AI first entered the world of education back in the 1960s when computer-assisted instruction (CAI) was developed. This teaching method utilised technology to provide users with learning materials and feedback, offering a personalised learning experience. Sound familiar?
PLATO (Programmed Logic for Automatic Teaching Operations) was the first ever CAI system. Invented in 1960, it was used by a wide range of learners, from school pupils and university students to prison inmates. You could choose from a broad range of courses to take, including maths, Latin, and the sciences. Over time these large, bulky intelligence tutoring systems (ITS) became smaller and more affordable, making them viable for mainstream schools.
PLATO in action in the late 1970s and 80s.
Another early example of ITS was TICCIT (Time-shared, Interactive Computer-Controlled Instructional Television). Developed in 1968, it delivered individualised, multimedia-based content to users in schools and at home, allowing users to do lessons at their own pace. This new system matched the latest teaching theories at the time, namely BF Skinner’s and Benjamin Bloom’s work, which supported the individualised tutoring of students in the classroom. These theories were critical to inspiring AI-based personalisation which is still one of the main benefits of AI teaching tools today.
AI was transformed, like much of the world, by the invention of the World Wide Web in 1989. The web could collect data based on user interactions with its services which software agents would be trained on. This enhanced the ability of learning analytics to adapt to learners and create even more personalised learning experiences.
The computer used by Tim Berners-Lee, the inventor of the World Wide Web, which became the world’s first web server.
By the 1990s, another familiar piece of technology was invented: Learning Management Systems (LMS) which organised content, tracked student progress, and managed online education. Many used basic automation rules, such as grading quizzes automatically or alerting students to their poor attendance, but today they can also use predictive analytics to do things like forecasting the chances of a user dropping out of school or university based on factors such as how often they login to the system.
A screenshot of the FirstClass desktop (an LMS) from 1993.
By the turn of the century, there continued to be developments in hardware, big data mining, AI models and architectures which spurred on AI’s rapid development. One such development was the adoption of Natural Language Processing in education, utilised in automatic essay grading and eventually AI chatbots. Automated grading systems were especially important when Massive Open Online Courses (MOOCs) were first released; they leveraged AI algorithms to analyse learner behaviour and personalise their learning experience.
But by far the biggest recent breakthrough for AI in education was the release of ChatGPT by OpenAI in 2022. For the past three years, generative AI, most significantly chatbots, had transformed the school experience for teachers, non-teaching staff, and students whether in terms of providing learning support or streamlining admin processes.
Of course, there are lots of other AI tools that have rapidly advanced over the past couple years and have changed the learning and teaching experience, from AI-powered assistive technology and task automation to data and learning analytics. You can learn more about AI in education and the most popular use cases in this blog.
Learn more about AI in schools in our upcoming webinar
Following our hugely popular AI webinars during the last academic year, on November 7th we’ll be hosting the fifth instalment of the series – AI Beyond the Classroom: From Prompt to Practice. To register for the webinar click here, and to watch recordings of our previous webinars click here.
About Bromcom AI
A vast number of AI tools and technologies that leverage AI in some capacity for education are now available, like Bromcom MIS. With our built-in AI tool, Bromcom AI, you can ask general queries, generate content including letters to parents, create lesson plans and homework, and run queries on your school data.
