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May 28, 2018 1:37:00 PM Jaeques Koeman

3 Ways Publishers Are Unintentionally Sabotaging Their Content (And What to do about it)

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It's no secret that the classroom is rapidly changing. Technology is changing how we learn at an exponential rate that we couldn't have predicted 30 years ago. 

Understandably, education publishers are having a hard time keeping up with the changes in classroom learning. While they were able to adapt to the technologies of the 20th century, digital and artificial intelligence (AI) technology is stumping publishers. Nevertheless, AI will impact the way publishers work and operate.

The reality is that there will no longer be a "one-size-fits-all" model of education anymore. Instead, AI-driven solutions will make learning a deeply individualized experience for any type of learner.

After nearly 15 years of building AI solutions for publishers, we’ve noticed there are three ways that publishers struggle with content management, namely:

  • Neglect
  • Disconnected Data
  • A lack of knowledge about AI in Education

Frankly, these problems might appear small. But in reality, these problems are ways that hinder publishers from managing their content in a smart way. And that has significant implications for how your company will grow in the future.

In this post, we’ll explore these three hurdles a little further in detail, as well as provide advice on how you can overcome them.

Problem #1: Neglect

Successful content management is entirely dependent on proper data entry.

Unfortunately, data entry is a mind-numbing process. Adding the same information into a spreadsheet over and over is entirely exhausting for a person. Even the best of us get tired of it. We make mistakes, miss important details, and get frustrated. As soon as a more stimulating task comes around, data entry is happily forgotten. It gets pushed to the back burner and quickly becomes neglected overall. 

This boredom is only natural for a person, but it is a disaster for a machine to crawl through corrupt data in a standardized way.

For AI to create adaptive learning experiences, it needs to have access to standardized and properly classified data. Without it, an AI program can’t learn what to look for when searching for relevant content. And if it is not trained, it might not be able to find anything worth presenting as a solution. Even worse, it might curate an experience that isn’t relevant to the learner.

We’ve found in some cases that as much as 40% of the data can be missing. That figure shows that there's already a pressing issue of data neglect for publishers and that results in difficulty in making make data-driven decisions. As a result, neglected data prohibits the proper implementation of adaptive learning, making it downright impossible.

How To Avoid Neglecting Data Management

The most immediate way to get rid of neglect in your data management is to hire data entry specialists. That way there is no excuse for a mountain of neglected data in your organization.

However, simply hiring people to do data entry has its limitations. As we mentioned, people get tired, cranky, and bored with data entry, so mistakes are bound to happen.

With a combination of both data entry professionals and an AI solution like 360AI, combating neglect is fairly easy to manage. The AI checks the meta-tagged data that the data-entry person has entered into the system. The data entry professionals can also check the machine’s meta-tagged data, ensuring that everything is complete and organized with the right taxonomy or the correct learning curriculum. That way all the forms are filled out with the correct metadata.  

Slowly but surely, this process will help you chip away at the massive amount of neglected data that has built up like a mountain over the years. It also makes your data more organized and adaptive for future AI solutions.

Problem #2: Disconnected Data

“Education” is a broad field. There are thousands of subjects and disciplines, and within each one there are even more areas of study. It can feel impossible to know where one discipline ends and where the next one begins.

Despite this overlap, the way publishers organize themselves vary significantly from department to department. A history book might be organized by time period while a political science book might be classified by the type of regime. There is no singular way to organize a piece of content because it will always depend on the respective taxonomy. So if there’s disorganization or inconsistencies within the categorization of content, it will be reflected in the taxonomy.

Humans can bridge these knowledge gaps. For example, if we want to find a book about political regimes in the 20th century, we'll be able to find relevant content. We'll come across books on fascism and the Nazi party, even though we did not search for it directly. We can connect the dots in our heads.

However, AI has a harder time making connections like a human. It can only do so if it has access to correctly tagged metadata from a variety of disciplines. For AI to deliver content that is relevant to a learner, it needs to have as much standardized data to crawl through as possible. With standardized data, AI can search through a broader range of disciplines and find more relevant answers to a question.  As a result, it can give smarter solutions to the learner making students happier and more engaged.

But with no standardization of data across disciplines leads to disgruntled students. First, there's less data for the AI to comb through, which mean fewer intelligent answers to questions. Then fewer responses to a learning question means that the AI is more likely to provide a result that isn’t relevant to the student. And students without relevant data will only become frustrated with the technology.

In this situation, the publisher nor the student wins.

How Educational Publishers Can Get More Connected Data

Managers and supervisors should learn what fields and subjects are mandatory for data entry. These expectations should be clearly delegated to the people who are doing data entry. Additionally, there should be a strict guideline for how to tag metadata. Each data entry professional should have a clear reference guide that they can refer back to in cases of doubt.

AI can also help make this process a little bit easier by helping build a scaffold for the format. As you train the machine with the correct formatting, a standardized form automatically emerges. This format is then applied to all data, regardless of its originating discipline.

Again, by combining AI with human training, the educational content becomes more manageable and connected. This means faster searching of data, easier relational understanding between disciplines and, most importantly, happy students using the platforms.

Problem #3: Lack of Knowledge About the Uses of AI in Education

The final problem that we’ve seen in our experience working with publishers is that they lack knowledge about AI in education. Simply put, publishers don't know how big the mountain is that they need to climb to reach smart content management.

Educational publishing is a profoundly complex and legacy-driven industry. Publishers have vast amounts of content libraries to sift through from years of operating business. Because there is a tremendous amount of content to manage, it’s understandable that change is slow. 

But every day that AI solutions are not adopted, the peak of the mountain grows a little taller.

AI is developing at a pace that most people can’t fathom. The industry is incredibly agile, and every day some new technology emerges that disrupts the market. Every day that passes, existing technologies become increasingly outdated.

Companies that handle large amounts of content, especially if they need to deliver that content to end users, will need to implement a content management system which offers an adaptive solution. Hence, the demand for adaptive content is on the rise across all sectors, especially educational publishing.

As a publisher's content library grows, the need for AI will become bigger. But it will also become more challenging to manage, making it more difficult to implement AI.

Publishers need to educate themselves about the technology—and how it can be used to their advantage - so they don't fall behind.

How You Can Learn More About AI in Education

If you’re interested in pushing your company ahead of the competition, educate yourself about AI Staying on top of the news will help you understand developments as they come and give you insight into the industry.

Here are some of our favorite resources:

You can also always check out our blog. It’s a growing treasure trove of information and insights about AI in education. If you subscribe, you’ll get a new post delivered to your inbox every week.

To seriously address the need for AI in your content management, you might want to check out our AI in Education Consulting page for more details.

Conclusion

Overall, these three sneaky problems inhibit publishers from managing smart content.

First, neglecting data entry leads to a massive backlog that’s impossible to sort through. Then disconnected data prohibits AI from delivering smart solutions. And lastly, a lack of knowledge about the possibilities of AI in education means publishers don’t know how far behind they are in the process.

Human solutions, like hiring data entry professionals, increased training, and reading AI news, do alleviate these problems, but overall, these need to be combined with an AI-driven solution.

That’s why we advocate 360AI for publishers that are ready to launch themselves into the 21st century. It’s a faster, easier, and smarter way to organize your content that will leave you prepared for the future.

Want to know how 360AI helped Malmberg meta-tag and organize data? Click the button below to download our case study.

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