How will AI transform teaching and learning at universities?

Teaching and learning in HE market map, by Emerge Education.

We’re building our annual list of the top emerging edtech companies in higher education for 2024, in collaboration with our Higher Education Edtech advisory board which is convened in partnership with Jisc and chaired by Mary Curnock Cook, CBE. As we do this, we’re diving into the trends and opportunities for tech-powered innovation along each step of the learner journey → from student recruitment to staff and student experience, teaching and learning, assessment and graduate employability.

In this fourth article, we’re casting an eye over the seismic changes to learning access, technology-enabled instruction and academic support that have occurred since the emergency pivot to online learning during the Covid-19 pandemic. Then, we estimated that a decade’s worth of edtech acceleration was packed into one year — but that was only the beginning, as the explosion in natural language interfaces for generative AI just two years later sent shock waves through the sector. Universities have had to adapt at an unprecedented pace — and the edtech market has been ripe for innovation. How will genAI change higher education?

Academic support here covers teaching and learning tools that develop learning communities, build interactive course content, and analyse classroom engagement and collaboration.

Teaching and learning is, of course, a massive category with many complex subcategories and so this article offers merely a snapshot 📸 of the current state of play. Please share your thoughts 💭 to enrich and expand the conversation 💬 in the comments, and take a look at our recommendations for deep dives 🧐 into different issues at the end.

The student journey in higher education.

Read on for:

• Challenges, trends and opportunities, including our predictions for the transformative impact of genAI
• Views from sector experts, plus tips for founders
• A mini-market map of key players and top emerging startups in this space

Keywords: teaching, learning, students, learners, educators, classroom, academic resources, academic support, study materials, lectures, seminars

💡 Why it matters

Robots will replace teachers by 2027.

That’s the bold claim British education expert Anthony Seldon made in 2018. He may have been the first to put a date on it, but plenty of others are doubling down on the principle, such as Bill Gates, who believes that AI-powered chatbots will become as good as any human tutor, and Khan Academy’s founder Sal Khan, who opened his 2023 Ted Talk by arguing ‘we’re at the cusp of using AI for probably the biggest positive transformation that education has ever seen’.

When ChatGPT made its public debut two years ago, the CEO of OpenAI predicted that it ‘will eclipse the agricultural revolution, the industrial revolution, the Internet revolution all put together’. With every new release, this looks less and less like hyperbole. ChatGPT swiftly reached more than 100 million unique users, and 30% of US college students said they had used it for assignments, making it one of the fastest-ever applications ever adopted — certainly in education settings.

At the same time, a febrile public debate about the role of higher education rolls on. ‘Ripped off’. ‘Taken advantage of’. ‘Exploited’. The ‘value for money’ question has the sector on the backfoot, grappling with existential questions about the societal good of education alongside pragmatic challenges around how to effectively support students in such a rapidly changing landscape.

🏈 State of play

“It is clear that AI will bring the next generation of teaching products (curriculum to podcasts, to flashcards, to study notes, to TikTok, AI tutors, AI homework assistants) and that over the next few years there will be a plethora of new tools in this area. The question is whether these innovations will be features, products or companies. I think very few will be the latter.”

Rob Cohen, ex-president, chief operating officer and chief financial officer at 2U, and Emerge VP

• To date, university spending on technology has focused on core infrastructure. Most mid- and large-sized universities spend significant amounts on technology used to attract students and process applications (through CRM and similar software), store student information and educational materials (SIS and LMS), manage business intelligence (ECM / EBI), and manage staff and business operations (ERP). This is all in addition to IT services, telecoms, generalist software, hardware and devices, and data centres. In total, US universities alone spend $16bn per year on technology.

