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Why Explainable AI May Not Be Enough: Predictions and Mispredictions in Decision Making in Education
Mohammed Saqr; Sonsoles López-Pernas – Smart Learning Environments, 2024
In learning analytics and in education at large, AI explanations are always computed from aggregate data of all the students to offer the "average" picture. Whereas the average may work for most students, it does not reflect or capture the individual differences or the variability among students. Therefore, instance-level…
Descriptors: Artificial Intelligence, Decision Making, Predictor Variables, Feedback (Response)
Yamauchi, Taisei; Flanagan, Brendan; Nakamoto, Ryosuke; Dai, Yiling; Takami, Kyosuke; Ogata, Hiroaki – Smart Learning Environments, 2023
In recent years, smart learning environments have become central to modern education and support students and instructors through tools based on prediction and recommendation models. These methods often use learning material metadata, such as the knowledge contained in an exercise which is usually labeled by domain experts and is costly and…
Descriptors: Mathematics Instruction, Classification, Algorithms, Barriers
Lin, Chien-Chang; Huang, Anna Y. Q.; Lu, Owen H. T. – Smart Learning Environments, 2023
Sustainable education is a crucial aspect of creating a sustainable future, yet it faces several key challenges, including inadequate infrastructure, limited resources, and a lack of awareness and engagement. Artificial intelligence (AI) has the potential to address these challenges and enhance sustainable education by improving access to quality…
Descriptors: Artificial Intelligence, Educational Technology, Sustainability, Technology Uses in Education