Applications of artificial intelligence within education

Jones, Marlene

doi:10.1016/0898-1221(85)90054-9

Cited by 26 publications

(6 citation statements)

References 10 publications

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“…Methodologically, our study aligns with the rigorous approaches advocated by Jones (1985), Panigrahi (2020), and Ayala-Pazmiño (2023). The amalgamation of quantitative assessments through statistical analyses, including paired sample t-tests, with qualitative feedback from questionnaires reflects a methodological strength that resonates with broader literature on AI in education.…”

Section: Discussionsupporting

confidence: 59%

“…A range of studies has explored the use of Artificial Intelligence in educational settings, highlighting its potential to enhance learning experiences and improve teaching quality. Jones (1985) and Manhiç a (2022) both discuss the development of intelligent computer-assisted instruction systems and learning management systems, with the latter focusing the use of AI for student performance assessment. Panigrahi (2020) and Fahimirad (2018) further emphasize the transformative impact of AI on education, particularly in personalized learning, and the potential for enhancing learning outcomes.…”

Section: A Ai Applications In Educationmentioning

confidence: 99%

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Embedding Artificial Intelligent Applications in Higher Educational Institutions to Improve Students’ Pronunciation Performance

Al-Shallakh

2024

tpls

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This study investigates the integration of Elsa Speak, an Artificial Intelligence (AI) application, into higher education to enhance the pronunciation skills of English Foreign Language Learners (EFLL) at a Jordanian university. Grounded in a comprehensive literature review, this study adopts a mixed-methods approach, utilizing a pre-post-test-controlled group design with a sample of 12 students. The seven-week intervention employing Elsa Speak demonstrates a significant improvement in pronunciation skills, supported by statistical analyses and positive student perceptions gathered through questionnaires. The findings contribute to the broader discourse on AI in education, highlighting the practical impact of such applications on language learning, specifically pronunciation improvement in higher education. The study advocates the continued exploration and strategic integration of AI tools, such as Elsa Speak, into language education practices.

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Section: Discussionsupporting

confidence: 59%

Section: A Ai Applications In Educationmentioning

confidence: 99%

Embedding Artificial Intelligent Applications in Higher Educational Institutions to Improve Students’ Pronunciation Performance

Al-Shallakh

2024

tpls

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“…Similarly, prior works from Jones [14] and Beck et al [15], provided exposition of the usage of computers as a resource in education. One of the key observations regarding these articles is that limitations such as programming language compatibility, resource constraints and inability to conduct conversations with the student in the student's natural language can now be mitigated with the technology currently available.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

Taylor,

Gupta,

Jung

2024

Preprint

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As data driven models gain importance in driving decisions and processes, recently it becomes increasingly important to visualize the data with both speed and accuracy. A massive amount of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. Visual analytics of educational big data has capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. How-ever, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense etc. leverage artificial intelligence and machine learning techniques to visualize data and generate insights out of it, however their ap-plicability in educational advances is limited. This paper focuses on leveraging machine learn-ing and visualization techniques to demonstrate their utility through a practical implementation using k-12 state assessment data compiled from the institutional websites of the State of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad-hoc visualization provides contextual anal-ysis within areas of concern for Education Agencies (EA).

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“…Similarly, prior works from Jones [14] and Beck et al [15] provided an exposition of the usage of computers as a resource in education. One of the key observations regarding these articles is that limitations such as programming language compatibility, resource constraints, and inability to conduct conversations with the student in the student's natural language can now be mitigated with the technology currently available.…”

Section: Related Workmentioning

confidence: 99%

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

Taylor,

Gupta,

Jung

2024

MTI

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As data-driven models gain importance in driving decisions and processes, recently, it has become increasingly important to visualize the data with both speed and accuracy. A massive volume of data is presently generated in the educational sphere from various learning platforms, tools, and institutions. The visual analytics of educational big data has the capability to improve student learning, develop strategies for personalized learning, and improve faculty productivity. However, there are limited advancements in the education domain for data-driven decision making leveraging the recent advancements in the field of machine learning. Some of the recent tools such as Tableau, Power BI, Microsoft Azure suite, Sisense, etc., leverage artificial intelligence and machine learning techniques to visualize data and generate insights from them; however, their applicability in educational advances is limited. This paper focuses on leveraging machine learning and visualization techniques to demonstrate their utility through a practical implementation using K-12 state assessment data compiled from the institutional websites of the States of Texas and Louisiana. Effective modeling and predictive analytics are the focus of the sample use case presented in this research. Our approach demonstrates the applicability of web technology in conjunction with machine learning to provide a cost-effective and timely solution to visualize and analyze big educational data. Additionally, ad hoc visualization provides contextual analysis in areas of concern for education agencies (EAs).

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Applications of artificial intelligence within education

Cited by 26 publications

References 10 publications

Embedding Artificial Intelligent Applications in Higher Educational Institutions to Improve Students’ Pronunciation Performance

Embedding Artificial Intelligent Applications in Higher Educational Institutions to Improve Students’ Pronunciation Performance

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

Leveraging Visualization and Machine Learning Techniques in Education: A Case Study of K-12 State Assessment Data

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