• Classroom and lecture hall interaction technologies such as real-time chatting, polling and breakout room discussions were the most widely used tools before the pandemic and remain so. The general trend in the student journey is that digitally-enhanced learning and teaching is enabling end-to-end coverage, moving from generic, piecemeal university systems to student-centric, personalised systems.
• Student engagement is a major focus in global higher education, not least because data shows clear correlations between student engagement, academic outcomes, progression and retention. In terms of impact, Universities UK’s overview of lessons learned from the pandemic highlights that the move to digital teaching and learning has coincided with a narrowing of attainment/awarding gaps. Many examples of how edtech is already contributing to improving student engagement are highlighted in our report, Enhancing student engagement using technological solutions.
• ChatGPT has already taken the education sector by storm, threatening to disrupt traditional learning practices by changing the way we teach and learn. While some educational institutions initially banned the use of ChatGPT — mainly due to concerns around how it could enable cheating — the momentum now is towards establishing ground rules and guidelines around generative AI use.
• While institutional stakeholders debate next steps, students are flying up the adoption curve. Within just 100 days of ChatGPT’s launch, nearly one in three students reported being a regular user of generative AI tools — a rate of growth we have, quite literally, never seen before. More than half of UK students have used generative AI for help on assessments, but very few are likely to be using AI to cheat — a majority of students consider it acceptable to use generative AI for explaining concepts (66%), suggesting research ideas (54%) and summarising articles (53%), but only 3% think it is acceptable to use AI text in assessments without editing. Sixty-five percent of students are ‘quite’ or ‘very’ confident that lecturers can detect if AI is used; they feel staff understand how AI works.
• There is a growing demand from students to integrate generative AI across the curriculum. In the past year, student perceptions have actually strengthened on this issue: learners increasingly view generative AI as a collaborative tool to coach and support active learning and critical thinking. In other words, learners instinctively use AI tools as a digital assistant rather than seeing them purely as answer providers. Students also emphasise the importance of generative AI-ready skills for employability. Beyond the UK, Chegg’s 2023 Global Student Survey spoke to 12,000 students across 15 countries and again showed that globally, students are embracing generative AI, with 40% reporting using it in their studies and 65% saying they want training in AI tools relevant to their future career. Another survey of 2,000 students on 2- and 4-year college degrees in the US made it clear that there is no turning back: 51% of students will continue to use generative AI tools even if it were prohibited by their instructors or institutions. (It is, however, important to note that these surveys focus on undergraduate students, often full-time, and may not capture the full spectrum of opinions, especially from nontraditional learners.)
• In contrast, 71% of instructors and administrators have never used these tools, with 32% reporting that they are not even aware of them. This is important because instructors, administrators or students who have experimented with generative AI tools are far more likely to recognise their potential value in education.
• Early adopter instructors who are regularly using these tools are, on the whole, opting to make changes to course design as they find ways to integrate AI into their teaching methods. Currently, many educators report drawing the line at using the tools to generate text, whereas non-generative uses of these AI tools (e.g., brainstorming, editing and outlining) are seen as more permissible.
• One of the unexpected outcomes of the ChatGPT phenomenon is the news that generative AI is considerably more prevalent in the arts than elsewhere in higher education. In June 2024, European art schools said that most students had integrated AI into their work.
• Some technologies lag behind AI in adoption, and many are focused on individual work, whereas the emphasis after the Covid-19 pandemic is on collaborative tools. (Technologies that enable connectivity and community building, such as social media–inspired discussion platforms and virtual study groups, saw the biggest uptick in use, followed by group work tools.) Tools enabling student progress monitoring, AR/VR, machine learning–powered teaching assistants (TAs), AI adaptive course delivery and classroom exercises are currently used by less than half of respondents to a recent McKinsey survey. Anecdotal evidence suggests that technologies such as AR/VR require a substantial investment in equipment and may be difficult to use at scale in classes with high enrollment — although there is enthusiasm for its ability to improve learning outcomes. For example, students at ASU who used a VR tool to complete coursework for an introductory biology class improved their subject mastery by an average of two letter grades.

