In his book “The Innovative Mindset,” George Couras made the amusing remark that “great instructors will never be replaced by technology, but technology in the hands of excellent teachers is transformational.”
Every element of human life is being swiftly transformed by technologies like AI and ML; nevertheless, education is one sector that has used this new technology sparingly while offering tremendous potential and opportunity.
Many times, the wonder of artificial intelligence and machine learning, which uses data to produce rules, is mistaken with the magic of software applications, where data and rules provide the answers.
We would want to argue that using artificial intelligence and machine learning could work its magic and change how effective and pertinent Indian higher education is.
Because knowledge was overemphasised at the expense of skill development, education suffered. In response, a system of education that emphasises both knowledge and skills was developed. Knowledge without skills leads to unemployment, but skills without education results in decreased productivity.
Google knows everything, so we need to move away from knowing and toward learning. Performance measurements also need to change from inputs to outputs. Personalization focuses on the learner’s motivation and piques their curiosity rather than making things simpler for them.
Annual exams must be replaced with ongoing feedback because they haven’t shown to be effective. A continuum between preparation, repair, and upgrade is necessary for lifelong learning. Employability is a crucial and measurable result!
Today’s AI and data-driven systems can monitor a student’s understanding of a subject and build individualised adaptive pathways through intelligent recommendation engines. Institutions are recognising the advantages of data-backed solutions, whether the objective is to pinpoint and better address pain points in the student journey, more effectively allocate resources, or raise student and faculty engagement.
Many institutions have tried using chatbots to respond to students’ questions, gathering a tonne of information about their interests and problems in the process. This data can be analysed by ML-enabled intelligent systems that can offer feedback, justifications, and prompt assistance. These systems can also help institutions develop cutting-edge learning programmes and services to enhance the educational experiences of students.
Due to financial constraints and physical constraints, experiential immersive education is difficult to implement with a large cohort. The goal is to supplement teachers, not replace them, and reduce their administrative workload so they can concentrate on more creative and humane aspects of learning. AI enabled digital assistants can provide a more personalised learning experience by reminding students to study, keeping track of their study times, and even analysing their grades.
Being able to view and interact with the human body or a microscopic cell instead of merely reading about it in a book would change how students engage with the topic. Immersive learning options are made possible by AI-powered technology.
Teachers would have more time to advise, inspire, and coach pupils if they were relieved of time-consuming administrative chores including imparting knowledge, overseeing, and responding to common questions. The effectiveness of flip classrooms, in which classes are used for discussions and students complete the lecture and learning in advance, would be significantly increased by a recursive study of learning outcomes across students, cohorts, and schools.
In contrast to machine learning, which can find complimentary abilities that will maximise critical thinking and test students’ aptitude for adaptation and collaboration, natural language, computer vision, and deep learning could provide answers to inquiries from students.
Predictive and prescriptive analytics are now combined in AI-powered learning analytics to personalise communications with students, boost retention rates, and enhance student experience and engagement.
ML tools collect in-the-moment, minutely detailed behavioural data and offer perceptive visual analytics, resulting in a seamless learning environment for the learner. This may give students more control over their learning pace, awareness of their preferred learning styles, and lifelong feedback on their own cognitive and behavioural preferences.
As some of them can read students’ handwriting, analyse teachers’ grading patterns, and grade assignments faster than a teacher, data driven AI and ML engines provide the chance to do away with traditional testing methods and quantify academic abilities and accomplishment in a more sophisticated way.
We are still in the early stages of capability creation, despite the fact that AI and ML technologies present a wide range of intriguing possibilities. Physical classroom setups frequently prove to be constrained due to time and space constraints. The challenging trinity between cost, quality, and scale can be overcome in education with the broad and thorough application of AI and ML technologies.
Expanding the use of artificial intelligence and machine learning may be advantageous for universities, which face numerous financial and demographic constraints as well as a wealth of opportunities, including reaching employed learners and online learning. We must not squander this chance to create a good, equitable educational system.
