Artificial intelligence (AI) science fair project:
Improving learning through auto-generated study questions

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Project Information
Title: Improving learning through auto-generated study questions
Subject: Artificial intelligence (AI)
Subcategory: Machine Learning
Grade level: Jigh School - Grades 9-12
Academic Level: Advanced
Project Type: Building / Engineering
Cost: Low
Awards: Google Science Fair Finalist
Affiliation: Google Science Fair
Year: 2015
Materials and Techniques: Django framework, Restricted Boltzmann Machine, Stanford Parser
Concepts: Machine Learning, Artificial intelligence (AI), Automatic Question Generation, Natural Language Processing, Machine Learning, Topic Model, Support Vector Machine
Description: Online texts are typically not accompanied by instructional material such as review questions, and practice assessments that are crucial in helping students reinforce the relevant concepts. Furthermore, the manual crafting of varied questions for every relevant online text is extremely time consuming. Automatic Question Generation (AQG) shows incredible promise here. First, was build upon advances in Natural Language Processing and Machine Learning an algorithm that automatically generate gap-fill multiple choice questions by using topic models to select topically important and coherent sentences from the text to ask about. Finally, the system was evaluated with a group of students to gain insights into the effectiveness of the system.

Natural Language Processing

Natural language processing (NLP) is a field of computer science, artificial intelligence, and computational linguistics concerned with the interactions between computers and human (natural) languages. As such, NLP is related to the area of human–computer interaction. Many challenges in NLP involve: natural language understanding, enabling computers to derive meaning from human or natural language input; and others involve natural language generation.

The history of NLP generally starts in the 1950s, although work can be found from earlier periods. In 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence.

Modern NLP algorithms are based on machine learning, especially statistical machine learning. The paradigm of machine learning is different from that of most prior attempts at language processing. Prior implementations of language-processing tasks typically involved the direct hand coding of large sets of rules. The machine-learning paradigm calls instead for using general learning algorithms — often, although not always, grounded in statistical inference — to automatically learn such rules through the analysis of large corpora of typical real-world examples. A corpus (plural, "corpora") is a set of documents (or sometimes, individual sentences) that have been hand-annotated with the correct values to be learned.

Some of the most commonly researched tasks in NLP: Question answering, Automatic summarization, Machine translation, Natural language understanding, Part-of-speech tagging, Speech recognition

Question answering: Given a human-language question, determine its answer. Typical questions have a specific right answer (such as "What is the capital of Canada?"), but sometimes open-ended questions are also considered (such as "What is the meaning of life?"). Recent works have looked at even more complex questions.

NLP research is gradually shifting from lexical semantics to compositional semantics and, further on, narrative understanding. Human-level natural language processing, however, is an AI-complete problem. That is, it is equivalent to solving the central artificial intelligence problem—making computers as intelligent as people, or strong AI. NLP's future is therefore tied closely to the development of AI in general.

See also:
Natural Language Processing
Question Answering

Source: Wikipedia (All text is available under the terms of the Creative Commons Attribution-ShareAlike License)

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