🚨 Challenges

• Forty percent of students are not satisfied with their instructors’ use of technology in the classroom. Yet keeping students engaged and motivated is a challenge that touches on many others, including learning design, building community, wellbeing and digital inclusion. It’s a tough time to be a student. In 2004, 34% of first-year students spent about 15 hours per week studying. In 2017, this figure had increased to 45%, while in 2019, this percentage fell two percentage points to 43%. On average, students currently spend around two more hours per week studying than fifteen years ago. This is often while juggling onerous working hours in paid jobs, too.
• Digital inequalities must be recognised. According to OfS polling, during the Covid-19 pandemic 52% of UK students said their learning was impacted by slow or unreliable internet connection, with 8% ‘severely’ affected; 71% reported lack of access to a quiet study space and 18% were impacted by lack of access to a computer, laptop or tablet.
• Digital capability and confidence. While there was a huge leap forward in staff and student digital capabilities over the past three years, there is still work to be done. According to the Jisc 2023 teaching staff digital experience insights survey, digital technologies are being used extensively in higher education teaching, yet only 39% of staff received guidance about the digital skills needed for their course. In other areas, staff felt the support received was even less, including: receiving an assessment of digital skills and training needs (16%); being provided with time to explore new digital tools or approaches (16%); and being offered formal recognition for their digital skills (8%). Support for teaching staff to access platforms and services off campus was actually below pre-pandemic levels (55%). Fear and lack of confidence are still significant barriers to staff digital adoption.
• Support and awareness around data remains particularly low compared to other areas of digital skills and competencies. Jisc found that only around a third (34%) of teaching staff agreed that they understood how their university collected and used student data (31% disagreed). Less than half (45%) were offered training on keeping data secure.
• Students’ digital and AI literacy skills vary widely, too. Worryingly, few students see AI ‘hallucinations’ as a problem, which suggests they are not verifying information and may be using inaccurate information. More than a third of students who have used generative AI (35%) do not know how often it produces made-up facts, statistics or citations (‘hallucinations’). AI can produce coherent text that is completely erroneous.
• Relatedly, the AI models with most penetration so far do not optimise for student learning. ChatGPT, for instance, is trained to deliver answers as fast as possible, but that is often in conflict with what would be pedagogically sound, whether that’s a more in-depth explanation of key concepts or a framing that is more likely to spark curiosity to learn more. As Edtech Insiders warned recently, the best solutions are not necessarily the ones that will thrive: ‘Do I think that big tech companies will figure out how to do assessment, content, or data insights better than edtech native companies? Absolutely not. But, the pattern we have often seen is that a free B- rated product often beats a paid A+ rated product in the education space. And even if buyers are willing to pay a premium for a “better” offering, how much more than free?’
• The exception to this is Google, which is doubling down on its channel/distribution advantage to natively embed AI into every one of its popular surfaces, including Search, Workspace, Android, Google Classroom, YouTube and more. Interestingly, these technologies are being sold on the potential they hold for education and LearnLM, Google’s new family of models based on Gemini, is grounded in educational research and fine-tuned for learning. (You can read LearnLM’s comprehensive technical report here.) Google is partnering with Columbia Teachers College, ASU, MIT Raise and Khan Academy to test and improve these new capabilities, as well as creating a host of other solutions such as Learning Coach (a customised Gemini, or GEM, offering step-by-step study guidance) and NotebookLM (users can upload materials to instantly generate summaries and quizzes).
• Legacy technology remains a significant issue for almost all universities. Institutions are constantly attempting to make better use of and integrate existing digital tools, cloud-based architecture and Software as a Service (SaaS) solutions. Proliferation of hardware, processes and software through academics’ individual choices can also add complications, although flexibility is needed so that academics have the freedom to develop the digital solutions that their discipline demands. Greater integration of interoperability standards would help, making integrations across diverse digital solutions easier. There are two major risk factors for every HE provider with legacy systems: cybersecurity and challenges in recruiting IT staff. Cybersecurity is already a significant issue for the education sector, with Jisc’s latest cyber security impact report underlining ransomware as the number one threat and the devastating attack on the British Library a stark warning.
• Data silos and a lack of data integration. More effective use of data offers immense potential but is still a mountain to climb for many universities. In HE, IT has tended to evolve in an organic way, resulting in piecemeal, in-house, one-off siloed systems rather than a holistic picture. Decentralised university structures of departments, school and faculties has made data silos the norm. The result is many individual point solutions, multiple points of failure and weaknesses, but few common standards and limited interoperability. Beyond data, creating a meaningfully integrated experience for all the various elements in a university’s digital environment can be a challenge.

