An additional problem that had the potential to provide an interesting challenge was that of evaluating the relative reading level of a sample of text.

1. The Problem

While many databases exist that rate the reading level on various scales, whether it is by grade level or on some other numeric scale, there is a lack of tools to dynamically evaluate the complexity of an arbitary piece of text that could be sourced from an article, website, book, or many others. An application could be developed that would allow a user to paste a sample of a text into an input field, and then return a numeric rating or educational level corresponding to that text. This way, educators would have a reliable method for determining whether a piece of writing was appropriate in terms of difficulty for that age level.

The system would need to take in several criteria as input, such as the level of difficulty of the vocabulary used and the complexity of the sentence structures found in the text. These values could then be input into a rule-based system to determine the final level of that text.

To expand the project, additional machine learning and AI concepts could be used. Natural Language Processing would be necessary to acurately handle and evaluate the sentence structure of the text among other issues. Categorization methods such as clustering could used to compare texts to others with known, established values, or a machine learning system such as a neural network could be trained against a large database of texts with known values.

2. Expert Sources

As a team we discussed, and even briefly described this project to a few of our family and friends who are experts in this area, including elementary and high school teachers, as well as those with degrees in English. Additional experts who would be necessary to the completion of the project would likely come from an Ohio State department such as Education or Linguistics.

3. Our Motivation

As a group, this project was initially appealing because it provided a flexible and daunting challenge. The problem has a somewhat subjectively defined solution, and yet, if it were to work successfully it could provide a very useful tool for educators. Additionally, it allows a very convenient segue into other AI areas of interest such as Natural Language Processing and text categorization.

4. Details

Upon researching the project further, it became apparent that a large portion of what would make this project challenging was finding accurate and reliable knowledge bases, beyond our experts, to help define the behavior of the system. It would be necessary to have access to large amounts of texts, possibly by scraping sites like Google Books in conjunction with an existing database such as that found at AR BookFinder. Additional challenges arise from the subjectivity of rating a concept like reading level; our experts would need to provide tangible feedback on the “correctness” of a nebulous concept.