Boston | 17 April 2025 | 5 tables, 40+ edtech executives, AI experts, educators and researchers
| Theme | What we heard | Primary table(s) | Echoed by… |
|---|---|---|---|
| 1. "More questions than answers." | The sector is still defining the problem-statements, not just the solutions. | Strategy & ROI | Overflow, Skills |
| 2. Human connection is non-negotiable. | AI will automate content & assessment, but community, coaching and empathy are the moat. | Strategy | Burnout, Skills, ROI |
| 3. Competitive compulsion to ship AI (ready or not). | "We can't not do it." Fear of being leap-frogged trumps a tidy business case. | ROI | Strategy, Overflow |
| 4. A widening adoption gap. | Early adopters drive the narrative; late movers feel alienated. We risk designing for the 5% and leaving the 95% behind. | Skills | Strategy, Burnout |
| 5. Burnout at every stage of the curve. | Early adopters drown in tool churn; mainstream teams scramble to stay relevant; skeptics feel the ground shifting beneath them. | Burnout | All tables |
The EdTech AI Roundtable brought together leaders from education, technology, and business to explore the complex intersection of artificial intelligence and education. Through structured discussions organized around four distinct lenses—Strategy, Skills, ROI, and Burnout—participants engaged in candid conversation under the Chatham House Rule.
What emerged was not a clear roadmap, but rather a sophisticated understanding of the current landscape: one characterized by rapid change, competing priorities, and profound questions about the future of learning. This analysis captures the key insights and tensions revealed during our discussions.
Across all discussion tables, participants consistently acknowledged that we are still in the early stages of understanding AI's full impact on education.
Table 1's Strategy discussion revealed fundamental questions about the timeline of AI evolution: "What is the world in x years—then what do we teach them?" Participants wrestled with questions of control ("Should students orchestrate their own learning?") and wondered about the appropriate level of human oversight as AI becomes more capable.
The Skills lens (Table 2) raised equally challenging questions about which capabilities would remain distinctively human versus which might be delegated to technology. Meanwhile, the ROI discussions (Table 3) questioned long-held assumptions about competitive advantage: "Is data a moat during AI?" with some participants suggesting that "IP advantage is a myth."
This uncertainty creates both challenge and opportunity—a space for innovation tempered by the responsibility to move thoughtfully.
Perhaps the most consistent theme across all discussions was the irreplaceable value of human connection in education.
As Table 1 noted simply but powerfully: "Humans need connection." Participants expressed concern about learning in isolated "bubbles" without challenge, drawing parallels to social media echo chambers. Several tables emphasized that while AI may automate certain tasks, it should ultimately serve to enhance human connection rather than replace it.
The Strategy discussions emphasized that "face-to-face education" remains critically important, with educators increasingly becoming "facilitators rather than one-way speakers." This represents not a diminishment of the teaching role, but an evolution toward more meaningful interaction.
From the Skills perspective, "social/emotional learning," "collaboration," and "working in teams" were identified as increasingly important. These insights suggest that as AI handles more routine tasks, distinctly human capabilities—empathy, connection, mentorship—become more valuable, not less.
The tension between thoughtful implementation and competitive necessity emerged strongly in discussions.
Table 3's ROI lens captured this dynamic most directly, with participants debating whether to "buy vs. rent" AI capabilities and how to "use AI as a way to disrupt yourself" before others do it to you. Many organizations reported feeling they had "no other option" but to implement AI features, even when uncertain about their long-term value.
This pressure creates cascading challenges across other domains. The Strategy discussions noted a "technology arms race" and "pace of innovation" as primary concerns. Meanwhile, the Burnout lens highlighted how this relentless pressure affects workforce wellbeing.
Particularly telling was the observation that "many organizations don't want to pay extra for AI features"—suggesting that AI capabilities are rapidly becoming table stakes rather than premium offerings in educational technology.
Participants recognized a wide variation in AI literacy, adoption rates, and philosophical stances toward the technology.
Table 1 captured this through discussions of potential market divergence: some stakeholders "spending intentionally on no screen time" and even the possibility that some might "pay to opt out of AI education." These observations suggest that educational institutions and companies may need to serve multiple models simultaneously.
The Skills discussions (Table 2) emphasized "technological resilience" and "adaptive mentality" as critical capabilities precisely because of this uneven landscape. Institutions must prepare both those who will be AI power users and those who remain skeptical or have limited access.
This spectrum creates challenges for unified strategy but also opportunities for differentiation in the market.
The psychological impact of rapid technological change emerged as a significant concern, though experienced differently across the adoption spectrum.
Early adopters reported feeling overwhelmed by constant change and the pressure to stay current. Meanwhile, those in mainstream adoption phases struggled with the need to continuously update skills and products. Perhaps most poignantly, AI skeptics expressed a sense that "the world is careening off a cliff."
These varying experiences of technological stress suggest that supporting mental health and wellbeing requires tailored approaches rather than one-size-fits-all solutions.
Several strategic imperatives emerged across multiple discussion lenses:
Redefining Educational Purpose: As AI capabilities expand, educational institutions must clarify which aspects of learning remain uniquely human-centered and which can be enhanced through technology.
Skills Reorientation: Critical thinking, creativity, collaboration, and ethical judgment consistently emerged as capabilities increasing in importance, while routine information processing diminishes.
Policy Leadership: Multiple tables emphasized that "policy should be leading" AI progress, learning from lessons of previous technological shifts like social media.
Ethical Frameworks: Questions of bias, data ownership, and AI governance appeared across all discussion lenses, suggesting the need for stronger ethical frameworks.
Sustainable Innovation: Organizations must balance competitive pressure for rapid implementation against the human capacity for change and the need for thoughtful integration.
The EdTech AI Roundtable revealed a sector in transition—grappling with profound questions about purpose, method, and value in an age of artificial intelligence. While technological evolution creates pressure for rapid change, the most consistent insight from our discussions was paradoxically timeless: the centrality of human connection in meaningful education.
As one participant eloquently stated: "Technology should serve us, not replace or take over." This principle may serve as the North Star for navigating the complex path ahead.
The event reaffirmed that AI in education is less a technology problem and more a leadership choreography. Our collective mandate: keep humans at the center while we write the playbook—because the second draft will be authored by the very learners we serve.
This summary was compiled based on table discussions under the Chatham House Rule. Specific attributions have been omitted to preserve anonymity.