🔥 Trends

• It doesn’t seem a stretch to imagine an educational future in which AI tutors are guiding students through their personalised learning journey, learning about their individual abilities, identifying gaps in their learning, and providing tailored feedback and support. The embedding of AI tutors would represent a transformative shift in higher education. It would allow institutions to deliver high-quality programmes at scale and at a lower cost, making learning more widely accessible and affordable for students. It would also allow faculty the time and freedom to build relationships with their students and focus on uniquely human in-person value-adds.
• The ideal vision is one where AI and faculty work together to deliver the best outcomes, rather than a two-tiered system where the less privileged are left with a low-cost, automated education. It is worth noting that we have faced a similar scenario before, when the media predicted MOOCs would replace traditional learning. One of the fears was that we would end up with a relatively small number of elite universities delivering high-touch, high-quality education to those who could afford it and low-cost, low-support MOOC-style courses delivered to the masses. This didn’t come to pass. Today, MOOCs are more widely recognised for offering lifelong learning rather than degrees, and student engagement levels and course completion rates are abysmal. We could draw the same parallel with low-cost automated support here.

“The single hardest problem in the science of learning is the ‘problem of transfer’. Most of what people learn they later apply only to the context in which they learned it, but teachers usually hope that students will apply those skills and information more broadly. Specific kinds of instruction, emphasizing how varied examples are tied together by underlying principles, is required to facilitate transfer — but that kind of instruction is rarely offered.”

Stephen Kosslyn, former chief academic officer at Minerva Project and Dean at Harvard University, and Emerge VP

• The definition of ‘academic engagement’ will have to change as the role of GenAI/CustomGPTs explodes. Consider the regulations on Regular and Substantive Interaction (RSI) — these are the requirements for faculty to be actively involved in the courses, otherwise they are just correspondence courses. These provisions cannot be met solely through the use of AI or AI-generated content and require active participation by the faculty.
• We’re seeing a few clear patterns among the solutions that have emerged. We’ll split them into three buckets:

1. Resources → This category represents the materials we use to enable teaching and learning — materials that students consult asynchronously, before or after class, and are used as the backbone of the curriculum, including textbooks, supplemental readings, videos and more. Resources are still dominated by publishers who have transitioned textbooks into digital and interactive formats, coupled with the rise in online video-centred courses. We are now seeing more AI-powered study tools designed to instantly turn media resources like websites or videos into traditional ‘study materials’ such as notes, flashcards or practise quizzes (Algor, Monic.ai, Wisdolia, Studyable, Study Smarter), and ways to “chat” with textbooks, pdfs and other materials.
2. Delivery → This category represents the infrastructure required to facilitate teaching and learning, from lectures and seminars to labs, tutorials and more. For all its successes aligning with the organisation and management of learning at scale, the LMS has so far failed to become a collaborative student-centred system; students use it to access materials and grades, but not as an engaging place to collaborate on academic work. For staff, the LMS as a content agnostic platform has struggled to provide meaningful and actionable learning insights. As the guardian of infrastructure and data, the LMS has avoided a decade of ‘the LMS is dead’ predictions (through acquisitions and feature developments), but they nevertheless can be described as legacy systems rather than open architecture-based systems that can co-exist easily with other systems. This makes it difficult for other players in the learning ecosystem to expand their functionalities, remits, analytics and impact. If the traditional LMS is to be replaced, a flexible, scalable and responsive admin and organisation tool to sit between student record systems and course delivery to enrol, rollover, schedule, sequence, and more would be needed. A dedicated lightweight management tool to support this could enable experience, content and collaboration tools to be integrated, and swapped in and out as needed, without affecting course delivery and admin. These tools could involve solutions such as content creation, especially video creation and quizzes, for educators, independent course creators and educational publishers (Atypical, Prof Jim). We’re also seeing a wave of AI-enhanced search tools that improve access to academic knowledge and surface information in new and unexpected ways (Consensus, Elicit, Perplexity, Heuristi.ca). Thus far, educator assistants and co-pilots have predominantly focused on serving K-12 to save teachers time when lesson planning, creating IEPs, writing curriculum, giving feedback and more (Doowii, Brisk, MagicSchool.ai), but there are a growing number of tools for HE educators to auto-generate learning paths which, when given a subject or question, will create an instant, personalised ‘course’ (Learn.xyz, Nolej).
3. Support → Estimates suggest that up to 96% of students will require additional help with their learning at some point in their university experience, but institutions struggle to cater effectively for individual students’ needs. Support is currently mainly offered through fixed office hours and study group times. The increased flexibility and personalisation made possible by blended learning affords opportunities to scaffold support around students, and meet them more directly at their points of need. As this area evolves beyond simply sharing learning assets such as study notes, there are opportunities to innovate using audio and video, and to create subject-specific learning pathways, writing aids and peer-to-peer support communities. This will go beyond academic content, to support the development of technical skills and metalearning strategies. For example, we’re seeing a new wave of student-facing AI bots designed to be personalised tutors (Gajix, GoKoan, Hisolver) and tools to support writing and grammar skills (Quillbot, Trinka, Writefull, Smodin).

