As the Internet became the dominant text of our generation, researchers at the University of Connecticut’s New Literacies Research Lab studied how integrating these online texts interacted with more traditional literacy practices. This work examined Online Reading Comprehension and helped identify the skills necessary as students read, write, and communicate online. 

Research in this nascent field was met with a certain amount of skepticism as questions were asked in and out of education circles about whether this counted as reading. 

Change is the one constant in this field. As the researchers started their work, the internet browser required that users navigate to a search engine and type their query into a search box. Developers quickly recognized that users had difficulty with this specificity and relatively quickly added the ability to type your search terms in the address bar. 

Things changed once again in 2008 when Google introduced Autocomplete, a feature that makes it faster to complete searches that you’re beginning to type. Google indicated that this microinteraction was added to help people save time as they can read faster than they can type, as well as correct spelling mistakes. 

As researchers at the New Literacies Research Lab were studying online reading comprehension assessments, they noticed that Google started suggesting keywords and search terms as students entered their queries. In a way, the machine was assisting students as they engaged in an inquiry online. The challenge was that there was no way to shut off the feature or understand where the suggestions were coming from. Google later indicated that these predictions were composed of personal results, trending searches, and related searches. 

For years, machine learning tools and artificial intelligence (AI) virtual assistants in the form of algorithms have been assisting and influencing individuals as they interact with the web. AI is defined in this piece as the ability of a system to learn, speak and solve problems as humans do, adapting to external data to achieve desired results.

This may come in the form of spell check or autocomplete services as you write. It may be the content recommendation services that your video streaming service uses to serve you another movie or show. These tools may also provide social media content that it believes you will interact with (either positively or negatively). The key purpose is to attract your attention and keep you using the tool, product, or service. The algorithm learns as you interact and data gets collected. The more time you spend interacting with the AI virtual assistant, the more it learns and the more benefit it can hopefully provide. 

The addition of AI virtual assistants to our use of digital texts and tools has been subtle. It may appear as the perfect route to make it through traffic, or a YouTube video that helps you figure out how to make an omelet. 

The recent launch of tools like ChatGPT has brought about a laser-like focus on the role of AI virtual assistants in our lives, especially in our classrooms. Although it is not easy to understand the values, motivations, and terms of use for these novel AI tools, I find them worthwhile as they make the interactions with the algorithm more overt. These are most certainly not a microinteraction like a Grammarly suggestion. In some instances, this mimics a full-on conversation with a virtual assistant. 

There are very clear concerns about AI as it enters our classrooms. We must consider if it is appropriate to give private companies control and accountability for student learning and if the hidden systems and goals of these companies won’t conflict with student growth. Finally, we must ask ourselves whether we want children to grow up in an educational world driven by data and machine learning. 

One thing appears to be true at this early point, AI will not substitute for the human connections and emotional aspects of learning. AI will not replace teachers and educators.

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Cover Photo by Maria Lupan on Unsplash