AI for education: A glossary of AI terms for teachers

Moving forward, AI is set to be even more prevalent in the classroom. So, it’s important to stay up to date and AI-literate to ensure that you, and your students, reap its benefits.

But less than 10% of educational institutions have formal guidance about how to use AI. As an educator, you need to inform yourself about AI for education, and be able to apply it in the most efficient way. Knowing the terminology around AI is the first step – this language can help teachers navigate AI-powered tools, and generate the best outcomes from them. Think of it like reading the instruction manual for a new device. Once you know the different parts and processes, you can tailor the settings to do exactly what you need it to. You can then share these insights with your students.

Below are the most commonly used phrases for AI in education. Keep in mind that as AI evolves, so too will this glossary. You should therefore constantly be updating your AI knowledge and language.

AI for education: a glossary of useful terms

1. Adaptive learning

Adaptive learning harnesses AI and data analytics to adjust and tailor instructions and educational content to users’ preferences. Based on historical interactions with AI-driven tools and platforms, adaptive learning shapes more personalised, effective learning experiences for your students.

In schools, adaptive learning is present in many digital platforms. This technology gives teachers insight into students’ strengths and weaknesses, suggests materials for improvement, and provides real-time feedback. It’s a powerful way to personalise learning, power student engagement and boost performance across the board.

2. Chatbots

Chatbots are AI-driven agents that interact with users through text or speech. And when it comes to AI for education, chatbots can free up a lot of teaching time. Chatbots can carry out various administrative duties like answering students’ frequently asked questions about key information like test dates and homework deadlines.

Chatbots can also facilitate interactive learning by being digital partners for language practice and memorisation tasks, and providing problem-solving exercises to students. They can also help students to access resources 24/7. With all these benefits, educational chatbots are likely to become more and more widespread in the next year or two.

3. Data analytics

Data analytics refers to collecting, reviewing, and drawing conclusions from data sets. Such data could be user behaviours, text inputs, or previous interactions with AI tools.

Data analytics is very valuable for teachers. At one quick glance, it can give you an overview of class performance, individual performance, and any trends or disruptions in learning. Teachers can equally use data analytics to monitor student attendance, engagement with AI-powered resources, homework completion, and highlight students who need extra attention in their work. It’s an easy way of monitoring your students to make sure they’re on track to fulfil their potential in your classroom, and allows for speedy intervention by making even small variations in performance more visible.

Data analytics can be applied to broader educational data too, like national statistics, to empower you to make evidence-based improvements in the classroom.

4. Deep learning

Deep learning is a highly technical subgroup of AI that has artificial neural networks to process and analyse data and determine complex patterns and high-level abstractions.

For teachers, deep learning is the backbone for adaptive textbooks, where content can be adjusted in real time based on students’ progress. Similarly, it can aid visual recognition for accessibility, so if a student has visual impairments, the tech can convert images and diagrams into tactile graphics or provide audio descriptions.

5. Gamification

Gamification is the application of game components and principles in non-game contexts. It can involve point scoring, teams, leadership boards, and friendly or competitive matches with peers. Teachers have been applying gamification principles to aid student engagement for years now – but how does it apply in AI for education?

Gamification is particularly impactful in this context because it positively influences students’ motivation to participate and interact with AI educational tools. For example, a language learning app could offer rewards like badges and titles to students who not only receive top marks but also who use the app most regularly and complete the most activities.

6. Generative AI

Generative AI (sometimes called GenAI) is AI models that have the ability to create new content, like text, images, and music based on input data. Midjourney is an example of generative AI because it creates images from scratch based on text descriptions. For teachers, generative AI can produce custom educational content – for example, a quiz or video on a given topic. The most well known generative AI tool is ChatGPT, which can be used in lots of different ways for teachers and students.

7. Machine learning

Machine learning (ML) is a subset of AI that centres on developing algorithms and models that enable computers to learn without being explicitly programmed. ML systems are designed to detect patterns, make predictions, and solve problems based on the data they are trained on. In big educational institutions like schools, there are years worth of data which can be input to machine learning models.

Once created, these machine learning models can assist personalised and adaptive learning, where systems accommodate students’ individual needs. It can also be used for predictive analysis, where teachers can spot students who are falling behind before their grades are impacted, allowing for targeted early intervention. Likewise, ML can optimise administrative tasks like class scheduling and resource allocation.

8. Natural Language Processing

Natural Language Processing (NLP) is a branch of AI that allows computers to interpret and generate human language.

In the classroom, NLP is most likely to be found in chatbots that can answer students’ queries online. Additionally, NLP can be utilised to develop customised learning experiences for students, for example, to adapt foreign language content according to the student’s proficiency, or create a one-to-one tutoring experience to support student learning.

9. Prompt engineering

Prompt engineering refers to the input you give an AI tool. When it comes to using AI for education, prompts can help you create everything from emails for parents to classroom quizzes to agendas for department meetings. You simply need to engineer the most useful prompt for the best results, and with this information, AI can more accurately carry out the desired function.

AI for education: enhancing your teaching ability

These technologies are not meant to replace teachers but to empower you with insights, resources, and time-saving capabilities that allow for more individualized and effective teaching.

From chatbots that offer instant assistance to adaptive learning platforms that cater to each student’s unique needs, AI enhances teachers’ ability to provide quality education.

That being said, AI in education is still evolving, with endless possibilities waiting to be explored.

These technological advances are paving the way towards a future where every student’s learning experience is elevated by the power of AI, making education not just accessible but truly exceptional.

Further reading

Dive into the big debate on AI and education, learning more about the benefits and risks of integrating AI into the educational system.