Assessing Perceived Trust and Satisfaction with Multiple Explanation Techniques in XAI-Enhanced Learning Analytics

Brdnik, Saša; Podgorelec, Vili; Šumak, Boštjan

doi:10.3390/electronics12122594

Cited by 2 publications

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“…Consequently, the predictions could have harmful (adversarial) consequences for users if they could be applied in real‐life unexplained, without users’ understanding of how and why they are predicted. Research on XAI shows that introducing explanations in AI systems to illustrate their reasoning to end users can improve transparency, interpretability, understanding, satisfaction, and trust (Brdnik et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

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NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture

Fister,

Podgorelec

et al. 2023

Expert Systems

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Numerical association rule mining (NARM) is a popular method under the umbrella of data mining, focused on finding relationships between attributes in transaction databases. Numerical association rules for time series are a new paradigm that extends the applicability of NARM to the domain of time series. Association rule mining algorithms result in numerous rules, the interpretation of which is sometimes not easy for human experts. Therefore, various visualization methods have been developed to improve the explanation results of the rule mining process. This article is a novel contribution to the development of a new visualization method capable of presenting the association rules for time series developed according to the principles of explainable artificial intelligence. The experiments are conducted in the context of smart agriculture (i.e., agricultural time series data), and show the great potential of the proposed visualization method for the future.

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Section: Introductionmentioning

confidence: 99%

“…of how and why they are predicted. Research on XAI shows that introducing explanations in AI systems to illustrate their reasoning to end users can improve transparency, interpretability, understanding, satisfaction, and trust(Brdnik et al, 2023).…”

mentioning

confidence: 99%

NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture

Fister,

Podgorelec

et al. 2023

Expert Systems

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Using explainable AI to unravel classroom dialogue analysis: Effects of explanations on teachers' trust, technology acceptance and cognitive load

Wang,

Bian,

Chen

2024

Brit J Educational Tech

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Deep neural networks are increasingly employed to model classroom dialogue and provide teachers with prompt and valuable feedback on their teaching practices. However, these deep learning models often have intricate structures with numerous unknown parameters, functioning as black boxes. The lack of clear explanations regarding their classroom dialogue analysis likely leads teachers to distrust and underutilize these AI‐powered models. To tackle this issue, we leveraged explainable AI to unravel classroom dialogue analysis and conducted an experiment to evaluate the effects of explanations. Fifty‐nine pre‐service teachers were recruited and randomly assigned to either a treatment (n = 30) or control (n = 29) group. Initially, both groups learned to analyse classroom dialogue using AI‐powered models without explanations. Subsequently, the treatment group received both AI analysis and explanations, while the control group continued to receive only AI predictions. The results demonstrated that teachers in the treatment group exhibited significantly higher levels of trust in and technology acceptance of AI‐powered models for classroom dialogue analysis compared to those in the control group. Notably, there were no significant differences in cognitive load between the two groups. Furthermore, teachers in the treatment group expressed high satisfaction with the explanations. During interviews, they also elucidated how the explanations changed their perceptions of model features and attitudes towards the models. This study is among the pioneering works to propose and validate the use of explainable AI to address interpretability challenges within deep learning‐based models in the context of classroom dialogue analysis.Practitioner notesWhat is already known about this topic Classroom dialogue is recognized as a crucial element in the teaching and learning process. Researchers have increasingly utilized AI techniques, particularly deep learning methods, to analyse classroom dialogue. Deep learning‐based models, characterized by their intricate structures, often function as black boxes, lacking the ability to provide transparent explanations regarding their analysis. This limitation can result in teachers harbouring distrust and underutilizing these models. What this paper adds This paper highlights the importance of incorporating explainable AI approaches to tackle the interpretability issues associated with deep learning‐based models utilized for classroom dialogue analysis. Through an experimental study, this paper demonstrates that providing model explanations enhances teachers' trust in and technology acceptance of AI‐powered classroom dialogue models, without increasing their cognitive load. Teachers express satisfaction with the model explanations provided by explainable AI. Implications for practice and/or policy The integration of explainable AI can effectively address the challenge of interpretability in complex AI‐powered models used for analysing classroom dialogue. Intelligent teaching systems designed for classroom dialogue can benefit from advanced AI models and explainable AI approaches, which offer users both automated analysis and clear explanations. By enabling users to understand the underlying rationale behind the analysis, the explanations can contribute to fostering trust and acceptance of the AI models among users.

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Assessing Perceived Trust and Satisfaction with Multiple Explanation Techniques in XAI-Enhanced Learning Analytics

Cited by 2 publications

References 36 publications

NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture

NarmViz: A novel method for visualization of time series numerical association rules for smart agriculture

Using explainable AI to unravel classroom dialogue analysis: Effects of explanations on teachers' trust, technology acceptance and cognitive load

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