Beyond Personalisation: Why AI in Education Should Empower Student Customisation
Think about the things you love to tailor to your own taste. Maybe it was tricking out your virtual ride in Gran Turismo or Mario Kart, choosing the perfect paint job and spoiler to leave your opponents in the dust. Perhaps it's the countless hours spent designing your avatar in a role-playing game, making sure every detail, from the haircut to the armour, is uniquely you. Or even as simple as setting the difficulty level on a new game to match your skill and desired challenge.
These experiences, deeply ingrained in popular culture from shows like Pimp My Ride to the modern gaming landscape, are all about customisation. They are about putting the user in the driver's seat, providing tools and options to shape an experience to their individual preferences and needs.
This got me thinking about the conversation surrounding Artificial Intelligence in education. The buzzword we hear constantly is personalisation. The promise is alluring: AI systems that adapt seamlessly to each student, delivering content at their pace, identifying knowledge gaps, and recommending the perfect next step. It's a vision of a truly tailored educational journey, orchestrated by intelligent algorithms.
But is personalisation the right lens? While powerful, AI-driven personalisation is often a passive experience for the learner. The AI observes, analyses, and adjusts for the student. It's like a smart streaming service recommending your next binge-watch based on your history – convenient, but you're not actively telling it why you want to watch something or how you want the story to unfold.
Personalisation vs. Customisation: Understanding the Difference
While often used interchangeably, personalisation and customisation in education offer fundamentally different approaches to tailoring learning.
Personalisation, particularly in the context of AI-driven platforms, typically involves the system using data about a student's performance, interactions, and preferences to automatically adjust content, pacing, and recommendations. The intelligence lies with the AI, which curates the learning experience based on its analysis. Think of Netflix recommending shows based on your viewing history – the platform is personalising your experience without direct input from you on why you want to watch something.
Customisation, on the other hand, places the agency firmly in the hands of the learner. It provides students with tools and options to modify their learning environment, choose their learning paths, select resources, and adapt the presentation of information to suit their individual needs and preferences. This is akin to configuring the dashboard on your smartphone – you decide which apps and widgets are most useful to you and arrange them accordingly.
The Limitations of AI-Driven Personalisation
The current discourse heavily favours AI-driven personalisation, promising efficiency and targeted instruction. Research in AI in education often focuses on the effectiveness of adaptive learning systems that adjust difficulty levels, provide automated feedback, or recommend the next learning activity based on algorithmic analysis. These systems can be highly effective in ensuring students are presented with material at an appropriate level and receive timely support.
However, an overemphasis on this form of personalisation risks creating passive learners. If the AI is constantly making decisions for the student, are we inadvertently limiting opportunities for them to develop crucial metacognitive skills – the ability to understand and manage their own learning?
This is where the argument for customisation gains traction. Shifting the focus to providing students with meaningful choices and tools for customisation can foster greater student agency and ownership over their learning.
Research on student choice in online learning environments highlights numerous benefits, including increased motivation, improved engagement, and the development of self-regulation skills. When students have the ability to choose how they learn, what resources they use (from a curated selection), or in what order they tackle topics, they become active participants rather than passive recipients.
Customisation: Putting Students in the Driver's Seat
Customisation is about agency. It's about the student actively making choices and using AI as a powerful set of tools to build their ideal learning environment. It's the difference between the AI selecting the perfect car for you based on your driving data (personalisation) and the AI providing you with an incredible garage of options and tools to build and modify your own dream machine (customisation).
Consider an AI-powered learning platform. A personalised approach might automatically hide content deemed too challenging or irrelevant based on the student's past performance. A customisable approach, however, might allow the student to flag content they find difficult and request alternative explanations or supplementary materials, or even choose to attempt more challenging content to push themselves. The AI becomes a powerful tool at the student's disposal for tailoring their experience, rather than a benevolent dictator of their learning path.
Pimp My Learning: Customisation in Action
Imagine an AI-powered learning platform built with customisation at its core, like a high-tech "pimp my learning" shop:
Instead of the AI simply serving up the next topic, it could present a skill tree or learning pathway interface where students choose which areas they want to focus on next, with the AI providing insights into prerequisites or connections between topics.
For understanding a complex concept, the AI could offer a menu of explanation styles – a detailed text explanation, an interactive simulation, a short video, or even a collaborative exercise with peers, allowing the student to select the approach that best suits their learning preference.
Students could use AI-powered tools to generate different types of practice problems or quizzes based on the concepts they feel they need to reinforce, taking control of their review process.
The interface itself could be customisable, allowing students to adjust the layout, notification settings, and even the type of feedback they receive from the AI.
This approach doesn't diminish the role of AI; it elevates it. AI becomes the intelligent engine that powers a highly flexible and responsive learning environment, providing students with the tools and information they need to make informed decisions about their learning journey.
Addressing Concerns with AI Personalisation
Critiques of purely AI-driven personalisation also raise important concerns. Issues of data privacy, algorithmic bias, and the potential for a black box where the system's decision-making is opaque are valid. Furthermore, there's a risk that if not carefully designed, personalised systems could inadvertently trap students in filter bubbles, limiting their exposure to diverse perspectives or more challenging material that could foster significant growth.
Focusing on customisation, while still leveraging the power of AI, can mitigate some of these risks. By giving students control, we empower them to navigate the learning landscape actively, make conscious choices about their learning, and develop critical evaluation skills regarding the resources and paths they select.
The Educational Philosophy Behind Customisation
Prominent voices in education have long championed student-centered approaches and the importance of learner autonomy. While not always specifically about AI, their insights resonate strongly with the argument for customisation:
John Dewey, a key figure in progressive education, emphasised the importance of active learning and the student's role in constructing their own knowledge.
The concept of student agency – the capacity to act independently and make free choices – is widely recognised as crucial for developing motivated and successful learners.
Conclusion: From Passive Recipients to Active Architects
As educators and developers, we need to be mindful of the distinction between AI personalising for the student and AI enabling the student to customise their learning. While personalisation offers efficiency, it's customisation that has the potential to cultivate the kind of active, self-directed, and motivated learners who can thrive in an ever-changing world.
This shift from AI personalising for the student to AI empowering student customisation is not just a semantic difference; it's a pedagogical one. It moves us towards a model where AI serves as a powerful co-pilot for the learner, providing intelligent support and options, rather than acting as an automated instructor dictating the path.
It's time to start building the educational equivalent of the custom car shop, empowering students to take the wheel and design their own path to success. Instead of solely asking "How can AI personalise learning for students?", perhaps the more impactful question is: "How can AI empower students to customise their own learning?" This shift in perspective could unlock the true potential of AI to create a more engaging, effective, and empowering future for education.
