

The ability of the Question and Answer (Q&A) Method With the Help of Learnin Videos Against Student Learning Outcomes Amid the Covid-19 Pandemic

Putra¹,

Junaid²,

Sulman³

2021

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“…One way to address the issue is to deliberately choose the student data that are underpinned by a learning theory in order to reflect the specific learning process. Because one of the goals of the performance prediction models is to optimize student-centered learning pathways, the choice of students' input data should be guided by the student-centered learning principle and reflect the student-centered learning processes (Ouyang & Jiao, 2021). There are emerging studies that focus on using online learning behavior data from the process-oriented perspective to accurately predict academic performance, rather than merely using student information data (e.g., demographics) or performance data (e.g., final grades) (Bernacki et al 2020).…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-enabled prediction model of student academic performance in online engineering education

et al. 2022

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Online education has been facing difficulty in predicting the academic performance of students due to the lack of usage of learning process, summative data and a precise prediction of quantitative relations between variables and achievements. To address these two obstacles, this study develops an artificial intelligence-enabled prediction model for student academic performance based on students’ learning process and summative data. The prediction criteria are first predefined to characterize and convert the learning data in an online engineering course. An evolutionary computation technique is then used to explore the best prediction model for the student academic performance. The model is validated using another online course that applies the same pedagogy and technology. Satisfactory agreements are obtained between the course outputs and model prediction results. The main findings indicate that the dominant variables in academic performance are the knowledge acquisition, the participation in class and the summative performance. The prerequisite knowledge tends not to play a key role in academic performance. Based on the results, pedagogical and analytical implications are provided. The proposed evolutionary computation-enabled prediction method is found to be a viable tool to evaluate the learning performance of students in online courses. Furthermore, the reported genetic programming model provides an acceptable prediction performance compared to other powerful artificial intelligence methods.

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Artificial intelligence in education: The three paradigms

Cited by 224 publications

References 32 publications

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

The application of AI technologies in STEM education: a systematic review from 2011 to 2021

The ability of the Question and Answer (Q&A) Method With the Help of Learnin Videos Against Student Learning Outcomes Amid the Covid-19 Pandemic

Artificial intelligence-enabled prediction model of student academic performance in online engineering education

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