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An alternative approach is the use of Artificial Intelligence (AI) techniques to parse the learner's response – so-called intelligent CALL – but there is a gulf between those who favour the use of AI to develop CALL programs (Matthews 1994) [77] and, at the other extreme, those who perceive this approach as a threat to humanity (Last 1989: ...
Prolog is particularly useful for symbolic reasoning, database and language parsing applications. Artificial Intelligence Markup Language (AIML) [11] is an XML dialect [12] for use with Artificial Linguistic Internet Computer Entity (A.L.I.C.E.)-type chatterbots. Planner is a hybrid between procedural and logical languages. It gives a ...
Although AI seems to be evolving rapidly, it faces many technical challenges. For example, in many cases the language used by AI is very vague, and thus confusing for the user to understand. In addition, there is a "black-box problem" [11] [10] in which there is a lack of transparency and interpretability in the language of AI outputs. In ...
Pronounced "A-star". A graph traversal and pathfinding algorithm which is used in many fields of computer science due to its completeness, optimality, and optimal efficiency. abductive logic programming (ALP) A high-level knowledge-representation framework that can be used to solve problems declaratively based on abductive reasoning. It extends normal logic programming by allowing some ...
Natural-language programming (NLP) is an ontology-assisted way of programming in terms of natural-language sentences, e.g. English. [1] A structured document with Content, sections and subsections for explanations of sentences forms a NLP document, which is actually a computer program.
The goal of most of these authoring tools is to simplify the tutor development process, making it possible for people with less expertise than professional AI programmers to develop Intelligent Tutoring Systems. Eight principles of ITS design and development
A large language model (LLM) is a type of machine learning model designed for natural language processing tasks such as language generation. LLMs are language models with many parameters, and are trained with self-supervised learning on a vast amount of text.
In DDL, students use the same types of tools that professional linguists use, namely a corpus of texts that have been sampled and stored electronically, and a concordancer, which is a search engine designed for linguistic analysis. Some tools have been specifically created for data-driven learning, such as SkELL, WriteBetter, and Micro-concord.