Explainable Ai for Data Driven Learning Analytics: A Holistic Approach to Engage Advisors in Knowledge Discovery

Nur, Nasheen; Nur, Nasheen; ElTayeby, Omar; Dou, Wenwen; Dorodchi, Mohsen; Niu, Xi; Maher, Mary Lou; Chambers, C.N.

doi:10.21125/edulearn.2022.2497

Cited by 3 publications

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“…There are many domains in which XAI has been applied to improve diagnostics and human decision-making tools, including healthcare, manufacturing, banking, education, insurance, autonomous driving, and so on [187][188][189]. Human communication, understanding, and learning depend on explaining decisions, and people naturally provide graphical and textual explanations [190].…”

Section: Human-assisted Ai For Decision-making In Different Domainsmentioning

confidence: 99%

“…The underlying mathematical scaffolding in deep machine learning models can be difficult for mathematicians to fully grasp [190]. By explaining AI, users can gain insight into the "why" behind model predictions, which will help them understand better, trust the model, and recognize and correct any incorrect predictions [188,189,191]. Human-AI interactions have previously been studied based on interpretability, trustworthiness, and usability.…”

Section: Human-assisted Ai For Decision-making In Different Domainsmentioning

confidence: 99%

“…XAI tools can aid us in understanding why ML makes certain decisions by providing explainable AI (XAI) tools. Researchers [189,199] have experimented on how real users would react with realistic explanations generated from a model built over a real dataset.…”

Section: Human-assisted Ai For Decision-making In Different Domainsmentioning

confidence: 99%

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An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

et al. 2023

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In a wide range of industries and academic fields, artificial intelligence is becoming increasingly prevalent. AI models are taking on more crucial decision-making tasks as they grow in popularity and performance. Although AI models, particularly machine learning models, are successful in research, they have numerous limitations and drawbacks in practice. Furthermore, due to the lack of transparency behind their behavior, users need more understanding of how these models make specific decisions, especially in complex state-of-the-art machine learning algorithms. Complex machine learning systems utilize less transparent algorithms, thereby exacerbating the problem. This survey analyzes the significance and evolution of explainable AI (XAI) research across various domains and applications. Throughout this study, a rich repository of explainability classifications and summaries has been developed, along with their applications and practical use cases. We believe this study will make it easier for researchers to understand all explainability methods and access their applications simultaneously.

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Section: Human-assisted Ai For Decision-making In Different Domainsmentioning

confidence: 99%

Section: Human-assisted Ai For Decision-making In Different Domainsmentioning

confidence: 99%

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An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

et al. 2023

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XEdgeAI: A human-centered industrial inspection framework with data-centric Explainable Edge AI approach

Nguyen,

Cao

2025

Information Fusion

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Explainable AI In Education : Current Trends, Challenges, And Opportunities

Rachha

Seyam

2023

SoutheastCon 2023

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Explainable Ai for Data Driven Learning Analytics: A Holistic Approach to Engage Advisors in Knowledge Discovery

Cited by 3 publications

References 0 publications

An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

An Empirical Survey on Explainable AI Technologies: Recent Trends, Use-Cases, and Categories from Technical and Application Perspectives

XEdgeAI: A human-centered industrial inspection framework with data-centric Explainable Edge AI approach

Explainable AI In Education : Current Trends, Challenges, And Opportunities

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