Academia.eduAcademia.edu

Figure 1 – uploaded by Fati Tahiru

See full PDFdownloadDownload figure
Technological Context Figure 1. Technology-Organisation-Environment Framework ((Depietro, R., et al., 1990)

Figure 1 Technological Context Figure 1. Technology-Organisation-Environment Framework ((Depietro, R., et al., 1990)

Related Figures (7)

Table 1. Search strings
Table 1. Search strings
Table 2. Inclusion and exclusion criteria After screening the article set was narrowed down to 25 articles. Abstract of all 25 eligible publications were manually read to ascertain the relevance of the articles to the subject focus of t! literature review. An evaluation based on full text reading further reduced the articles to 23 articles. es were synthesized based on the following: year of publication, author name, t Afterward, artic title of the paper, research pur were categorized context discusse can be adapted. 1 s challenges al The challenges firm structure, and availability its impact on Education. Then t pose, method and type, and geographical coverage. The discussio and presented as opportunities, benefits, and challenges of AI. TOE was used in t! discussion of the challenges in the adoption of Artificial intelligence in education. The technological bout existing technology and other available new technologies that in an organizational context focused on organizational readiness, t issues in the ado selected articles is presented in Table 3. of the requisite infrastructure in the use of artificial intelligence and he Environmental context focused on the ethical and socio-economic ption and implementation of artificial intelligence in Education. The full list of t ne ne ns ne ne ne backward snowballing search approach to complement the systematic review method. The advantage of snowballing is using references or citations of an article as a guide to identifying other references (Wohlin, 2014). When using a snowballing approach, one does not only benefit from the papers or articles but also from their references and citations as well. The literature search resulted in 215 articles as of June 2019. The criteria used for the articles to be included, as shown in Table 2, were based on the following factors: focus on articles about the subject of the research, articles published from 2010 to 2019 in peer-reviewed journals and conference proceedings, articles written in English, and studies based on both qualitative and quantitative methods.
Table 2. Inclusion and exclusion criteria After screening the article set was narrowed down to 25 articles. Abstract of all 25 eligible publications were manually read to ascertain the relevance of the articles to the subject focus of t! literature review. An evaluation based on full text reading further reduced the articles to 23 articles. es were synthesized based on the following: year of publication, author name, t Afterward, artic title of the paper, research pur were categorized context discusse can be adapted. 1 s challenges al The challenges firm structure, and availability its impact on Education. Then t pose, method and type, and geographical coverage. The discussio and presented as opportunities, benefits, and challenges of AI. TOE was used in t! discussion of the challenges in the adoption of Artificial intelligence in education. The technological bout existing technology and other available new technologies that in an organizational context focused on organizational readiness, t issues in the ado selected articles is presented in Table 3. of the requisite infrastructure in the use of artificial intelligence and he Environmental context focused on the ethical and socio-economic ption and implementation of artificial intelligence in Education. The full list of t ne ne ns ne ne ne backward snowballing search approach to complement the systematic review method. The advantage of snowballing is using references or citations of an article as a guide to identifying other references (Wohlin, 2014). When using a snowballing approach, one does not only benefit from the papers or articles but also from their references and citations as well. The literature search resulted in 215 articles as of June 2019. The criteria used for the articles to be included, as shown in Table 2, were based on the following factors: focus on articles about the subject of the research, articles published from 2010 to 2019 in peer-reviewed journals and conference proceedings, articles written in English, and studies based on both qualitative and quantitative methods.
Volume 23 + Issue 1 * January-March 2021
Volume 23 + Issue 1 * January-March 2021
Table 3. Continued 2012, 2014, and 2015 recorded one (1) article each, four (4) articles was published in 2016 and 2017, whereas six(6) and five(5) articles were published in 2018 and 2019 respectively. This indicates the sradual and steady pace of research in AI application in education, as suggested by Dickson (2017) who asserts although there has been a steady improvement in the adoption of technology in education 10t much significant advancement has been noticed in the use of AI in education from the 20th century to date. On Geographical coverage, all 23 published articles were studies conducted within he context of developed countries and no publication was found on the use of AI in Education in the context of developing countries. This suggests that the focus of AI use in education in developing countries has not commenced or still at an early stage. Fi ee nS ee ne een: iy ny ee eT a a a: i ee ee een bin ee fe 2 ee. i; ee emery 2.77 Be koe
