Explainable Artificial Intelligence in education

Khosravi, Hassan; Shum, Simon Buckingham; Chen, Guanliang; Conati, Cristina; Gašević, Dragan; Kay, Judy; Knight, Simon; Martínez-Maldonado, Roberto; Sadiq, Shazia; Tsai, Yi‐Shan

doi:10.1016/j.caeai.2022.100074

Cited by 189 publications

(111 citation statements)

References 62 publications

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“…Also, Vilone and Longo (2022) propose some kind of conversational, argument-based, explanation system for a machine-learned model, in order to enhance its degree of explainability by employing principles and techniques from computational argumentation that frame the act of explaining as akin to non-monotonic logic. Finally, we can find also applications of Human-centred XAI for education, as in Khosravi et al (2022). In particular, Khosravi et al (2022) present a framework that considers six key aspects in relation to explainability for studying, designing and developing educational AI tools.…”

Section: Xai and Question Answeringmentioning

confidence: 99%

“…Finally, we can find also applications of Human-centred XAI for education, as in Khosravi et al (2022). In particular, Khosravi et al (2022) present a framework that considers six key aspects in relation to explainability for studying, designing and developing educational AI tools. These key aspects focus on the stakeholders, benefits, approaches for presenting explanations, widely used classes of AI models, humancentred designs of the AI interfaces and potential pitfalls of providing explanations.…”

Section: Xai and Question Answeringmentioning

confidence: 99%

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Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex data

Sovrano

Vitali

2022

Data Min Knowl Disc

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In this paper we introduce a new class of software tools engaged in delivering successful explanations of complex processes on top of basic Explainable AI (XAI) software systems. These tools, that we call cumulatively Explanatory AI (YAI) systems, enhance the quality of the basic output of a XAI by adopting a user-centred approach to explanation that can cater to the individual needs of the explainees with measurable improvements in usability. Our approach is based on Achinstein’s theory of explanations, where explaining is an illocutionary (i.e., broad yet pertinent and deliberate) act of pragmatically answering a question. Accordingly, user-centrality enters in the equation by considering that the overall amount of information generated by answering all questions can rapidly become overwhelming and that individual users may perceive the need to explore just a few of them. In this paper, we give the theoretical foundations of YAI, formally defining a user-centred explanatory tool and the space of all possible explanations, or explanatory space, generated by it. To this end, we frame the explanatory space as an hypergraph of knowledge and we identify a set of heuristics and properties that can help approximating a decomposition of it into a tree-like representation for efficient and user-centred explanation retrieval. Finally, we provide some old and new empirical results to support our theory, showing that explanations are more than textual or visual presentations of the sole information provided by a XAI.

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Section: Xai and Question Answeringmentioning

confidence: 99%

Section: Xai and Question Answeringmentioning

confidence: 99%

Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex data

Sovrano

Vitali

2022

Data Min Knowl Disc

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“…Other than the shift in mindset with regard to analytic models, the role of human expertise is being increasingly emphasised with regard to machine learning model development: hence the term, human-in-the-loop (Cranor, 2008;Dautenhahn, 1998;Grønsund & Aanestad, 2020). The central importance of humanity in the use of machine learning for educational research and practice has been unpacked very recently by Khosravi and colleagues (Khosravi et al, 2022) in their framework of explainable artificial intelligence for education (XAI-ED). Within this framework, six priorities are proposed, including centrality of stakeholders (e.g., learners, parents, teachers), avoidance of common pitfalls in the use of machine learning (e.g., overly complex models), and thoughtful explanations (i.e., effective and relevant demonstrations and examples).…”

Section: Machine Learning For Online Learning Analysismentioning

confidence: 99%

Access to online learning: Machine learning analysis from a social justice perspective

McIntyre

2022

Educ Inf Technol

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Access to education is the first step to benefiting from it. Although cumulative online learning experience is linked academic learning gains, between-country inequalities mean that large populations are prevented from accumulating such experience. Low-and-middle-income countries are affected by disadvantages in infrastructure such as internet access and uncontextualised learning content, and parents who are less available and less well-resourced than in high-income countries. COVID-19 has exacerbated the global inequalities, with girls affected more than boys in these regions. Therefore, the present research mined online learning data to identify features that are important for access to online learning. Data mining of 54,842,787 initial (random subsample n = 5000) data points from one online learning platform was conducted by partnering theory with data in model development. Following examination of a theory-led machine learning model, a data-led approach was taken to reach a final model. The final model was used to derive Shapley values for feature importance. As expected, country differences, gender, and COVID-19 were important features in access to online learning. The data-led model development resulted in additional insights not examined in the initial, theory-led model: namely, the importance of Math ability, year of birth, session difficulty level, month of birth, and time taken to complete a session.

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“…Here, a normal CNN was employed, and for every CNN, comparative analysis was provided. The authors’ methodology discussed in this study was to contribute an additional feature to the ongoing online teaching–learning curriculum that will provide an extra edge to the teachers by identifying children’s emotions and giving the teachers an overview of the emotional state of the students according to a recent study [ 4 ] by using different XAI techniques, which provide the user explanations that are generated alongside emotion category results that will help to gain teachers’ trust of children. Using XAI, a user can recognize how different features are contributing for each emotion using easily understandable colorful highlighted visualization of different features of the face.…”

Section: Introductionmentioning

confidence: 99%

Kids’ Emotion Recognition Using Various Deep-Learning Models with Explainable AI

Rathod

Dalvi

Kaur

et al. 2022

Sensors

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Human ideas and sentiments are mirrored in facial expressions. They give the spectator a plethora of social cues, such as the viewer’s focus of attention, intention, motivation, and mood, which can help develop better interactive solutions in online platforms. This could be helpful for children while teaching them, which could help in cultivating a better interactive connect between teachers and students, since there is an increasing trend toward the online education platform due to the COVID-19 pandemic. To solve this, the authors proposed kids’ emotion recognition based on visual cues in this research with a justified reasoning model of explainable AI. The authors used two datasets to work on this problem; the first is the LIRIS Children Spontaneous Facial Expression Video Database, and the second is an author-created novel dataset of emotions displayed by children aged 7 to 10. The authors identified that the LIRIS dataset has achieved only 75% accuracy, and no study has worked further on this dataset in which the authors have achieved the highest accuracy of 89.31% and, in the authors’ dataset, an accuracy of 90.98%. The authors also realized that the face construction of children and adults is different, and the way children show emotions is very different and does not always follow the same way of facial expression for a specific emotion as compared with adults. Hence, the authors used 3D 468 landmark points and created two separate versions of the dataset from the original selected datasets, which are LIRIS-Mesh and Authors-Mesh. In total, all four types of datasets were used, namely LIRIS, the authors’ dataset, LIRIS-Mesh, and Authors-Mesh, and a comparative analysis was performed by using seven different CNN models. The authors not only compared all dataset types used on different CNN models but also explained for every type of CNN used on every specific dataset type how test images are perceived by the deep-learning models by using explainable artificial intelligence (XAI), which helps in localizing features contributing to particular emotions. The authors used three methods of XAI, namely Grad-CAM, Grad-CAM++, and SoftGrad, which help users further establish the appropriate reason for emotion detection by knowing the contribution of its features in it.

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Explainable Artificial Intelligence in education

Cited by 189 publications

References 62 publications

Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex data

Explanatory artificial intelligence (YAI): human-centered explanations of explainable AI and complex data

Access to online learning: Machine learning analysis from a social justice perspective

Kids’ Emotion Recognition Using Various Deep-Learning Models with Explainable AI

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