Explainable Artificial Intelligence for Training and Tutoring

By H. Chad Lane, Mark G. Core, Michael van Lent, Steve Solomon, Dave Gomboc. 2005

68 citations recorded by the source corpus.

Subfields: Safety, Robustness & Interpretability

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In Safety, Robustness & Interpretability

Citations in the corpus

Measuring and Improving BERT’s Mathematical Abilities by Predicting the Order of Reasoning. (2021, background): “Symbolic or semi-symbolic architectures naturally produce justiﬁcations as a sequence of formulas in some formal language (Lane et al., 2005; Core et al., 2006; Lomas et al., 2012; Johnson; Liang, 2016; Malinowski and Fritz, 2014).”
Generating Visual Explanations (2016, background): “In contrast to systems like [13,14,15,16,17,18] which aim to explain the underlying mechanism behind a decision, authors in [1] concentrate on why a prediction is justiﬁable to a user.”
Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “…Allan, and Eggan, 1988; Guerlain, 1995; Haddawy, Jacobson, and Kahn, 1997; Katz and Lesgold, 1992; Kehoe, Stasko, and Taylor, 2001; Lajoie, 2009; Lane et al., 2005; Lesgold et al., 1988; Loksa et al., 2016; Mark and Simpson, 1991; McGuinness and Borgida, 1995; Merrill, 1987; Miller, 1988;…”
Multimodal Explanations: Justifying Decisions and Pointing to the Evidence (2018, background): “, medical [31] and feedback for teaching programs [19, 32, 9]).”
Grounding Visual Explanations (2018, methodology): “Early textual explanation models are applied to medical images [25] and developed as a feedback for teaching programs [12,27,3].”
Attentive Explanations: Justifying Decisions and Pointing to the Evidence (2016, background): “[39] proposes explanation systems for medical applications, [24, 40, 10] propose explanations as a feedback to improve simulated training for special teaching programs, [28] proposes a model to explain robot actions, [23] evaluates methods developed to explain Bayesian networks and [21] proposes a s”
Explainable Artificial Intelligence: a Systematic Review (2020, background): “Based in these early studies, [123, 124, 125, 126] proposed a modular architecture for explaining the behavior of simulated entities in military simulations.”
The role of emotion in self-explanations by cognitive agents (2017, background): “In many AI applications users require insight into the motivations behind a system’s decisions [16, 22].”
Explainable artificial intelligence for breast cancer: A visual case-based reasoning approach (2019, background): “XAI has been particularly studied in the military domain [13,14].”