Table 3. Continued 2012, 2014, and 2015 recorded one (1) article each, four (4) articles was published in 2016 and 2017, whereas six(6) and five(5) articles were published in 2018 and 2019 respectively. This indicates the sradual and steady pace of research in AI application in education, as suggested by Dickson (2017) who asserts although there has been a steady improvement in the adoption of technology in education 10t much significant advancement has been noticed in the use of AI in education from the 20th century to date. On Geographical coverage, all 23 published articles were studies conducted within he context of developed countries and no publication was found on the use of AI in Education in the context of developing countries. This suggests that the focus of AI use in education in developing countries has not commenced or still at an early stage. Fi ee nS ee ne een: iy ny ee eT a a a: i ee ee een bin ee fe 2 ee. i; ee emery 2.77 Be koe
Table 4. List of Al opportunities articles There has been a noticeable increase in research on opportunities in AI in education between 2015 to 2018. And most of the research work is based on the design and perceptive approach. However, studies based on a design science approach usually place no attention to the socio-economic factors of AI adoption in education.
Table 4. List of Al opportunities articles There has been a noticeable increase in research on opportunities in AI in education between 2015 to 2018. And most of the research work is based on the design and perceptive approach. However, studies based on a design science approach usually place no attention to the socio-economic factors of AI adoption in education.
Table 5. List of Al benefits articles
Table 5. List of Al benefits articles
Table 6. List of challenges in Al Technological ‘his section discussed an empirical study on emerging architecture for AIED systems and the drivers hat influence the creation of AIED systems. Those factors that drive the AIED system and the AIED tructure. First, it was observed that the principal factors influencing AIED research are Educational leeds, which as a result generates AI theories, tools, and techniques. Scholars discussed the four ore elements of AIED: a model of the learner, domain expertise, teaching expertise, and interfaces. The goal of the AIED/ITS system is geared towards the individual learner. However, in recent years. idvancement in a broader learning context is encouraged with the inclusion of other parties such as eachers and parents. Moreover, data is needed in creating a group learning environment but data haring poses a serious challenge which gives rise to machine learning and educational data mining ommunities (Kay, 2012). The study Pierce and Hathaway (2018) state that data is important in Treating an effective AI system. The study further stressed that inaccurate or incomplete data in Al echnology may affect decisions or outcomes made by the system; since decisions made depend largely m the knowledge domain of the systems. Another challenge is the use of the wrong technology in he design of adaptive learning systems. Vendors must be probed enough to ensure that their systems ise machine learning technology and not any other alternate technology. (Pierce & Hathaway, 2018). AI technology raises some privacy concerns with the focus on information ownership. The use yf AI in education requires the collection of sensitive information like student academic records and
Table 6. List of challenges in Al Technological ‘his section discussed an empirical study on emerging architecture for AIED systems and the drivers hat influence the creation of AIED systems. Those factors that drive the AIED system and the AIED tructure. First, it was observed that the principal factors influencing AIED research are Educational leeds, which as a result generates AI theories, tools, and techniques. Scholars discussed the four ore elements of AIED: a model of the learner, domain expertise, teaching expertise, and interfaces. The goal of the AIED/ITS system is geared towards the individual learner. However, in recent years. idvancement in a broader learning context is encouraged with the inclusion of other parties such as eachers and parents. Moreover, data is needed in creating a group learning environment but data haring poses a serious challenge which gives rise to machine learning and educational data mining ommunities (Kay, 2012). The study Pierce and Hathaway (2018) state that data is important in Treating an effective AI system. The study further stressed that inaccurate or incomplete data in Al echnology may affect decisions or outcomes made by the system; since decisions made depend largely m the knowledge domain of the systems. Another challenge is the use of the wrong technology in he design of adaptive learning systems. Vendors must be probed enough to ensure that their systems ise machine learning technology and not any other alternate technology. (Pierce & Hathaway, 2018). AI technology raises some privacy concerns with the focus on information ownership. The use yf AI in education requires the collection of sensitive information like student academic records and
Related topics:
Information Systems
580 California St., Suite 400
San Francisco, CA, 94104
© 2025 Academia. All rights reserved