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19 min read

AI’s impact on the future of Higher Education

Updated: Oct 23

Education in the AI era - Part #2 : AI in HE


The landscape for AI adoption in higher education differs significantly from that in K12 education. While K12 education tends to be degrees of compulsory and generally organised by localised districts overseen by central government or private operators, higher education is typically voluntary and operates in a competitive global marketplace where institutions vie for student recruitment, retention, and success.


Unlike the standardised and centrally managed K12 system, higher education often operates in a decentralised manner, with departments and faculty holding considerable autonomy, resulting in varied approaches to technology adoption across disciplines, typically reflecting the nature of the subject matter. Additionally, some universities have more financial flexibility, drawing funding from tuition, research grants, donations and external partnerships, which could enable them to invest in advanced, innovative technologies. Universities also often balance dual missions—teaching and research—which necessitates tools that enhance both educational outcomes and support ground-breaking research.


These factors—competitive pressures, decentralised governance, financial resources, and a dual mission—uniquely shape how higher education institutions integrate AI compared to the more regulated and (typically) uniform K12 environment.


The blog is split into the following sections:



Let's dive in!

 


So, where does AI stand in European Higher Education today?

 

AI adoption in European higher education is evolving, but its development looks notably different from the patterns seen in K12 education. While both sectors are beginning to explore the potential of AI for personalised learning, adaptive content delivery, immersive and interactive learning, and content creation, the goals and applications of AI in universities are shaped by the unique structure of higher education—where the missions of teaching and research are intertwined. This distinction naturally leads to a more complex and multifaceted use of AI in universities.

 

Ivan Bofarull, Chief Innovation Officer at ESADE Business School, offers valuable insights into the current state of AI adoption. He notes, "In terms of adoption, we don't have a sense so far of seeing our lives very much improved by AI in higher education, generally speaking." This candid assessment suggests that AI adoption in higher education is still in an early, exploratory stage. Despite this overall perception, there are concrete examples of AI use at ESADE. Bofarull shares, "I would say that 20% approximately of our staff is using either Copilot or ChatGPT in order to be more productive at his or her job." This statistic provides an indication of AI tool usage in a leading European business school.



"I would say that 20% approximately of our staff is using either Copilot or ChatGPT in order to be more productive at his or her job.


We are creating communities of practice among our faculty so that they focus on specific types of impact that AI and specifically generative AI might have in their learning and in the learning experience with their students."


Ivan Bofarull

Chief Innovation Officer at ESADE Business School

 

 

To foster AI adoption and knowledge sharing among faculty, ESADE has taken an innovative approach. Bofarull explains, "We are creating communities of practice among our faculty so that they focus on specific types of impact that AI and specifically generative AI might have in their learning and in the learning experience with their students." These communities, consisting of 10-15 professors each, meet regularly to share insights and experiences from their AI implementation efforts in teaching.

 

While institutions like ESADE are proactively exploring AI's potential, recent research reveals a significant gap between student expectations and university readiness. A YouGov-Studiosity survey of 2,422 UK students found that 64% believed their universities were not adapting quickly enough to include AI support tools for their studies. This sentiment is particularly noteworthy given the potential financial implications for institutions. The survey also highlighted a disparity between domestic and international students, with 57% of international students expecting AI support compared to only 37% of domestic students. This difference is crucial, as international students often contribute substantially to university finances through higher tuition fees and perhaps arrive at their universities with expectations regarding the quality of the course they receive and surrounding support, including use of technology.

 

 

The gap between student expectations and institutional readiness underscores a critical challenge in AI adoption. A significant theme that emerged from discussions with leaders like Professor Ian Dunn at Coventry University is the critical role of data management in this process. Universities are beginning to recognise that effective AI use hinges on how well they can organise and harness their institutional data. Higher education institutions have traditionally not been structured to treat their interactions with students as data points, unlike other sectors like retail, where customer interactions are systematically tracked and analysed. This shift towards a data-driven approach is crucial but requires substantial investment in infrastructure.

 

In terms of AI applications, European universities are exploring a broad spectrum of tools. However, the decentralised nature of higher education poses a notable challenge: the lack of integrated solutions that can cover the entire student journey across diverse departments and disciplines. This decentralisation contrasts with the more standardised and centrally managed K12 system, where AI tools are often implemented uniformly. In universities, different departments often adopt technology at different paces and for different purposes, complicating efforts to create a unified, institution-wide AI strategy.

