Artificial intelligence is beginning to transform many industries, and education stands to benefit tremendously from new AI technologies. As machines become more intelligent using techniques like machine learning, predictive analytics and natural language processing, innovative possibilities are emerging to enhance teaching and learning.
Across classrooms and online learning environments, artificial intelligence shows great potential to revolutionize how we educate students. Some estimates suggest AI may help to personalize learning for over 1.85 billion market valuation by the year 2030. This rapid growth highlights AI’s ability to impact education on a massive scale. This blog post explores 10 specific ways AI is starting to make a difference. Objective of this blog would be to evaluate how AI will change education in future.
1. Personalized Learning
One major use of AI in education is personalized learning. With AI, online courses and educational software can adapt to each student’s strengths, weaknesses, and learning style. This is done through algorithms that analyse a student’s responses, completion time for assignments, test scores, and other data points. The software then determines what topics or concepts the student needs more help with, and tailors the learning experience accordingly.
For example, an AI tutoring system may spend extra time explaining fractions to a student who is struggling with that unit. Or it could present math word problems in a visual format if the student learns better that way.
2. Intelligent Tutoring Systems
Closely related to personalized learning are intelligent tutoring systems powered by AI. These systems act as online tutors that monitor students as they work through lessons, assignments, quizzes or whole courses. But instead of simply providing feedback or the next step, intelligent tutoring systems apply advanced algorithms to determine a student’s thought processes, misconceptions and gaps in understanding.
Based on this analysis, the system knows precisely where a student is struggling and how to intervene. It may provide an alternative explanation, hint or worked example to help unblock the student. Some systems even adapt their teaching strategy in real-time based on the individual’s responses. For example, switching between visual and verbal instruction.
3. Automated Essay Scoring
Grading student writing is a tedious and time-consuming manual process for teachers. AI can help with essay scoring by automating at least the initial assessment. Advanced natural language processing techniques allow machines to understand key qualities of writing like thesis development, organization, word choice and more. Some systems are even trained to detect writing style, voice or other nuances.
Automated essay scoring engines analyse submitted essays and provide feedback on areas of strength as well as opportunities for improvement. For lower stakes assignments, the AI score may be considered final to save teacher time. For higher stakes tests, the AI acts as a first pass to flag essays that may need a human instructor’s full review. Either way, students benefit from faster feedback so they can apply lessons to future assignments. And teachers’ workloads are lightened, freeing up valuable time for other tasks.
4. Adaptive Learning Platforms
Many of today’s online and hybrid learning systems are powered by artificial intelligence (AI). These platforms, which go by the name of adaptive learning platforms, integrate self-paced classes, assignments, and evaluations with multimedia content such as books and videos. AI algorithms are continuously analysing performance data as students interact with the course. A person’s study habits, time invested, patterns of correct and incorrect responses, and other indicators are included in this.
The adaptive platform then suggests a customized course of action based on the gathered insights. It could recommend going over a lesson several times until mastery is attained or skimming over previously comprehended material to concentrate on weaker points. Several instances showcase similar issues with escalating complexity levels as abilities advance.
5. Virtual Teachers and Tutors
As AI and machine learning progress, virtual teachers and tutoring bots are becoming more common. Powered by natural language processing, these AI for student helpers can converse with students through chat, video or text. They answer questions about course content, provide lessons on demand, and offer feedback through realistic dialogue. Some systems even mimic human expressions, vocal tones and teaching styles.
While not intended to replace flesh-and-blood educators, virtual tutors extend learning opportunities beyond the classroom. Students gain another accessible resource for practice, review and remediation anytime, anywhere through their smartphone or laptop.
6. Predictive Analytics for Student Success
Student information systems collect a wealth of meaningful data over time, from transcripts to attendance records, extracurricular involvement and more. AI can uncover patterns within these sprawling datasets to predict things like course performance, risk of dropping out or likelihood of on-time graduation. Known as predictive analytics, the techniques involve training machine learning models on past student outcomes and demographic factors.
Armed with these insights, administrators, counsellors and advisors can proactively intervene when early warning signs emerge. At-risk students may receive tutoring, mentoring or other targeted support services. Strategies that historically work well could be more systematically applied school-wide or district-wide based on predictive modelling.
7. Virtual & Augmented Reality
Immersive technologies like virtual reality (VR) and augmented reality (AR) open new realms for experiential learning. Subjects like human anatomy that were previously taught through textbooks or diagrams come alive through VR simulations. Students can explore a virtual body and zoom in to examine organs, bone structures or micro-level biological processes.
Similarly, AR mobile apps overlay interactive layers onto real-world views through smartphone or smart glasses. This bridges abstract concepts to concrete applications. Field trips for history, science or art become more vivid encounters when overlaid with context-sensitive information. Future AR workbooks may embed virtual experiments and diagrams within physical pages.
8. Adaptive Practice Exams
AI can elevate standardized testing preparation by crafting adaptive simulated exams that mimic real conditions. As a student answers multiple-choice, fill-in-the-blank or free response items, the computer interface analyses mistakes, timing and other data points. It then selects subsequent questions tailored to each individual – focusing more on known knowledge gaps while still sampling from across relevant domains. Optional feedback instantly pinpoints which concepts or skills require remediation. Students gain valuable exposure to rigorous, exam-like environments without waiting for the official test date.
9. Automatic Speech Recognition
Speech recognition software powered by deep learning now makes it possible for machines to understand human speech with high accuracy. In classrooms, AI for educators could assist learning through speech-to-text dictation and voice interaction. For example, an AI assistant converts a student’s read aloud into text to analyse fluency while simultaneously displaying highlighted text. This enables running records for formative assessment without manual transcription.
Speech recognition also empowers students with disabilities through user interfaces not requiring visuals, typing or fine motor skills. Similarly, AI enhances the learning experience for English language learners by transcribing lectures, discussions or readings in real-time. Pronunciation practice becomes more engaging through voice response systems. The same speech technologies facilitate virtual seminars, flipped classrooms and team-based assignments that broaden participation. More natural spoken interactions are revolutionizing education through AI.
10. Intelligent Classroom Assistants
Intelligent Classroom Assistants is one of the best applications of AI in education. AI in the form of digital teaching assistants may soon materialize in brick-and-mortar classrooms through advanced robots, holograms and augmented reality. Powered by computer vision, natural language processing and context awareness, these AI helpers automatically capture whiteboard notes, stream audio from classroom discussions, answer factual questions, look up information on demand and more.
Students gain another educational support beyond the instructor alone – an AI assistant that operates invisibly yet enhances learning productivity. For example, students could privately chat with an AI tutor for clarification on homework exercises while the teacher leads whole class instruction. Similarly, AI may engage struggling learners during lab experiments or quietly pull aside individuals needing re-teaching unobtrusively while class activity continues. The end result boosts student engagement, participation and outcomes through added intelligence in the learning environment.
Conclusion
Artificial intelligence has tremendous potential to positively reshape education in transformative ways over the coming years. As the10 applications discussed have shown, AI opens up new possibilities to personalize learning for all students and take the quality of instruction to greater heights. While the full realization of this future may still be years away, the groundwork is clearly being laid. These 10 applications help to understand how will AI affect education in future.
As AI technology continues to evolve rapidly, educators must keep abreast of evolving trends. A community like MyW3Magic can help in this regard by keeping members informed of the latest developments at the intersection of AI, blockchain, and other emerging technologies. Through curated news, discussions, and educational resources, MyW3Magic provides a valuable space for professionals to explore both opportunities and challenges associated with integrating innovative tools into their respective fields.