Methodology for building an intelligent tutoring system

By William J. Clancey. 1987

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Citations in the corpus

Explanation in Human-AI Systems: A Literature Meta-Review, Synopsis of Key Ideas and Publications, and Bibliography for Explainable AI (2019, background): “ease of development. NEOMYCIN did that in providing a DARPA#TAB#XAI#TAB#Literature#TAB#Review p.#TAB#50 diagnostic procedure (tasks/metarules) and associated language of “dysfunction” taxonomic and causal relations (Clancey, 1984, 1986a). It was recognized early during the development of explainable”
Metrics for Explainable AI: Challenges and Prospects (2018, methodology): “ecisions that are based on personal information. Future laws may restrict AI, which represents a challenge to industry. Expert Systems researchers have previously implemented methods for explanation (Clancey, 1984, 1986; McKeown & Swartout, 1987; Moore & Swartout, 1990). In a sense, explanat”
Definition, framework and research issues of smart learning environments - a context-aware ubiquitous learning perspective (2014, methodology): “…evaluates students ’ learning status and performance, an instructional model or pedagogical knowledge model that determines teaching content, educational tools and presentation methods based on the outcomes of the student model, and a user interface for interacting with students (Clancey 1984).”
From Guidon to Neomycin and Heracles in twenty short lessons (1986, background): “The interested reader will find the metarules listed in (Clancey 1984c), with a discussion of the procedure in terms of operators and the cognitive, social, mathematical, and case population constraints implicit in the rules.”