 

Despite these challenges, AI's potential in higher education remains significant. Dunn points out that AI's greatest near-term impact might lie in administrative tasks and the automation of processes like customer service, admissions, and student support systems. The adoption of AI tools like Salesforce's Einstein or AI-integrated student record systems is already underway, albeit at a developmental stage. These tools promise to centralise and streamline administrative functions, potentially reducing costs and improving efficiency. However, their implementation requires careful consideration of data security, integration challenges, and significant financial investment.

 

The path forward for AI in European higher education is not without obstacles. Formal guidelines for AI usage remain sparse, with a UNESCO survey from June 2023 finding that only 13% of universities had issued formal guidance for staff and students on responsible AI use. The initial reaction to generative AI has also been mixed, with some institutions adopting a defensive stance due to concerns about academic integrity. However, as Dunn notes, the "genie is out of the bottle," and institutions are beginning to accept that they cannot simply ban or restrict these technologies.

 

Looking ahead, the introduction of the AI Act on August 1, 2024, is expected to shift the landscape of AI adoption in European higher education. As the first comprehensive regulatory framework on AI in the EU, the Act will play a critical role in shaping how universities implement AI technologies. While it should help ensure transparency and safety in AI applications, it may also initially slow down adoption as institutions grapple with its regulatory implications.

 

In conclusion, AI in European higher education is evolving in a complex environment where data integration, cohesive solutions, and regulatory considerations all play significant roles. While there is enormous potential for AI to transform learning, teaching, research, and administration, realising this potential will require a focus on developing robust data management strategies, fostering collaboration across departments, and navigating new regulatory frameworks with precision. The journey ahead is challenging but filled with opportunities for those institutions willing to innovate and adapt.

 


Mapping the momentum: where are startups doubling down in Higher Education?

 

As AI permeates higher education, startups are playing a pivotal role in shaping this transformation. The market map (shown below) illustrates where these companies are concentrating their efforts, highlighting both established areas of focus and emerging opportunities for growth. In particular, we will touch on three areas where AI is being adopted: 1) research support, 2) administrative tasks and academic integrity/assessment, and 3) personalised & adaptive learning paths and interactive & immersive learning.

 

 

1. Research Support

 

Artificial intelligence is rapidly establishing itself as a fundamental component in research support across higher education, providing a diverse range of tools designed to streamline the research process by automating tasks, handling large datasets, and aiding in the discovery and synthesis of new insights. To better understand how AI is being adopted in this area, it helps to think of research support as a matrix, where tools can be categorised based on two dimensions: the type of research they support (new research versus existing research) and the nature of their function (administrative versus discovery).

 

In the realm of administrative tools for new research, AI is being leveraged to enhance the organisation and management of research projects from their inception. Take Labguru (US), for example, which integrates AI to provide an electronic lab notebook and research management platform. This tool assists researchers in planning, documenting, and tracking the progress of experiments. By automating data entry, managing protocols, and even predicting potential issues based on previous data, Labguru allows researchers to focus more on innovation rather than getting bogged down by logistical details. Similarly, RSpace (UK) offers a digital lab notebook system that incorporates AI to manage data and documents across both new and ongoing research projects. This tool not only helps researchers organise their work but also connects them with relevant past studies, enhancing the continuity and integration of new research efforts, which is crucial in multidisciplinary fields.

 

On the other hand, administrative tools for existing research focus on managing and organising vast amounts of already published material. AI-powered tools like Zotero (US) and Mendeley (UK) offer advanced features for citation management and research organisation. These platforms utilise AI to automate the tedious task of sorting through citations, identifying duplicates, and ensuring that references are correctly formatted. Moreover, their AI capabilities extend to suggesting related literature, helping researchers stay updated with the latest developments by analysing their existing libraries and research focus. Another example, Scholarcy (UK), stands out as a tool that uses AI to summarise research papers and generate interactive flashcards from academic content. This not only accelerates the comprehension of complex literature but also aids in organising readings and managing references more effectively—critical for scholars juggling multiple projects or fields of study.