🌍 Key players

With new AI tools launching weekly, it is near impossible to stay on top of the evolving AI ecosystem. Edtech Insiders recently launched The AI Tools in Education Database — a continually updated database of AI tools in education, tagged and searchable by feature.

Here, we have created a market map of the key players, subdivided into three categories that broadly capture different aspects of the teaching and learning experience: Resources, Delivery, Support.

Teaching and learning in HE market map, by Emerge Education.

Teaching, learning and academic support is a huge category. Rather than aim for a comprehensiveness which is neither possible nor useful, we have chosen here to build a market map around startups — all of the companies featured have been founded within the past few years and all are driven by AI-native capabilities. For this reason, the LMS and learner analytics (data) subcategories appear sparse, with few new players, because they are dominated by incumbents which, even though they may not be providing optimal solutions, will nevertheless be hard to dislodge due to their level of market penetration. However, we are seeing lots of new players with valuable and transformative AI-powered offerings above this infrastructure layer.

🔭 Who is getting ahead?

Arizona State University’s Dreamscape Learn was developed through a two-year partnership between ASU and VR company Dreamscape Immersive. Dreamscape Learn’s Immersive Biology in the Alien Zoo is a virtual reality curriculum in which students explore biological concepts and hands-on tasks within an ‘orbiting intergalactic wildlife sanctuary full of endangered life forms’. Starting in 2022, classes have now been taken by more than 6,000 students. Initial research suggests that, following the Alien Zoo experience, student grades in lab work improved substantially. The Alien Zoo lab cost about $5M to develop. In January 2023, Dreamscape Learn raised $20M in series A funding.

‘Yuki’, the first robot lecturer, was introduced in Germany in 2019 and has already started delivering lectures to university students at The Philipps University of Marburg. The robot acts as a teaching assistant during lectures; he can get a sense of how students are doing academically and what kind of support they need.

