Intelligent Learning Technologies: Applications of Artificial Intelligence to Contemporary and Emerging Educational Challenges

Chaudhri, Vinay K.; Lane, H. Chad; Gunning, Dave; Roschelle, Jeremy

doi:10.1609/aimag.v34i3.2482

Cited by 12 publications

(14 citation statements)

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“…Education and information search is an area where the literature has identified the potential benefits of AI technology solutions. Chaudhri, Lane, Gunning, and Roschelle (2013) discussed application of AI in education to improve teacher effectiveness and student engagement. The study analysed the potential of AI within education in the context of intelligent game-based learning environments, tutoring systems and Fig.…”

Section: Application Domainsmentioning

confidence: 99%

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Dwivedi

Hughes

Ismagilova

et al. 2021

International Journal of Information Management

1,586

986

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As far back as the industrial revolution, significant development in technical innovation has succeeded in transforming numerous manual tasks and processes that had been in existence for decades where humans had reached the limits of physical capacity. Artificial Intelligence (AI) offers this same transformative potential for the augmentation and potential replacement of human tasks and activities within a wide range of industrial, intellectual and social applications. The pace of change for this new AI technological age is staggering, with new breakthroughs in algorithmic machine learning and autonomous decision-making, engendering new opportunities for continued innovation. The impact of AI could be significant, with industries ranging from: finance, healthcare, manufacturing, retail, supply chain, logistics and utilities, all potentially disrupted by the onset of AI technologies. The study brings together the collective insight from a number of leading expert contributors to highlight the significant opportunities, realistic assessment of impact, challenges and potential research agenda posed by the rapid emergence of AI within a number of domains: business and management, government, public sector, and science and technology. This research offers significant and timely insight to AI technology and its impact on the future of industry and society in general, whilst recognising the societal and industrial influence on pace and direction of AI development.

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Section: Application Domainsmentioning

confidence: 99%

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Dwivedi

Hughes

Ismagilova

et al. 2021

International Journal of Information Management

1,586

986

View full text Add to dashboard Cite

show abstract

“…As it is possible to read from all the recent and enthusiastic papers about AIEd, the hopes for the future are really high, above all those applied to STEM (science, technology, engineering, mathematics) education (Chaudhri et al, 2013). Researchers are really excited and convinced of the possibility to have one-to-one tutoring, and to be able to overcome all the learning difficulties of people mostly excluded from a normal class.…”

Section: Problems Posed By the Use Of Aiedmentioning

confidence: 99%

A “psychopathic” Artificial Intelligence: the possible risks of a deviating AI in Education

Zanetti

Iseppi

Cassese

2019

Research on Education and Media

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This work analyses the use of artificial intelligence in education from an interdisciplinary point of view. New studies demonstrated that an AI can “deviate” and become potentially malicious, due to programmers’ biases, corrupted feeds or purposeful actions. Knowing the pervasive use of artificial intelligence systems, including in the educational environment, it seemed necessary to investigate when and how an AI in education could deviate. We started with an investigation of AI and the risks it poses, wondering if they could be applied also to educative AI. We then reviewed the increasing literature that deals with the use of technology in the classroom, and the criticism about it, referring to specific use cases. Finally, as a result, the authors formulate questions and suggestions for further research, to bridge conceptual gaps underlined by lack of research.

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“…A new species of responsive items offers students immediate feedback on questions, thus serving a formative assessment function. Machine learning techniques (Chaudhri, Gunning, Lane, & Roschelle, 2013) can also be applied whereby item-based assessments improve through use. For instance, newly designed items—even teacher-developed items—that have not yet been validated can be mixed with well tested ones in order to determine their difficulty, and students could offer feedback based on their underlying thinking (correct thinking/wrong answer or incorrect thinking/correct answer may prompt reframing of the item; Cope, Kalantzis, McCarthey, et al, 2011).…”

Section: Evidence Of Learning In Computer-mediated Learning Environmentsmentioning

confidence: 99%

Big Data Comes to School

Cope

Kalantzis

2016

AERA Open

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The prospect of “big data” at once evokes optimistic views of an information-rich future and concerns about surveillance that adversely impacts our personal and private lives. This overview article explores the implications of big data in education, focusing by way of example on data generated by student writing. We have chosen writing because it presents particular complexities, highlighting the range of processes for collecting and interpreting evidence of learning in the era of computer-mediated instruction and assessment as well as the challenges. Writing is significant not only because it is central to the core subject area of literacy; it is also an ideal medium for the representation of deep disciplinary knowledge across a number of subject areas. After defining what big data entails in education, we map emerging sources of evidence of learning that separately and together have the potential to generate unprecedented amounts of data: machine assessments, structured data embedded in learning, and unstructured data collected incidental to learning activity. Our case is that these emerging sources of evidence of learning have significant implications for the traditional relationships between assessment and instruction. Moreover, for educational researchers, these data are in some senses quite different from traditional evidentiary sources, and this raises a number of methodological questions. The final part of the article discusses implications for practice in an emerging field of education data science, including publication of data, data standards, and research ethics.

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Intelligent Learning Technologies: Applications of Artificial Intelligence to Contemporary and Emerging Educational Challenges

Cited by 12 publications

References 0 publications

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy

A “psychopathic” Artificial Intelligence: the possible risks of a deviating AI in Education

Big Data Comes to School

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