 

When it comes to discovery tools for new research, AI plays a pivotal role in exploring uncharted territories. IBM Watson Discovery (US) exemplifies this by offering an AI-powered search and text analytics platform capable of sifting through enormous datasets to uncover patterns, trends, and insights that might not be immediately apparent. This capability is particularly valuable in new research, where identifying novel hypotheses and research directions can set the stage for ground-breaking discoveries. By leveraging natural language processing (NLP) and machine learning, IBM Watson Discovery can analyse complex, unstructured data, making it easier for researchers to draw connections between disparate pieces of information, which might otherwise remain overlooked. In Europe, Causaly (UK) is a powerful AI tool that enables researchers to explore causal relationships in biomedical research. This tool uses AI-driven natural language processing to read and understand scientific literature, allowing researchers to quickly identify new research pathways by revealing hidden connections between biological entities, diseases, and treatments. The ability of Causaly to pinpoint these relationships is particularly useful in identifying novel hypotheses and advancing new research, potentially accelerating the development of new treatments or the understanding of complex biological processes.

 

For discovery in existing research, AI tools like Iris.ai (NO) are at the forefront. Iris.ai employs a combination of NLP and machine learning to assist researchers in performing comprehensive literature reviews. By analysing the content of thousands of academic papers, Iris.ai can map the landscape of existing research, identifying key themes, gaps in knowledge, and potential avenues for further study. This AI-driven approach not only speeds up the literature review process but also enhances its depth, providing insights that might be missed through manual review alone. Such capabilities are becoming increasingly essential as the volume of published research continues to grow exponentially, making it nearly impossible for researchers to stay on top of relevant literature without AI assistance. Another innovative European example is Flow.bio (UK), which combines AI with biosensing technology to monitor physiological data in real-time. The AI component in Flow.bio processes the vast amounts of data it ingests, identifying patterns that can inform the ongoing analysis of past studies. This approach allows researchers to revisit and refine their previous work, potentially uncovering new insights from data that had already been collected but not fully analysed. The integration of AI and biosensing in Flow.bio not only enhances the accuracy of the research but also contributes to the development of personalised medicine and other advanced fields by providing deeper insights into existing datasets.

 

The integration of AI across this matrix of research support—whether in administrative tasks or discovery processes, for new research or existing studies—enables universities to optimise their research workflows. This holistic approach to AI adoption ensures that both the logistical and intellectual aspects of research are supported, driving more efficient, insightful, and innovative academic work. As AI continues to evolve, its role in research support will likely expand, offering even more sophisticated tools that can predict trends, suggest new areas of study, and automate increasingly complex tasks.


 

2. Administrative Tasks

 

Beyond research, AI is increasingly being adopted to streamline administrative tasks within universities, significantly improving the efficiency of support systems. One example is LearnWise (NL), which is developing AI-driven solutions to enhance administrative operations. Their AI assistant, Aiden, offers personalised, 24/7 support for both students and faculty, handling inquiries related to student services, IT issues, and academic departments. Aiden’s smart routing feature ensures that queries are directed to the most relevant department, seamlessly integrating with existing ticketing systems to expedite internal processes. By automating routine support tasks, LearnWise not only reduces the administrative burden on university staff but also has the potential to significantly improve response times and overall satisfaction for students and faculty alike.


Another area where AI is making inroads is academic integrity, an essential component of university administration. Tools like Rosalyn (US), a Brighteye portfolio company, are at the forefront of AI-powered exams and academic proctoring. Rosalyn uses advanced facial recognition and behavioural analysis to monitor students during exams, ensuring that assessments are conducted fairly and securely. Its real-time proctoring technology detects suspicious behaviours or irregularities, which is particularly valuable in maintaining academic standards during remote exams, where traditional invigilation is challenging. Similarly, as concerns over AI-generated content and plagiarism rise, universities are turning to AI tools to detect academic dishonesty. Turnitin (US), a widely recognised platform, leverages natural language processing to compare student submissions against extensive databases of academic content, identifying potential plagiarism. Turnitin has become a staple in many universities, playing a crucial role in maintaining rigorous academic standards and addressing the growing challenge of AI-generated essays and content in higher education.

 

3. Personalised & Adaptive Learning Paths and Interactive & Immersive Learning

 

Similar to K12, AI is also driving advancements in personalised and adaptive learning paths, as well as interactive and immersive learning environments in higher education. Startups like Algor (IT), Knowunity (DE), and Present Pal (UK) are leading the way, offering solutions that cater to the growing demand for student-centred, engaging educational experiences. These companies leverage AI to tailor content to individual learning styles and paces, making education more accessible and effective. Algor (IT), for instance, provides tools that customise learning experiences in real-time, ensuring that students receive the support they need to succeed. Meanwhile, Present Pal (UK) enhances public speaking skills through immersive, AI-driven simulations, while Knowunity (DE) fosters collaborative learning with AI tools that facilitate discussion and personalised content recommendations. Moreover, these technologies make education more inclusive. Present Pal, for example, supports students with learning difficulties by providing tailored assistance, ensuring they can participate fully in all aspects of their courses. As AI continues to evolve, its role in personalised and immersive learning is set to expand, further transforming educational practices and improving outcomes across higher education.