🔮 Predictions

• So far, attention has focused on the transformative impact of GenAI for natural language queries and text/video/audio generation. The impact will be just as paradigm-shifting in science subjects. Celebrated mathematician Terence Tao predicts that AI will be a trustworthy co-author in mathematical research by 2026, when combined with search and symbolic maths tools. Indeed, in 2022, OpenAI used Lean to solve some math olympiad problems, while Coscientist and ChemCrow are other examples for chemistry, integrating GPT-4 with professional tools like molecular synthesis planner and reaction prediction.
• Scientists at the world’s first dedicated AI postgraduate research university, the Mohamed bin Zayed University of Artificial Intelligence (MBZUAI) in the United Arab Emirates, have been exploring smaller models that can be trained more efficiently, reducing their carbon footprint and making models much more accessible. For universities worried about the environmental impacts of large language models, with sustainability and social responsibility agendas in mind, SLMs offer efficient alternatives to costly LLMs. They require less data and computing power, and SLMs are also easier to control and correct, offering greater transparency and oversight, especially in education contexts. Localised SLMs can be developed that are also tailored to specific languages, such as Arabic, to ensure accuracy and cultural relevance.
• Edtech has a unique challenge: the end user is far from the buyer. Edtech is almost always purchased by someone other than the learner, and frequently by someone other than the teacher. This disconnect can delay recognition of quality products and entrench subpar ones in long software review cycles. The ‘product-market fit’ concept from other tech sectors, which assumes that addressing user problems will drive demand, can be deceiving — the best solutions will not necessarily dominate. Add to this the inherent difficulty in measuring edtech efficacy. (The UN recently released an alarming report highlighting that most edtech products are never evaluated, with fewer than 1 in 8 UK companies undergoing rigorous testing or disclosing third-party certifications. In the US, only 1 in 10 edtech decisions are backed by peer-reviewed evidence, and much of the data is from biased sources.)
• What will win in this context? First, anything that fills a data gap. While data is plentiful and now, thanks to AI, very easily aggregated, any company that can find and exploit a gap in the data available has significant opportunities. Second, any company that truly facilitates and personalises learning, not just by putting content online but by engaging and ensuring learning is both continuous and ‘sticky’. Duolingo is a great example.

🎯 Opportunities for startups

GenAI engines of opportunity for universities

In this category, we see particular opportunities for AI-driven solutions that offer:

• AI HE study tutors → Problem: Bloom’s 2 sigma problem. Solution: AI may turn the ‘2 sigma problem’ into a ‘2 sigma opportunity’. Generative AI can be used to provide questions, instructions and feedback to students in a dynamic way to help them with their studies just like a tutor would.
• AI-enabled and collaborative study material authoring tools for HE students (the future of ‘taking notes’) → Problem: Learners spend ages taking notes and creating other study materials like flashcards and mindmaps. Usually only small percentage of students actually do this, but they get considerable benefit from the materials. Solution: Giving learners super powers to take better notes and to take them more efficiently… such as creating synthetic videos/images, creating automatic summaries from large amounts of information, creating personalised classroom materials, transcribing lecture notes, enhancing study notes or making study materials more accessible through changing their formats or levels of complexity.
• Learning materials and classroom management co-pilots → Problem: Content creation is expensive and takes a lot of time, yet is critical. It’s basically in the job description of educators. Solution: Until now, machine learning techniques were used to recommend the right pieces of prefabricated content to learners. Today, we are starting to use generative AI to create the right piece of content for the learner from scratch based on their needs, radically reducing the cost and improving the effectiveness of learning materials creation.
• High quality digital universities for developing market students → Problem: 150M additional students in developing markets interested in strong brands and there are not enough universities to serve them. OPMs cannot serve this segment because it’s too expensive. Solution: Either a local challenger university (such as Nexford, Kibo), a Western global brand building a solution for local markets (such as Cintana) or a new model making use of AI (such as Human Systems).
• Improving access to academic knowledge → Problem: Academic knowledge is hidden away in complicated papers. Solution: Processes and tools that make it more accessible and approachable and personalised.

