Augmented Intelligence Coaching System

Pratsri, Sajeewan; Nilsook, Prachyanun; Wannapiroon, Panita

doi:10.1109/ri2c51727.2021.9559817

Cited by 5 publications

(3 citation statements)

References 18 publications

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“…It is the important step that encourages learners to absorb the content. The main goal is to present content that is interesting and understandable according to individual learning styles [9] [18]. The third step, action and expression, is the step stimulating learners' brains to think during practices and expression by applying the knowledge.…”

Section: Discussionmentioning

confidence: 99%

“…Originally, AI was developed to mimic human behaviours by learning from examples, gaining experiences, memorising objects, comparing understanding, responding to languages and symbols, making decisions and solving problems similarly to humans [17]. However, AI tends to perceive and react to people automatically and naturally, so it can be a facilitator for learners to study by themselves [18]. AI will ultimately be a virtual assistant for each learner and will observe and analyse the learner efficiently [19].…”

Section: Introductionmentioning

confidence: 99%

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IVUL Model: An Intelligent Learning Development Process

Chaiyarak¹,

Nilsook²,

Wannapiroon³

2022

Int. J. Emerg. Technol. Learn.

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The purposes of this research study are to develop an intelligent virtual universal learning (IVUL) model and to evaluate its appropriateness. The study consisted of two phases. Phase 1 involved the development of the IVUL model for univer-sal learning. The conceptual frameworks and theories in the documents and re-search studies on universal design for learning, intelligent learning and virtual learning were studied by the researchers. All main components were then synthe-sised to design the IVUL model. This process can be divided into three main steps: engagement, representation, and action and expression. Each step has sub-steps: access, build and internalise. However, the details of these are dependent on the main steps. The second component is the intelligent learning process, an important process of the model that drives learners to automatically learn by themselves. Artificial intelligence is used as a crucial component that promotes and supports each learner to meet learning goals and objectives according to the universal learning model. The third component is the virtual learning process, which results in learning through computer environments and the Internet. Phase 2 involved an evaluation of the appropriateness of the IVUL model, with in-depth interviews with 20 experts in education and digital technologies. The appropriate-ness of the model was evaluated using the 5-point Likert scale. The findings show that the designed IVUL model can be used for learning development at the highest level.

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Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

IVUL Model: An Intelligent Learning Development Process

Chaiyarak¹,

Nilsook²,

Wannapiroon³

2022

Int. J. Emerg. Technol. Learn.

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“…Contrarily, in environments with limited resources, the ability of AI-based DSS to develop these abilities may be hampered, which could have an influence on employability (Jokhan et al, 2022). BDA can be used widely at resource-rich educational institutions to analyze large datasets, tailor learning experiences, and give students with data-driven insights (Pratsri, Nilsook, & Wannapiroon, 2021). Students have access to and use BDA tools to stimulate innovative thinking in such situations, where financial and technological resources are available.…”

Section: Resource Availability As a Moderatormentioning

confidence: 99%

AI and big data-driven decision support for fostering student innovation in music education at private underground colleges

Liu

2023

J INFORM SYSTEMS ENG

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This study investigates the transformative impact of AI-based Decision Support Systems (DSS) and Big Data Analytics (BDA) on student innovation and employability skills in an era of rapid technological advancement, with a focus on the mediating role of technological acceptance and the moderating role of resource availability. This study, which draws on a wide range of educational contexts and data sources, gives complete knowledge of the complex links between technology adoption, student results, and contextual factors. The results of this study show how AI-based DSS and BDA have a significant impact on musical education. These technological advancements enable tailored instruction and foster students' creative thinking. In order to prepare students for a work market that is rapidly changing, they act as a catalyst for improving employability skills. The study, however, emphasizes the complicated dynamics at work. Technological Acceptance emerges as a major mediating component, underlining the significance of students and instructors freely and effectively accepting technological tools. Furthermore, as a moderating factor, Resource Availability takes center stage, emphasizing the need for equitable access to educational resources to ensure that technology-driven advantages are accessible to all. The results of this study have broad repercussions. The adoption of AI and BDA by educational institutions is encouraged as transformative technologies for enhancing the learning process. Policymakers must create regulations that support equal access to technology and promote an innovative culture in the classroom. This study highlights for students how important it is to adopt new technologies, realizing how important they are in determining both their academic and career paths.

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