Artificial intelligence (AI) is transforming how we live and work, and the education sector is no exception. From intelligent tutoring systems to AI-powered admin tools, institutions are now exploring how this technology can make learning more personal and management more efficient.
But while the potential is exciting, applying AI meaningfully in an educational context is the real challenge — and opportunity. In the blog below, we dive into how AI is changing both the learning experience and institutions’ operations, with real-world examples, benefits, and things to consider.
Let’s look at what smarter learning and management really look like in today’s AI-enhanced education landscape.
A New Chapter in Education
AI in education is no longer just a futuristic concept — it’s already reshaping how higher education institutions deliver learning and run operations.
Artificial intelligence is especially powerful when it comes to automating repetitive tasks, identifying patterns in student behaviour, and adapting content in real time.
That said, we’re still in the early stages. While the tools are impressive, the real conversation is about how to apply them in meaningful, ethical, and sustainable ways that support human connection, not replace it.
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As we consider how AI is evolving, one of its most impactful applications is in the learning experience itself, particularly when it comes to personalising education. Let’s look at this in more detail.
Personalised Learning with AI
One of the most promising areas of AI in education is personalised learning.
Instead of a one-size-fits-all approach, AI enables learning experiences tailored to each student’s individual needs, pace, and progress.
Using AI algorithms that analyse student performance, learning patterns, and even behaviour, platforms can adjust content delivery, recommend extra resources, or flag areas where a student might need help.
Take Squirrel AI in China, which uses adaptive tutoring to personalise every lesson based on prior knowledge and engagement. Or MATHia, which Carnegie Learning created, supports math education with AI that adjusts in real time. Then there’s Knewton Alta, a college-level platform that personalises learning paths based on ongoing student performance.
These intelligent tutoring systems are changing how students experience the learning process, making it more responsive, intuitive, and ultimately, more effective.
Now that we’ve looked at how AI supports personalised learning, let’s explore another exciting development — intelligent tutoring systems that combine machine learning with human instruction.
Intelligent Tutoring Systems: Human + Machine Learning
Speaking of tutoring, let’s look deeper at Intelligent Tutoring Systems (ITS) — digital platforms that offer students personalised instruction, feedback, and support on demand.
These systems are especially useful in high-enrolment environments, where it’s challenging for lecturers to give every student the attention they need. Rather than replacing educators, ITS tools are designed to complement human teaching, offering scalable support that reinforces learning and boosts engagement.
Here’s a closer look at some of the key features that make ITS so effective:
- Natural Language Processing (NLP) for Conversational Interaction
NLP allows students to interact with the system using everyday language, just as they would with a tutor. Whether asking a question, requesting clarification, or working through a problem, students can engage in a more natural and intuitive way. This conversational layer not only improves user experience but also deepens understanding by encouraging students to articulate their thinking. [source]
- Real-Time Performance Tracking that Adapts Instruction Dynamically
Intelligent tutoring systems constantly monitor student inputs — like answers, response times, and interaction patterns — to assess understanding in real time. Based on this data, the system can adjust the difficulty of the material, offer hints, or redirect the student to foundational concepts. This makes the learning process far more responsive and personalised than traditional digital platforms. [source]
- Built-In Scaffolding to Support Learning Progression
Scaffolding is a teaching method where learners are supported as they build knowledge from basic to more advanced concepts. ITS tools do this by breaking down complex topics into manageable steps, offering prompts and guidance at the right moments, and gradually reducing support as the learner becomes more confident. This approach mirrors how a good teacher would coach a student, helping them achieve mastery over time. [source]
Together, these features enable ITS platforms to deliver a rich, adaptive, and student-centred learning experience, especially valuable in large classes or online environments where human resources are stretched.
While AI is clearly enhancing the learning process, its power also extends into back-office operations — a less visible but equally important part of education management.
Automating the Admin: AI in Back-Office Operations
AI doesn’t just support the classroom — it’s also quietly revolutionising the back office. From speeding up tedious tasks to improving accuracy, AI tools are increasingly being used to streamline the behind-the-scenes operations of higher education institutions.
Some examples include:
- Automated grading of multiple-choice and short-answer assessments
- AI-powered admissions screening that evaluates applications against set criteria
- Chatbots that handle frequently asked questions 24/7
- Timetabling algorithms that optimise class schedules
These innovations help reduce the burden of repetitive, time-consuming administrative tasks, freeing staff up to focus on higher-value work and student support.
Beyond saving time, AI is also proving to be a game-changer in promoting greater accessibility and inclusion across education systems.
Improving Access and Inclusivity with AI
AI in education isn’t just about speed and efficiency. It’s also opening up opportunities for more inclusive learning.
Accessibility tools powered by AI are helping students with disabilities learn on their own terms. Examples of these include:
- Speech-to-text and text-to-speech technologies
- Real-time translation tools that support multilingual learning environments
- Predictive learning support, flagging when students might fall behind
These AI tools help bridge gaps that might otherwise require human intervention, creating more flexible, responsive support services that promote student success across a wide range of needs.
With so many promising use cases, let’s take a look at where AI is already being applied in real-world institutional settings — and what we can learn from them.
Real-World Institutional Use of AI
So what does all of this look like in action?
Here are a few institutions already leading the way:
- Arizona State University uses AI to reshape student advising and support services, helping students stay on track with proactive alerts and virtual assistants. [source]
- OpenAI’s Whisper is used for audio transcription in lectures, improving accessibility and note-taking for students. [source]
- China’s national education strategy is integrating AI into classrooms to improve skills like problem-solving and communication. [source]
While results are promising, these examples also underscore the importance of human interaction. AI tools are most effective when they work alongside educators, not in place of them.
Of course, no discussion about AI would be complete without acknowledging the challenges. Let’s unpack some of those next.
The Challenges of Implementing AI in Education
With all this potential, keeping our feet on the ground is important. There are real challenges to implementing AI in education, which can’t be ignored.
Key issues include:
- Data privacy – Who owns student data, and how is it protected?
- Equity – Not all institutions (or students) have access to AI-capable devices or infrastructure.
- Digital readiness – Some institutions may not have the IT skills or frameworks needed to adopt AI tools effectively.
- Over-reliance – Using AI without oversight could lead to poor decision-making or unbalanced learning experiences.
The path to AI in education must be thoughtful, transparent, and human-centred, not just tech-driven.
So, where does this leave us? Let’s close with a few final thoughts on what it all means for the future of education.
Conclusion
AI in education opens doors to more innovative learning, better support, and more agile management. But it’s not a magic fix. Success lies in thoughtfully designing, implementing, and integrating these AI technologies into higher education environments.
When used well, AI can support human effort, not replace it, and make education more responsive, inclusive, and effective.
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