 

While these areas are thriving, the map also highlights potential white spaces in critical categories like Professional Development. Tools for educators remain underrepresented, despite the rising demand for platforms that can personalise training and learning, and improve teaching effectiveness. Unlike K12 educators, who often undergo formal training, many university professors are researchers first, with limited exposure to teaching methodologies. Yet, universities have a vested interest in ensuring their professors can teach effectively for several reasons: university reputation is closely tied to teaching quality, which sometimes directly but often indirectly impacts student recruitment; student retention and success are heavily influenced by how well professors engage and support their students, which in turn affects university rankings and accreditation; and finally, students who have a positive educational experience are more likely to become future donors, strengthening the university’s long-term financial position.

 

Moreover, Professor Ian Dunn highlighted that there is a growing need for AI tools that focus on student wellbeing and support - universities are increasingly interested in AI systems that can track student behaviour, flag potential issues, and provide proactive support. Startups that can offer solutions in this area, particularly those that address both academic and personal challenges faced by students, would likely tap into a significant market opportunity.

 

4. Accessibility solutions


There is, rightly, a growing emphasis on accessibility, particularly for students who are less comfortable with traditional methods of learning due to challenges like reading difficulties, auditory processing issues, or attention disorders. Universities are increasingly adopting tools and technologies that enable all students to participate fully in academic life, with a notable focus on improving how students take and manage their notes.


For students with disabilities or learning differences, traditional note-taking methods during seminars and lectures can be overwhelming or even inaccessible. In response, several Edtech innovations are gaining traction to meet these challenges:


Speech-to-text and audio transcription tools: dictation solutions allow students to convert spoken words into text in real time. This aids students who may find it difficult to focus on listening and writing simultaneously, providing them with accurate, automated transcripts of seminars and lectures for easier review later.


AI-powered note summarisation: with advancements in AI, platforms are now offering automatic summarisation features, helping students condense lecture content into key points without manually sifting through hours of material. Tools like Glean or ScribeSense can analyse and highlight important information, allowing students who struggle with attention to focus on core ideas.


Multimodal note management platforms: platforms that integrate various types of content (audio, video, text, and images) into a single interface are becoming invaluable for students who need diverse ways to engage with material. Tools like Notion or Evernote let students combine lecture notes, images of whiteboard notes, and voice recordings into one accessible location. This flexibility is particularly helpful for students with dyslexia or ADHD who benefit from visual and auditory reinforcement.


Assistive technology integration: universities are incorporating assistive technologies such as screen readers, magnifiers, and customisable font sizes into note-taking tools, making it easier for students with visual impairments or reading difficulties to navigate their notes. Text-to-speech capabilities also allow students to listen to their notes rather than read, catering to auditory learners or those with cognitive load concerns.


Solutions in this space include: Zotero, Claroread, Glean, Structured and Timetree.



Weighing the Cost: Are Universities Committing to the AI Spend?

 

Universities around the world increasingly recognise the importance of AI - much of their financial commitment has been directed toward AI research and teaching. Significant investments are being funnelled into establishing AI research hubs, developing AI curricula, and recruiting top AI researchers. For instance, the University of Southern California has committed over $1 billion to its AI initiative, which includes the creation of a new department, the hiring of 90 new faculty members, and the construction of a seven-storey building to house its AI department. Similarly, the New Jersey Institute of Technology is launching two new AI graduate programs to meet the rising demand for AI engineers and analysts​. In Europe, similar initiatives are emerging. The University of Oxford is sharing in an £80 million investment through the Engineering and Physical Sciences Research Council to develop next-generation AI technologies. In Denmark, the Pioneer Centre for Artificial Intelligence was granted €47 million to lead world-class AI research with a focus on societal challenges, ethics, and AI design. Meanwhile, in Germany, the Technical University of Munich (TUM) partnered with SAP to open a new research centre, investing €100 million to foster AI software solutions​.

