Impact of Artificial Intelligence in Special Need Education to Promote Inclusive Pedagogy

Garg, Shalini; Sharma, Smriti

doi:10.18178/ijiet.2020.10.7.1418

Cited by 65 publications

(20 citation statements)

References 7 publications

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“…Garg and Sharma [13] attempted to investigate how artificial intelligence has impacted the schooling of students with exceptional needs. e data were gathered through a qualitative study involving targeted interviews with both professors and kids needing special attention.…”

Section: Related Literaturementioning

confidence: 99%

[Retracted] Implementing the Hybrid Neuro‐Fuzzy System to Model Specific Learning Disability in Special University Education Programs

Wending¹

2022

Journal of Mathematics

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Learning difficulty is a significant issue that comprehensively needs to be taken care of by university instructors with a focus on altering their ways of educating students since various degrees of learning difficulty could be observed among students. Hence, proper approaches should be adapted and utilized that provide chances for each student, securing his/her educational right. By doing so, it guarantees that each student has a right of learning with a different pace of conducting it. For example, the same educational and teaching materials cannot be adapted to each student. Hence, the training of students with learning difficulty should follow a model that is characterized as “from bottom to top,” namely, “normal to learning with difficulties.” The characterization of this approach is very similar to that of the treatment of human beings: biological malfunctions by medicine and mechanical malfunctions by psycho-sociology. This paper proposes a diagnostic model to determine the cognitive ability of students who deal with learning disabilities. The computerized model is proposed based on a combination of fuzzy logic and machine learning utilizing association rules and cellular automata. The advantage of the proposed method is that it helps find the needs of these types of students, so the widely implemented practices that shape his/her learning behavior are avoided. Therefore, practical recognition of each student with a different pace of learning should be realized.

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Section: Related Literaturementioning

confidence: 99%

[Retracted] Implementing the Hybrid Neuro‐Fuzzy System to Model Specific Learning Disability in Special University Education Programs

Wending¹

2022

Journal of Mathematics

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“…Certainly, ADM can provide benefits to workers with disabilities. ADM is used to help persons with disabilities gain the skills to obtain and retain work by improving educational support provided to students with disabilities [ 93 , 94 ]. Algorithmic technology is also used to support people with health concerns and conditions at work.…”

Section: Algorithmic Decision-making At Workmentioning

confidence: 99%

Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors

2022

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Purpose This article examines ways COVID-19 health surveillance and algorithmic decision-making (“ADM”) are creating and exacerbating workplace inequalities that impact post-treatment cancer survivors. Cancer survivors’ ability to exercise their right to work often is limited by prejudice and health concerns. While cancer survivors can ostensibly elect not to disclose to their employers when they are receiving treatments or if they have a history of treatment, the use of ADM increases the chances that employers will learn of their situation regardless of their preferences. Moreover, absent significant change, inequalities may persist or even expand. Methods We analyze how COVID-19 health surveillance is creating an unprecedented amount of health data on all people. These data are increasingly collected and used by employers as part of COVID-19 regulatory interventions. Results The increase in data, combined with the health and economic crisis, means algorithm-driven health inequalities will be experienced by a larger percentage of the population. Post-treatment cancer survivors, as for people with disabilities generally, are at greater risk of experiencing negative outcomes from algorithmic health discrimination. Conclusions Updated and revised workplace policy and practice requirements, as well as collaboration across impacted groups, are critical in helping to control the inequalities that flow from the interaction between COVID-19, ADM, and the experience of cancer survivorship in the workplace. Implications for Cancer Survivors The interaction among COVID-19, health surveillance, and ADM increases exposure to algorithmic health discrimination in the workplace.

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“…Технологии ИИ активно используются в образовании для помощи людям с ограниченными возможностями здоровья. Они позволяют ставить диагноз, например определять дислексию; игровые технологии помогают детям с расстройствами аутистического спектра осваивать социальные навыки; «умные переводчики» облегчают слабослышащим учащимся восприятие учебного материала и взаимодействие; виртуальная и дополненная реальности помогают достигать высоких результатов; специальные программы дают возможность слабовидящим работать с учебным материалом [Garg, Sharma, 2020]. Большинство таких разработок созданы для детей школьного возраста, и в сфере высшего образования разработка учебных материалов или помощников на основе ИИ для людей с ограниченными возможностями здоровья имеет большие перспективы.…”

Section: итого найдено статейunclassified

Artificial Intelligence for Learning Analytics and Instructional Design Steps: An Overview of Solutions

Drugova

Zhuravleva

Zakharova

et al. 2022

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Artificial intelligence methods are getting frequently used in research and development in learning analytics, which is aimed at analyzing data collected during learning to enhance its results. The same aim is relevant for instructional design models, the most widely applied is ADDIE model, which cuts course design into steps. The first two research fields are criticized for a weak connection to teaching practice, while the third lacks evidence-based and measurable nature. This literature review aims to show the prospects of bringing the three fields together. The theoretical analysis of the paper covers AI definition, its techniques and methods, areas of application in educational setting, the definition of learning analytics, its borders with other fields, spheres of application, definition and the essence of instructional design, as well as the concept of ADDIE model which frames the practical analysis of the review. Forty-three articles were included in the final sample. The solutions described there correlate with the tasks of the instructional design steps and are systematized according to them. It was found that the least number of solutions described in the literature were assigned to the analysis, design and evaluation steps, more articles were assigned to the development step, and the largest number of papers considered the application step. It can be due to the difference in the availability of data at different ADDIE steps. The weak focus of teachers on methodological reflection at the assessment step also may play a role. These deficiencies open up the opportunities for future research and developments. To push these solutions forward it is crucial to elaborate on the models, to move from anecdotal experiments to a wide-scale practice, and to enhance required competencies among the faculty. The questions and conclusions presented in the article help to set a new pedagogically-oriented framework for discussions of AI and learning data analytics.

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Impact of Artificial Intelligence in Special Need Education to Promote Inclusive Pedagogy

Cited by 65 publications

References 7 publications

[Retracted] Implementing the Hybrid Neuro‐Fuzzy System to Model Specific Learning Disability in Special University Education Programs

[Retracted] Implementing the Hybrid Neuro‐Fuzzy System to Model Specific Learning Disability in Special University Education Programs

Workplace health surveillance and COVID-19: algorithmic health discrimination and cancer survivors

Artificial Intelligence for Learning Analytics and Instructional Design Steps: An Overview of Solutions

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