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How AI In Education Is Shaping the Learning Landscape

Education experienced one of the biggest disruptions with the sudden and immediate change in how learning was delivered. Camera has replaced face to face interaction, digital white boards has replaced writing boards and papers and books replaced by e-books, digital worksheets and online files. With most learning institutions, schools, and universities considering continuing hybrid, combined on-site and digital learning methods, there is great potential for effective integration and adoption of AI in Education.
Effective use of AI and ML has been integrated in the administrative side, with the automation in the business functions, processes, and services to cut down process time, remove redundant tasks, and reduce errors.
On the learning front, tertiary education services have been using bots to deliver 24/7 assistance to students from records retrieval, answering FAQs for admin assistance. Institutions have also started developing digital learning communities with ML at its core, QBot. Students tag Qbot in a channel and ask questions. It then prompts the author professor so no question remains unanswered. When the community responds, the professor or tutor can choose the best response and throughout the process the model learns and builds and knowledge database.
AI in education has much more opportunities, applications and use cases in engaging students, empowering staff, optimising operation, and transforming services providing a digital feedback loop that will optimise the whole education system. Providing connected data in all fronts of its operation to better improve the experiences for all stakeholders.
AI in Education | Digital Loop

AI in Education Use Cases

AI has transformed the tools of education. Educational applications include personalised learning platforms to promote students’ learning, automated assessment systems to aid teachers, and facial recognition systems to generate insights about learners’ behaviors. AI use cases in education aim to improve student success and outcomes, boost innovation, and personalise the learning experiences.

ML-based identification of students at risk of failing or late completion

Collect and ingest data about students that have passed, and those that have failed. This includes attendance, hours spent on tasks (self-reported and reported), time left in subject/course, difficulties, LMS interactions and more to ensure the on-time completion of courses.

Use direct feedback from tutor, lecturer, course assessment and other signals such as pass rates and LMS data to determine success.

Maximising completion rates results in greater university revenue.

ML-based personalisation

Create student profiles to provide personalised course materials (personalised learning), identify potential /new students, cross/up-sell to masters, PHD and other degrees based on learning progression and interests.

cs software to monitor PPE compliance and safety monitoring to ensure employee safety.

CE&E, Theme & Comment Analytics

Improve collection and understanding of topics, context, and survey results from social media sites such as Facebook, feedback and survey reviews from Google Reviews.

Facilities management and location awareness

Use IoT and application metadata to understand facilities utilisation and minimise facilities maintenance/cleaning expenditure.

Scheduling and staff optimisation

Automatically schedule students, tutors, lecturers to maximise “X” (where “X” = salary cost, maximise available experience, maximise time/day preferences etc.)

RPA/Task Automation

Automate enrollments, markings, and feedback, and all non-teaching tasks.

Predict and Prevent OH&S Incidents’ in labs

Identify spills, falls, smoke, unauthorised access, unsafe behaviours and other risk factors.

Data Platform Modernisation / Lakehouse in 5 days

Integrated solution for data acquisition, storage, preparation, delivery, and governance to handle end-to-end requirements of learning institution to better manage and use data for analysis and insights.

Chatbots

  • Teaching Assistants – to assist lecturers and tutors
  • Orientation Day – maximise outcomes and minimise time spent
  • Administration and BAU – requesting transcripts, checking accounts, finding directions, resetting passwords and anything else commonly requested by students.

 

The University of Sydney used Azure Cognitive Services for Corona-Bot, an AI-infused chatbot to respond between 200-400 inquiries about covid-19 and provide instant answers.

Inclusion and Universal Access with AI in Education

AI Tools for Children with Special Needs

Better serving “students who require learning at a different level or on a particular subject that isn’t available in their own school.

Helping to “make global classrooms available to all, including those who speak different languages or who might have visual or hearing impairments”.

Creating access for “students who might not be able to attend school due to illness”.

Online and Remote Classroom Collaboration

Remote and hybrid learning tools such as Microsoft teams provide lecturers, learners and support staff simplified and structured digital learning environment. Seamlessly integrating with existing solutions, significantly enriching collaboration. Online classrooms allow easy collaboration, increases engagement and elevate day-to-day work with the tools provided such as whiteboard, break-out rooms, sharing files and lessons and recording lessons.

AI has and will continue to help the education system innovate and improve its services. Educational institutions, schools and universities can speak to AI Consulting Group to identify data and AI opportunities and use cases to help address their immediate needs and provide solution to their most pressing challenges.

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