Adoption of AI tools for internal university operations is more limited. While European universities are exploring a broad spectrum of AI applications, the decentralised nature of higher education often leads to fragmented spending and implementation across departments. This means that, although AI tools are being adopted, their integration is typically piecemeal, and substantial financial commitments towards a cohesive, institution-wide deployment are sparse. Some leading universities, such as ETH Zurich and the University of Michigan, are opting to build in-house AI solutions tailored to their specific needs. ETH Zurich’s Ethel, for instance, is an AI assistant designed to support grading and feedback, reflecting a preference for custom solutions that offer greater control and customisation. Similarly, the University of Michigan has developed proprietary tools like U-M GPT and U-M Maizey, leveraging internal resources to address their unique requirements. This trend suggests that well-resourced universities may prioritise bespoke solutions over third-party vendors, focusing on customisation and control.

 

​​This does not imply that startups lack opportunity in the higher education space. On the contrary, the market dynamics suggest that there is a significant untapped opportunity for AI startups to deliver AI tools and solutions to less resource-rich universities. Institutions that do not have the financial or technical bandwidth to create their own AI solutions are prime candidates for third-party providers. Startups could help these universities rapidly adopt AI tools that improve operational efficiency, enhance student engagement, or streamline admissions, all while offering scalability and cost-effectiveness that these institutions desperately need to remain competitive.

 

However, the real challenge for startups lies in universities’ complex procurement dynamics. Universities that lack the resources for internal development are more likely to adopt plug-and-play AI solutions, but the sales process in higher education can be lengthy and often involves navigating bureaucratic hurdles. Startups will need to demonstrate clear ROI and offer low-barrier pilot programs to entice universities into AI adoption. Given the current focus on AI research and teaching, startups could find opportunities in areas where universities are struggling to keep pace with student demands or where operational inefficiencies are slowing down administrative processes. The burgeoning demand for AI-enhanced student services, such as AI-driven chatbots for inquiries or predictive analytics for identifying at-risk students, presents a compelling entry point for startups that can offer easy-to-deploy solutions. Within these challenges, startups can position themselves as a medicine, not a vitamin…

 

The future of AI adoption in universities may hinge on how well startups can fill the gap between large, resource-rich institutions like ETH Zurich or TUM, which are building their own tools, and smaller institutions that need turnkey solutions. Additionally, as government-backed AI initiatives across Europe and the U.S. continue to expand, startups can potentially align their offerings with these public investments, leveraging government grants or partnerships to accelerate AI integration in higher education.

 

Ultimately, while leading universities are taking custom approaches to AI integration, the broader market is still largely untapped, especially in Europe. Startups that can offer solutions tailored to the unique needs of mid-sized and smaller universities—which are eager to adopt but unable to build—stand to benefit the most as the higher education sector becomes increasingly reliant on AI to maintain competitiveness and meet evolving demands.

 


Blueprints for success: How are AI startups succeeding in Higher Education?

 

AI adoption in higher education comes with its own set of unique challenges, from decentralised decision-making and faculty autonomy to the sheer diversity of academic needs across institutions. Unlike K12, where top-down decisions often shape technology adoption, universities require a more nuanced approach. Despite these complexities, AI startups are finding success by employing strategic go-to-market approaches that align with the needs of higher education institutions.


  1. Leveraging faculty pilots for bottom-Up adoption: In K12, district-wide or school-wide decisions often drive AI tool adoption, making a single contract impactful across entire schools. However, in higher education, individual faculty members and departments often control their own budgets and are responsible for decision-making. Companies like Labster (DK/ US), which offers AI-powered virtual labs, have capitalised on this by focusing on bottom-up adoption. Rather than trying to win over an entire university at once, they run pilots at the department level, allowing faculty to test their tools and generate buy-in organically. This strategy is particularly effective in Higher Education, where broader adoption often follows once a tool proves its value within a specific department or within individual teaching staff. Unlike K12, where centralised approval can fast-track deployment, universities require startups to build trust from the ground up.


  2. Framing AI tools around student outcomes: Another key distinction is the emphasis higher education places on student outcomes, particularly retention, graduation and student recruitment rates, which directly affect funding, rankings, and accreditation. Startups like Civitas Learning (US) focus on AI-driven student success tools that identify at-risk students and offer targeted interventions. In contrast to K12, where AI tools are often positioned as enhancing curriculum delivery or classroom management, higher education institutions are more motivated by solutions that can demonstrate measurable improvements in student success metrics. Startups that clearly link their AI tools to universities’ KPIs on graduation, retention or student recruitment find more traction in this space.


