Artificial Intelligence in Education

Goksel, Nil; Bozkurt, Aras

doi:10.4018/978-1-5225-8431-5.ch014

Cited by 87 publications

(32 citation statements)

References 22 publications

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“…A growing body of literature has attempted to uncover the value of big data at different education levels, from preschool to higher education (Chen N.-S. et al, 2020). Several journal articles and book chapters have presented retrospective descriptions and the latest advances in the rapidly expanding research area from different angles, including systematic literature review (Zawacki-Richter et al, 2019;Quadir et al, 2020), bibliometric study (Hinojo-Lucena et al, 2019), qualitative analysis (Malik et al, 2019;Chen L. et al, 2020), and social network analysis (Goksel and Bozkurt, 2019). More details can be found in the previously mentioned reviews.…”

Section: The Position Formulationmentioning

confidence: 99%

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

et al. 2020

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We discuss the new challenges and directions facing the use of big data and artificial intelligence (AI) in education research, policy-making, and industry. In recent years, applications of big data and AI in education have made significant headways. This highlights a novel trend in leading-edge educational research. The convenience and embeddedness of data collection within educational technologies, paired with computational techniques have made the analyses of big data a reality. We are moving beyond proof-of-concept demonstrations and applications of techniques, and are beginning to see substantial adoption in many areas of education. The key research trends in the domains of big data and AI are associated with assessment, individualized learning, and precision education. Model-driven data analytics approaches will grow quickly to guide the development, interpretation, and validation of the algorithms. However, conclusions from educational analytics should, of course, be applied with caution. At the education policy level, the government should be devoted to supporting lifelong learning, offering teacher education programs, and protecting personal data. With regard to the education industry, reciprocal and mutually beneficial relationships should be developed in order to enhance academia-industry collaboration. Furthermore, it is important to make sure that technologies are guided by relevant theoretical frameworks and are empirically tested. Lastly, in this paper we advocate an in-depth dialog between supporters of "cold" technology and "warm" humanity so that it can lead to greater understanding among teachers and students about how technology, and specifically, the big data explosion and AI revolution can bring new opportunities (and challenges) that can be best leveraged for pedagogical practices and learning.

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Section: The Position Formulationmentioning

confidence: 99%

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

et al. 2020

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“…Zawacki-Richter, Marín, Bond and Gouverneur [33] investigated research on AI in higher education and they critically draw attention to most of the papers' "lack of critical reflection of challenges and risks of AIEd, the weak connection to theoretical pedagogical perspectives, and the need for further exploration of ethical and educational approaches in the application of AIEd in higher education." In their review study, Goksel and Bozkurt [34] identified three broad themes, namely, adaptive learning, personalization and learning styles, expert systems and intelligent tutoring systems, and AI as a future component of educational processes. However, they further noted that though AI in education has great potential, it would be misleading to accept it superior by default and researchers "need to develop a critical stance before fully integrating AI into educational processes.…”

Section: Related Literaturementioning

confidence: 99%

“…It is reported that use of AI-generated data through learning analytics and intelligent tutoring systems improve student performance [57]. Due to significant progress in theory and practice derived from the application of AI [12] and the potentials in educational data mining, learning analytics [50,58], it is further argued that AI is a future component of educational processes [34].…”

Section: T-sne Text-mining and Social Network Analysismentioning

confidence: 99%

Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

et al. 2021

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Artificial intelligence (AI) has penetrated every layer of our lives, and education is not immune to the effects of AI. In this regard, this study examines AI studies in education in half a century (1970–2020) through a systematic review approach and benefits from social network analysis and text-mining approaches. Accordingly, the research identifies three research clusters (1) artificial intelligence, (2) pedagogical, and (3) technological issues, and suggests five broad research themes which are (1) adaptive learning and personalization of education through AI-based practices, (2) deep learning and machine Learning algorithms for online learning processes, (3) Educational human-AI interaction, (4) educational use of AI-generated data, and (5) AI in higher education. The study also highlights that ethics in AI studies is an ignored research area.

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“…Due to the outbreak of COVID-19, filling forms, upgrading files, cross-referencing documents can be used in this case, as the need arises. With this advantage, many firms will use AIenabled data management systems to identify extortion, avoid cyber security threats and enhance their overall business activities during the pandemic (Goksel and Bozkurt, 2019;Di Vaio et al 2020).…”

Section: Conclusion and Implications Of The Studymentioning

confidence: 99%

Technology and Entrepreneurial Marketing Decisions During COVID-19

Polas

Raju

2021

Glob J Flex Syst Manag

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The prime concern of this study is to explore how technology influences entrepreneurial marketing decisions during the world pandemic (COVID-19). The study uses a sample of 127 SMEs from Bangladesh. Data were collected by sending out questionnaires electronically and by mail. Smart PLS (SEM) 3.0 was used to analyse the data following the quantitative method. The study reveals positive and significant relationships between entrepreneurial opportunity recognition, opportunity development and opportunity exploitation with their entrepreneurial marketing decisions. It also claims that entrepreneurial passion mediates the relationship between entrepreneurial opportunity recognition and opportunity development with their entrepreneurial marketing decisions. However, entrepreneurial passion does not mediate the relationship between entrepreneurial opportunity exploitation and entrepreneurial marketing decisions. The study offers researchers a broader and more wide-ranging view of the importance of artificial intelligence in small firms. Researchers, educators and practitioners will benefit from the findings. The analyses are more complex and varied than the methodologies used in most of the limited previous research.

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Artificial Intelligence in Education

Cited by 87 publications

References 22 publications

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

Challenges and Future Directions of Big Data and Artificial Intelligence in Education

Artificial Intelligence and Reflections from Educational Landscape: A Review of AI Studies in Half a Century

Technology and Entrepreneurial Marketing Decisions During COVID-19

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