💎Tips for founders

• Keep it simple and stay focused: If you cannot explain your product in one sentence, you are most likely going to fail. Understand the challenges that the sector is grappling with and prioritise solutions for challenges where there is no product available, rather than offering different options for the same area of focus. Every success story in education to date has targeted very specific student pain points and learning resources: Chegg became a unicorn thanks to an early focus on textbooks rentals, Quizlet through flashcards, Coursehero through study notes and Varsity Tutors through connections with tutors. Focus on achieving large scale against a specific gap before significantly expanding product functionality.
• Sell solutions not features: There are around 25,000 HE institutions worldwide, of which 5,000 are in Europe and the US. Given this finite market size, you need to prove that your value proposition or product expansion path meets a defined need and can command a high enough price to remain successful. We have previously talked about this through the lens of model market fit theory. Businesses in education that do not achieve model market fit end up as mid-market players that struggle to raise further capital, fail to scale or are absorbed by market leaders.
• Pursue foot-in-the-door strategies: Be careful how much you are expecting from universities, especially when it comes to AI. They are risk-averse, with procurement departments that protect against mistakes but also slow down and prevent rapid adoption of new, expensive or complex products. Offering low-risk pilots, trials or small early rollouts that are priced under procurement thresholds could be smart ways to build trust and reputation before expanding to other departments and universities. Another option is to approach relevant faculties direct and find early adopter academics, but this may be tougher now as universities seek to consolidate their tech stacks after the ‘wild west’ of the Covid-19 emergency pivot.
• Build a reputation: Selling to universities is one of the most difficult sales processes after selling to government. Given the financial and reputational risks, as well as sheer operational complexity, universities find it difficult to work with startups. The best edtech startups focus on finding a couple of pioneering large universities with a proven appetite for innovation. This proves the effectiveness of their products, and they can then rely on testimonials and case studies to attract interest. Most universities are followers of trends. Focus on the needs of the staff and students using digital systems and get some benefits out to users early; either pilot in one area or build the minimum viable version and make it available to all, then adapt in the light of feedback. To be effective these processes are likely to be lightweight and iterative rather than the traditional project-management approaches so common within HE. Start small and respond to what works.
• Understand university motivations: A vague promise of better student outcomes and experiences is not enough on its own. While universities are rethinking the education they offer, edtech companies need to show how their solutions can either help increase revenue, improve student retention and / or reduce time and costs. The stronger your business case and the more robust the evidence, the easier the sell.
• Pedagogy and learning design: There are many examples of current practice, but staff are struggling to find a universal evidence base for what works in blended formats that assesses the impact of different interventions on student retention, progression, outcomes, awarding gaps and so on. Some researchers have recognised this divide and have developed initiatives in which entrepreneurs and educators work together to improve edtech products. Finding evidence-based models will be a collaborative effort between academics and pedagogical advisors, in order for each university to benefit from learnings across the sector and find opportunities to align their pedagogic approach with the digital services available. Questions universities might ask vendors on this theme include: In what ways were educators and learners included in developing the product? What were their major concerns and how were those concerns addressed? Were they representative of the various groups of students who might use these tools, including in terms of age, gender, race, ethnicity and socioeconomic background?

🔗 Read on

• How to build a unicorn by partnering with universities to diversify their revenue streams — find specific ideas on what to build and advice on the differentiators, business models and go-to-market approaches we look out for as investors in this space.
• Our deep-dive reports into this area, in collaboration with Jisc, including Digital learning rebooted (2020) and Technology-enabled teaching and learning at scale (2021).
• Jisc’s reports, such as Learning and teaching reimagined initiative (2020), and best practice guides, such as this on learner analytics and this toolkit on digital transformation.
• UNESCO has categorised the myriad ways in which large language models such as ChatGPT can support staff and students here.
• Part 1 in this series → How startups can support universities with recruitment, applications and enrollment
• Part 2 in this series → Startups for student success: how technology can help universities create a stellar student experience
• Part 3 in this series → How edtech can help universities support staff and use AI to drive efficiencies in HE

Read more news, views and research from the only fund backed by the world’s leading education entrepreneurs, in Emerge Edtech Insights.

📣 Call to action

We are now building our list of the top emerging edtech companies in HE in 2024.

👇 If you have seen an exciting company in this space, please tell us in the comments 👇

Our list analyses 100s of companies operating worldwide, using public and private data — it is crowdsourced, and voted on by our Higher Education edtech advisory board, led by Mary Curnock Cook.

Please share companies you think we should consider in comments 👇 and join us on 27 June to discover who has made the final list!

🙏 Thanks

At Emerge, we are on the look-out for companies (existing and new) that will shape the future of learning in higher education over the coming decade.

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Thank you for reading… I would hugely appreciate some claps 👏 and shares 🙌 so that others can find it!

Nic