AI and Teaching and Learning

Generative AI (GenAI) is a type of artificial intelligence that can create new content, like text, images, music, videos, or code, by learning patterns from existing data and then generating novel outputs based on those patterns. 

GenAI models are trained on massive datasets, allowing them to identify and learn the underlying structures and relationships within the data. Once trained, these models can generate new content that resembles the data they were trained on, but is not simply a copy of that data. 

The earliest form of GenAI is the development of Chatbots.  Marie Gobiet's "The History of Chatbots - From ELIZA to ChatGPT" (Onlim, 15 February 2024), provides a very detailed list (14 items) and description of the many forms of chatbots starting with the very first chatbot which was developed by MIT professor Joseph Weizenbaum in the 1960s. It was called ELIZA: it uses pattern matching and substitution methodology to simulate conversation. The program was designed in a way that it mimics human conversation. The Chatbot ELIZA worked by passing the words that users entered into a computer and then pairing them to a list of possible scripted responses. It uses a script that simulated a psychotherapist.

Generative AI (GenAI) is still limited in what it can accomplish due to its reliance on data-driven algorithms. While these algorithms may be able to recognize patterns or trends within data sets, they have difficulty understanding context when presented with new information or scenarios outside of their training parameters. Hence GenAI cannot draw conclusions or make decisions based on complex situations — activities that only humans can do at present. Furthermore, GenAI cannot replace human creativity completely as it lacks the ability to formulate novel ideas or recognize abstract concepts such as humor or irony, all of which require a human touch.

While GenAI has the potential to revolutionize many aspects of our lives by taking over time-intensive creative tasks and providing business insights — it still has its limitations. There will always be some tasks which will require human intervention in order for them to truly succeed. As such, we must ensure that we use this tool responsibly if we want it to reach its full potential without sacrificing our own ingenuity in the process.

For an informative analysis of Ai in Education see: U.S. Department of Education, Office of Educational Technology, Artificial Intelligence and Future of Teaching and Learning: Insights and Recommendations, Washington, DC, 2023. (67 pages) .

This report addresses the clear need for sharing knowledge and developing policies for “Artificial Intelligence,” a rapidly advancing class of foundational capabilities which are increasingly embedded in all types of educational technology systems and are also available to the public. We will consider “educational technology” (edtech) to include both (a) technologies specifically designed for educational use, as well as (b) general technologies that are widely used in educational settings. Recommendations in this report seek to engage teachers, educational leaders, policy makers, researchers, and educational technology innovators and providers as they work together on pressing policy issues that arise as Artificial Intelligence (AI) is used in education.