Artificial intelligence (AI) is revolutionising numerous sectors and industries, including the realm of education. AI can potentially address significant global challenges and opportunities in education, encompassing aspects like accessibility, quality, equity, personalisation, and lifelong learning. This article explores how AI can lend support to educators, learners, and policymakers in creating more efficacious and all-encompassing learning environments. AI is causing a revolution in various industries, like medicine and finance. However, its impact on education is equally remarkable. Let's delve into some instances where AI is enhancing the educational landscape.
10 Ways AI Can Help Students Learn
One theory in pedagogy suggests that individuals have diverse learning styles, such as visual, aural, or kinesthetic learners. Although this theory has been debated, it is generally acknowledged that people learn differently, varying in work and study styles, learning pace, and ease with specific subjects or concepts. Given this understanding, personalising the learning experience becomes essential. However, manually tailoring lesson plans for every student is impractical due to time constraints. This is where AI-powered personalised learning comes into play.
AI's strength lies in its ability to quickly analyse vast amounts of data and identify patterns, making it the perfect tool for developing personalised learning plans. AI-based learning systems can provide teachers with detailed insights into students' learning styles, abilities, and progress, enabling them to customise their teaching methods accordingly. For instance, AI can suggest more advanced work for some students and offer extra attention to others.
Read more: Top 10 Humanoid AI Robots in 2023 So Far
Furthermore, AI can enhance accuracy in predicting results, assisting teachers in ensuring their lesson planning meets learning targets.
AI's role in planning, scheduling, and creating unique learning experiences for students can free up teachers' time, allowing them to focus on high-value tasks such as individual student support. Several universities have tested the use of chatbots for repetitive tasks typically handled by professors, providing 24/7 assistance to students.
Adaptive learning employs AI to tailor resources and learning activities to suit each learner's unique needs, especially in online learning.
This method involves rigorous analysis of a student's performance data, enabling the AI algorithm to adjust the course material's pace and difficulty, optimising the learning process.
Adaptive learning not only improves the learning process but also saves time and resources by eliminating unnecessary repetition and focusing on areas where a student may struggle. The teacher can offer support where needed, while the student can learn at a comfortable pace.
Many companies are embracing adaptive learning to improve content delivery. A notable example is Duolingo, a language-learning app that provides personalised exercises for learning various languages. The app uses AI to ensure lessons are paced and levelled according to each student's performance.
Grading assignments and exams is among the most time-consuming tasks in education. AI-powered tools, utilising machine learning algorithms, can evaluate essays, multiple-choice tests, and programming assignments with remarkable accuracy and efficiency, thus saving teachers considerable time.
Using computers for grading ensures consistency and reduces bias, including unconscious bias that teachers may have, while also providing personalised feedback to students. This encourages students to take ownership of their learning and improve in problem areas.
Although automated grading has significant advantages, it is crucial to address biases that may persist in AI due to the underlying data used to train the algorithms. China has been experimenting with AI auto-grading platforms, with around 1 in 4 schools using machine-learning auto-grading platforms.
Intelligent Tutoring Systems
Intelligent Tutoring Systems (ITS) are computer systems powered by machine learning algorithms that offer personalised and adaptive lesson plans based on individual students' learning needs and pace. Similar to other AI tools, ITSs analyse student data to understand learning patterns, enabling them to provide customised suggestions, feedback, and exercises tailored to each student's requirements.
ITS benefits both students and teachers, allowing educators to monitor students' progress and modify their teaching approach for effective lesson delivery. Students can learn at their own pace while receiving support when needed and engaging in more advanced concepts when ready.
A study by the U.S. Department of Education revealed that existing ITS can enhance student literacy by improving their reading comprehension and writing skills. Implementing these systems in classrooms poses challenges, and natural language processing techniques have been suggested for scoring student responses.
Smart Content Creation
Creating lesson plans presents a significant challenge for teachers, as each student has unique learning requirements. "Smart content creation" refers to the use of AI to automate and enhance the generation of educational content. AI platforms analyse student data to create personalised and engaging educational material.
This customised content can then be used to create tailored environments catering to different learning outcomes. Students can select lesson plans that align with their needs. AI can generate interactive quizzes, simulations, and experiments using chatbots, augmented reality, or virtual reality, enriching the learning experience.
Coursera is a prime example of successful smart content creation, using AI to curate multiple educational and professional courses and suggest appropriate courses based on students' learning performance, pace, and individual needs.
Analysing large amounts of student data can be a tedious task, but AI-powered automated analytics make the process easier. Teachers can use the data to track student performance and engagement, enabling timely interventions and additional support as required. Students can also leverage analytics to monitor their own progress and request help when needed.
For instance, the University of Michigan offers a dashboard called "My Learning Analytics," allowing students to visualise and track their grade distribution, assignment planning, and resources.
Implementing learning analytics in education may face ethical and privacy issues, as well as challenges related to data collection and analysis. Addressing these concerns with the help of AI is essential to ensuring responsible usage.
Virtual assistants powered by AI can automate administrative tasks such as lesson planning and scheduling, freeing up teachers' valuable time to focus on essential duties like lecturing and engaging with students.
Virtual assistants also provide customised feedback to students, monitor their progress, and offer additional resources based on individual needs. Interacting with AI-powered virtual assistants has even shown positive correlations with students' academic performance.
Natural Language Processing
Natural Language Processing (NLP) is an AI field focused on making computer systems capable of understanding and interpreting human languages. NLP has various applications, such as text generation, chatbots, and information extraction. For instance, ChatGPT, developed by OpenAI, serves as a language model used by students for homework help, exam preparation, and learning in general. Teachers can also use ChatGPT to prepare lesson plans and check assignments for grammar and information.
It is important to view NLP and AI as assistive technologies, not absolute sources of knowledge, as they may have limitations and potential biases. Careful consideration of the technology's impact on the education sector is essential.
AI-powered predictive modelling analyses large amounts of data to predict various outcomes, such as student performance. This information is valuable to teachers, parents, institutions, governments, and students, as it can greatly enhance the learning experience and set benchmarks. Predictive modelling allows teachers to offer timely guidance based on students' predicted performance and previous test or exam results.
Data-driven analysis helps improve individual student performance and provides additional support when needed, enriching the learning experience. Governments can also utilise predictive modelling for planning educational goals.
Augmented and Virtual Reality
Augmented Reality (AR) and Virtual Reality (VR) technologies have gained popularity in recent years. AR overlays computer-generated content onto real-world objects, while VR creates a simulated virtual environment. These immersive technologies hold immense potential for education.
Students can interact with AR and VR learning materials to enhance their understanding of complex concepts and their overall learning experience. VR can simulate labs for conducting chemistry experiments or virtual dissections, while AR enables studying stars and galaxies up close.
AR and VR technologies have promising applications in enhancing STEM courses, medical simulations, arts and humanities materials, and technical education. They are already being utilized by various institutions, like Arizona State University (ASU), to offer unique learning experiences.
Artificial Intelligence presents promising advancements for the education sector, benefiting both teachers and students. However, it cannot replace human interaction and empathy, and addressing biases and data privacy concerns remains crucial. Integrating AI responsibly is essential for leveraging its potential while understanding its limitations. The future of education could be revolutionised with AI's continued implementation, shaping the learning experiences of future generations.
Read more: ChatGPT-4: All you need to know