AI is often described as groundbreaking, poised to transform education by making learning more efficient, engaging, and effective.
AI-first startups are making strides in reshaping education, addressing key issues in workload and student motivation, among others, and unlocking new opportunities for teaching and learning. Intriguingly, so are ‘AI-second’ startups, appending AI elements to existing solutions using LLMs, rather than opting to be AI-centric, or focused on proprietary AI.
What have we covered in the Series?
To better understand the diverse ways AI is being applied across education, we have dived into how AI is impacting 3 different parts of our education systems:
K12 - how AI can impact schools, from core administrative support to teacher, learner and parent support
Higher Education - How AI can support central universities and faculty operations, student experience and research
Vocational Education - How AI can support learning and training that is tightly aligned with specific industry needs
Which stakeholder groups have we considered?
Within each part, we have structured our commentary around three key stakeholder groups: Administrators, Educators/Researchers, and Learners.
Each group’s typical remit and responsibilities represents a distinct area where AI can impact educational institutions– improving administrative efficiency, enhancing teaching and research support, and creating connected, engaging learning experiences
So, how can AI solutions help stakeholders in education and learning organisations?
They can help:
Educators to plan lessons, generate content, and grade coursework
Students to receive adaptive learning support, complete homework more effectively, and get immediate, targeted feedback on their progress
Administrators to optimise staffing and resource allocation, model financial scenarios, predict outcomes and improve operational efficiency.
For each part of the series, we formed a focused market map.
Here is an abridged version covering K12, Higher Education and Vocational Education (with reduced logo count):
To read the deep dives in full:
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