The PeakSpan EdTech team recently attended the EDUCAUSE conference in Chicago — a yearly staple for our conference schedule and a great spot to unite Higher EdTech companies (incumbents + scale-ups), buyers (University CIOs, CTOs, Directors, etc.), and investors, like PeakSpan 🙂.

The conference was buzzing this year for our team with numerous information sessions, poster discussions, coffee chats, and meetings. Thus, we aim to synthesize some of our top themes across the ecosystem for anyone who missed the event! See below for our list of key takeaways, and please feel free to reach out to discuss any / all of them!

First, it wouldn’t be a proper synthesis without calling out one trend that is top of mind for ALL institutions this year — Generative AI (+ 3 Key Trends):

1. With the growth of AI usage in Higher Ed (where over 40% of university students admit to using AI for their assignments), professors are scratching their heads in regards to adequately instructing and assessing students. One thing is a given: the traditional assessment status quo is BROKEN. Professors must be creative with designing AI in vs. out of their traditional workflows. EDUCAUSE highlighted the plethora of ways AI could be effectively incorporated into assessments. For example, professors can require the usage of AI for prompts, revision cycles, group discussion, etc.
2. Students are already using AI to improve workflows, so why shouldn’t institutions properly i) instruct their students/faculty on optimal integration methods, and ii) take advantage of its capabilities? We heard an excellent analogy for AI — think of AI as an electric bike — i.e., something that augments existing processes and makes them better. That said, institutions need to ensure everyone knows how to “ride” this new electric bike — which can be accomplished by creating new policies and adequately training all relevant constituents.
3. Just like the looming threats we have seen mentioned for the Enterprise sector, where predictions suggest AI will replace an estimated 300M jobs globally, much of traditional manual work in the EdTech sector has the potential to be automated. We see this as a massive area of operational improvement for the student and faculty experience. Across areas like communications (e.g., chatbots, automated emails) and data management (e.g., combing through datasets + surfacing actionable reports), AI platforms can reduce the time spent on historically tedious, manual work (like fielding the same student questions over and over again!), and allow faculty to focus on creative, thought-demanding human-to-human interactions.

Procurement Cycles (Oh how they have changed!):

1. Long gone is the day of the short 1-week (or even 1-month!) sales cycle of cold outreach, platform demo, buyer decision and implementation cycle we saw at the height of the pandemic. In short, procurement cycles are more extended than ever and quick sales are GONE. Issues of security, data governance, and IT overload have extended procurement cycles and EdTech needs to adapt accordingly.
2. Platforms need to differentiate and properly integrate into the institutional ecosystem. Moreover, they must align with existing problems (e.g. student retention, data overload, assessment creation, etc.) or they may fall into procurement limbo — i.e. selling a solution without a targeted buyer.
3. On that note, while CTOs, CIOs, CSOs (and the rest of the C alphabet) play a key role in budgetary priorities, directors of functional areas (e.g. Innovation, Student Success, Curriculum, Recruiting, Alumni Engagement) have emerged as the final budgetary decision makers.

Data, Data, Data (How can we manage?):

1. With more EdTech, comes more data, such as enrollment rates, course completion, financial aid acceptance, and graduation progress. Even before the usage of LLMs became standard, institutions have been grappling with the problems of data overload, governance, security and privacy.
2. With more data collection, universities and stakeholders have growing concerns for data privacy. Emerging EdTech solutions must embed secure data collection and storage into their LMS’ and language learning models (LLM’s), if they are implementing AI for data analysis. Once platforms establish trust among their users, frictionless data collection will contribute to faster, more actionable insights, where Higher Ed leaders can enact meaningful solutions for their communities.

Student Support (How can we properly engage and measure outcomes?):

1. Learning loss, mental health issues, and disengagement are more evident than ever. According to a 2022 study, over 60% of university students cited symptoms that qualified them for mental health issues during the 2020–2021 school year. Students need customized support throughout their college journey, and retention is top of mind with universities under an enrollment crunch, as massive efforts are needed to correct an overall undergraduate enrollment decrease of 13.0% from the fall of 2019 to 2020.
2. In order to effectively personalize support, solutions must integrate with existing systems / workflows (i.e. digital + analog), surface students most in-need of support (based on data indicators), and streamline the connection of existing resources like mental health services, support groups, tutors, career counselors, and much more.

Need to Have vs. Nice to Have (And why reporting is critical!):

1. As economic uncertainty, declining perceived ROI from students/parents, and associated potential loss of tuition dollars loom on university’s minds (61% of universities have experienced decreases in tuition revenue), cost efficiency is top-of-mind. Simply put, sophistication of features or AI capabilities means very little when solutions struggle to report on ROI in student outcomes, learning workflows, and administrative improvements.
2. The “nice to have” vs. “need to have” argument is top of mind for budgetary decision-makers. Thus, platforms need to clearly articulate their value proposition and provide clear reporting features to show improved KPIs like categories like student wellness, time saved, test scores, graduation rates, and much more.

In summary, Higher Ed leaders nationwide are forecasting massive disruptions from emerging technology and, as a result, are seeking solutions that will enable their institutions to serve and guide students throughout these technological transitions. EdTech companies must address these headlining topics to earn the designation as partner of choice for universities and students, and understanding how each issue contributes to the next will ensure robust solutions across the industry.

References

1. Half of College Students Say Using AI on Schoolwork Is Cheating or Plagiarism

2. AI could replace equivalent of 300 million jobs — report

3. Trends in college student mental health and help-seeking by race/ethnicity: Findings from the national healthy minds study, 2013–2021

4. The Pandemic’s Impact on College Enrollment

5. Tuition Revenue Has Fallen at 61% of Colleges During the Pandemic