3 problems of Artificial Intelligence in Education

Changes in demographics and technology have always been the catalyst for new forms of education platform services. With the advent of AI technology, the core part of the Fourth Industrial Revolutioт the education service industry is being transformed by the introduction of new methods and trends. Examples can be seen all over the educational services industry as companies are starting to utilize Artificial Intelligence Methodology, Machine Learning, and Deep Learning as the underlying technologies commercialized due to high public interest.

However, in order to further refine and fully realize AI-based education services, more testing and validation is required. Nevertheless, many companies are promoting their services as smart AI education products, while not really addressing the following key problems.

Low Data Quality

One significant problem with machine learning- based education services is that the form of data collection derived from users is extremely raw which can be largely unsuitable for building any meaningful model. To resolve this issue, the design of a module that can collect and structure significant data is necessary. Without this process being done properly, the expected AI-based learning services that can actually benefit the consumers (students) cannot be provided because it is implemented based on inferior or incomplete data. Currently, machine learning educational services are based primarily on a faulty basic data collection model. Global companies make promises through massive marketing campaigns and offer services for AI and deep learning which claim to overcome these limitations of the low data quality.

Yet, to date, these AI based products fail to reach an adequate level of sophistication expected from true AI because of these poor data issues. The ANW Learning Management System (LMS) will solve this by relying on the ANW’s inference engine to provide clean relevant data.

Improper Use of Machine Learning.

Data quality and relevance is just one example of the many elements of significantly overestimated capabilities in relation to companies applying machine learning methods. Even with proper support data, consulting measures, and correct application services relying on data regression methods alone fail to produce accurate results. The first step to solving this problem is to struc- ture the quantified data and let go of the assumption that the values obtained via machine learning are the final meaningful extrapolations that can be produced by AI.

Limitation of the Data-Driven Approach

The productivity of the human language is limitless. Thus, no amount of big data collection can fully under- stand the infinite sentence structures of natural language. Because of the limitation of dismantling sentences into keywords and conducting related parsing rather than complete sentence parsing, the majority of the soclaimed “AI-based services” remain at the level of simple pretense or simple voice recognition. Furthermore, the core of the AI- based Learning Management System (LMS) is to understand the language and to recognize dialogue completely. As such, the ultimate question is posed on whether a Deep Learning algorithm may understand the lan- guage better than linguists and resolve the low relevance problem.