  3. Positioning as a compliance ally in a more complex regulatory environment: Data privacy is a concern in K12, but in higher education—particularly in Europe—universities face even more stringent requirements under regulations like GDPR. AI startups need to not only offer cutting-edge tools but also ensure they meet these rigorous privacy standards. Turnitin (US), for instance, has successfully navigated this by emphasising the ethical use of AI and their commitment to data privacy in compliance with EU regulations. This is a more nuanced approach than in K12, where data privacy concerns are managed at the district or school level.


  4. Selling through enterprise partnerships: Another strategic approach for startups is selling their AI tools to enterprise companies that already have strong relationships with universities. For instance, partnerships with established Edtech companies, academic publishers, or digital learning platforms like Pearson or Elsevier allow AI startups to scale by embedding their technologies into widely-used solutions. This enables universities to access cutting-edge AI tools without the need to overhaul their existing systems. By becoming a value-adding layer on top of already trusted platforms, startups can bypass long sales cycles and build credibility by association, making their tools more readily accepted by universities.


  5. Collaborating to offer bundled solutions: Ian Dunn emphasised the potential for startups to collaborate and offer bundled solutions, addressing multiple university needs in a single package. By partnering with other edtech companies, startups can provide comprehensive, integrated tools that appeal to diverse departments, simplifying the adoption process. This approach not only broadens a product’s appeal but also accelerates market penetration by leveraging shared resources and customer bases. Bundled solutions offer universities a one-stop, scalable option, making adoption more accessible and impactful across various academic and administrative functions.

 


AI’s Evolution: what can Higher Education expect next?

 

As AI continues to evolve, the future of its application in higher education is expected to expand far beyond current innovations. The next wave of AI in universities will bring entirely new tools, services, and platforms that can reshape how education is delivered, how research is conducted, and how institutions function as a whole. Here are some opportunities and innovations that could emerge in the future:


  1. AI-Generated personalised degree programs: One ground-breaking idea is the development of entirely personalised degree programs tailored to individual student interests, career goals, and learning styles. Startups could build AI systems that dynamically create degree pathways for each student, pulling together courses, projects, internships, and certifications. These AI-powered platforms would assess a student’s prior knowledge, track their academic performance in real-time, and adjust the curriculum on the fly. Imagine a student’s degree that evolves with them, continuously updated based on the latest job market trends and personal growth. Universities adopting this AI-generated curriculum could completely transform how education is delivered, making learning more adaptable and individualised than ever before.


  1. AI for research commercialisation: As discussed, universities are not only centres of learning but also hubs for innovation and research. AI could help bridge the gap between academic research and its commercialisation. Startups that develop AI tools to identify patentable ideas, analyse market readiness, or connect researchers with investors and industry partners could unlock new pathways for universities to turn research into revenue. Such AI tools could help universities navigate the often-complex process of translating academic findings into commercially viable products or services, creating a new wave of university-led startups.


  2. AI-Powered academic wellbeing and mental health tools: Similar to K12, with mental health becoming a growing concern on campuses, startups could emerge that use AI to monitor and support student well-being. Imagine an AI system that tracks patterns in student behaviour—such as attendance, assignment submissions, or social interactions—to identify early warning signs of burnout or depression. Such a tool could recommend interventions or connect students with counselling services before they fall too far behind.


  3. AI for "living" curriculum creation: Instead of static syllabi, AI could create "living" curricula that evolve continuously based on the latest academic research, industry needs, and student feedback. Startups could develop AI systems that dynamically update course content, pulling in the newest research papers, case studies, and data trends from around the world. This would allow universities to keep their curricula relevant in rapidly changing fields like biotechnology, or climate science. Students would always be learning from the most current knowledge, and professors would be free to focus more on facilitating deep discussions and guiding critical thinking, rather than updating content.

 

In this future, AI becomes more than a tool—it’s an essential partner in driving higher education towards unprecedented levels of customisation, collaboration, and intellectual development. Startups that can build visionary solutions in these areas will help universities unlock the full transformative power of AI.

 


As ever, if you'd like to discuss this work or you are a founder working on a solution in this space, we would love to hear from you, so please do get in touch with rs@brighteyevc.com or via the deck submission form on our homepage.


 We are grateful to Sabrina Bukenya, from Stanford's Graduate School of Business, for her support on this project.



NEXT UP: AI in Vocational Education


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