The advent of technology and its subsequent integration into traditional education systems have catalyzed a renewed approach to learning. With priority being given to personalization in just about every aspect of modern life, the same also extends into the field of education and teaching. Personalized learning has caught the attention of educationists, developers, and policymakers at large, bringing about the possibility of innovation and intuitive approaches to educating the upcoming generations of students. With adaptive learning, pupils can gain access to course material tailored to fit their unique capabilities. Aligned to each student’s cognitive potential, adaptive learning technologies can bring about better learning in shorter durations, while also making the experience of education more interactive.
AI’s impact on education has also allowed for better prospects when it comes to adaptive learning and assistive technologies. Adaptive learning AI can automate the process of tailoring course curriculums to individual students, allowing them to learn better and at a pace that suits their capacities. Technologies powered by AI can provide students with a large assortment of iterations from a single concept or topic, covering both the understanding and applicative aspects of learning. These techniques also make learning more immersive and help students remain focused on their studies, helping them improve their learning outcomes and performance on tests. In an era where AI’s prominence is consistently growing in education, it is only fitting that adaptive learning—one of its many applications—is understood comprehensively.
What is Adaptive Learning?
Adaptive learning allows students to learn at their own pace, with a focus on what they find difficult to learn.
Adaptive learning involves customizing course material based on students’ individual learning styles and capabilities. Rudimentary forms of adaptive learning have been in use even in traditional classrooms. A simple instance of this would entail a teacher providing a variety of examples to a student unable to grasp a concept being taught in class. In the case of AI-supported adaptive learning, the structuring of personalized learning materials is taken care of by the algorithm. AI learning assesses the student’s strengths, weaknesses, and overall performance while structuring their curriculum and learning goals. Students’ proficiency and learning styles are also evaluated by the AI, making way for better-structured programs suited to a student’s unique needs in the classroom. This model can be deployed in both schools and in universities, where language models efficiently decipher student interactions and performance on the interface. The algorithm looks at the time taken to solve quizzes, groups the student’s skill sets, and is also capable of providing numerous varieties of examples to bolster their understanding of a topic. Students might also be repeatedly tasked with tests and quizzes based on topics that are categorized as weak areas to help them perform better and sharpen their knowledge in these domains.
Teaching in the age of AI is bound to become more tech-savvy in case these solutions find widespread adoption. Idiosyncratic traits and necessities, while forming the basis for adaptive learning AIs, can be scaled to provide a pointed analysis of a vast number of students. This allows teachers to assess and analyze the overall performance of their pupils. These technologies can be used as platforms for testing students and maintaining a record of their scores. Relevant data can then be utilized to create metrics and performance indicators, which can then allow teachers to focus on areas that require improvement. This approach can be time and cost-effective while providing an overarching structure to base important academic decisions that affect a large number of students.
The Limitations of Adaptive Learning Technology
Unless the concerns surrounding AI are addressed, adaptive learning might not be able to find acceptance in traditional learning settings.
As with any up-and-coming technology, adaptive learning, too, has several limitations that raise a considerable degree of uncertainty and skepticism. With AI and language models being prone to biases and a tendency to misconstrue information, the utilization of artificial intelligence in education becomes dicey. From hallucination problems to misinterpretation of information, the possibility of AI making mistakes when assessing student performance poses a great risk for its widespread deployment in the academic niche. Integrating adaptive learning with existing education policies can also be a challenge, considering traditional education systems rely heavily on interpersonal skills and interaction between both students and teachers to ensure learning continues holistically. With the introduction of screens, the cutback on human interaction might have long-term implications for students.
Another important drawback of deploying adaptive learning AIs in education might be the possibility of reducing healthy competition among students. Personalized education and learning might make it difficult to assess a student’s skills against those of their peers. This has the potential to bring down spirited and positive competitiveness in a classroom. The advent of chatbots and other AIs has made it evident that these technologies can also be misused. This has especially been a point of concern with AI writing and language-related assignments. Addressing these challenges and concerns is quintessential if educators and developers intend on mainstreaming adaptive learning. Moreover, ensuring technologies are made accessible to all students alike is equally important. This step will effectively address concerns surrounding the exacerbation of existing inequities in education by technological aids.
The Takeaway
The future of adaptive learning AIs relies on the overall optimization of deep learning models to suit the nuanced requirements of education.
With the digital learning market projected to grow at over 20% year-on-year, adaptive learning and AI-supported education technologies are bound to make their way into the classroom of the future. This is also fueled directly by the drastic advances and development of advanced artificial intelligence models that are more capable of handling complex tasks unsupervised. Thanks to durable deep learning networks and the ever-growing capacity for efficient computing, adaptive and personalized learning are constantly seeing improvements. However, despite the technical prowess and effectiveness of these strategies, their widespread approval and deployment in schools and universities will remain subject to the various concerns and limitations surrounding these tools being addressed effectively.