With every new technology, trend or tool promising to change higher education, faculty are forced to learn yet another platform, students bounce between inconsistent experiences and administrators struggle to stitch together systems that were never designed to work as one.
From automated grading assistants to generative writing apps, the sector is awash with solutions that promise transformation but too often deliver confusion. Now, with AI, universities face more challenges as they rely on a patchwork of disconnected tools, putting student data and institutional intellectual property at risk.
This is the reality of AI use in education today: fragmented, siloed and harder to manage than the problems it promises to solve.
Fragmentation is holding higher education back
The 2024 THE Digital Maturity Index report revealed that universities “struggle with fragmented platforms and insufficient expertise”, leading to data ecosystems that are siloed and underused. Critical systems such as enrolment, learning management and performance data often fail to connect, limiting universities’ ability to identify and support at-risk students.
The report also highlights how institutions continue to acquire new tools without “fully utilising them”. More technology isn’t the answer. Without integration and intention, every new AI product will only add to the noise.
The future of AI in education
Educational institutions don’t need another standalone tool – they need an AI tool that unifies the learning journey, empowering educators and learners without distracting them. Instructure believes that AI should function as a conductor rather than a soloist, aligning technology into a coherent, transparent and human-centred experience.
That means AI embedded seamlessly into existing workflows; clear “AI nutrition facts” so institutions know how each tool works and when they’re being used; and educators firmly in control of outcomes and students supported by technology. When AI is integrated into the virtual learning environment (VLE), it shouldn’t become the focus of teaching and learning but a way to enhance them.
AI’s practical impact on teaching and learning
Educator-centred AI provides rubric suggestions and feedback summaries, but educators make all the grading decisions. Automating routine tasks such as rubric drafting and translation doesn’t just save time – it gives it back to educators so they can focus on higher‑value interactions with students.
For learners, AI can remove barriers by offering personalised, accessible content that meets them where they are. When these features are opt‑in, with controls at the institutional and course level, universities gain what they want most: trust, transparency and choice.
A new phase for generative AI in education
The next wave of AI in higher education will be about embedding generative models responsibly into coursework and the VLE. This new phase is about assignments where students engage with AI in structured, outcome-aligned ways, making it a formative process rather than a surveillance activity. When most students are already experimenting with AI, the real challenge isn’t access but ensuring adoption comes with integrity. Instructure takes this approach, partnering with OpenAI, AWS, Anthropic, Microsoft and Google to enhance privacy, security and scalability.
Openness and privacy are central, and student data must remain within the VLE without being stored or used for training. Every feature should be made clear and available to all users through plain‑language explanations of how data flows, what is retained and what stays under institutional control.
Higher education institutions need to embrace technology that enables them to innovate through choice. Looking beyond a handful of point solutions, the concept of an ecosystem is imperative as it allows AI agents to perform tasks across the VLE and other systems without administrators losing control.
Return focus to the human core of teaching and learning
Higher education will outgrow the experimental phase of AI. Pilots and point solutions have shown what’s possible – and the cost of fragmentation. The path forward is about adopting coherent, orchestrated systems that are scalable, safeguard academic integrity and return focus to the human core of teaching and learning.
Successful AI adoption in higher education won’t be defined by the number of tools institutions acquire. It will be determined by how well they are integrated into the VLE and how that makes AI reliable and in the service of learning that transforms potential into opportunities.
Find out more about Instructure.
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