In the AI era, the battle of EdTech is once again 'education'

The Commoditization Curve

The previous investment cycle treated AI integration as a proxy for product quality. During the generative AI surge, platforms positioned as "AI-based education" attracted capital on the assumption that algorithmic sophistication would translate to retention and outcomes. Venturesquare's reporting indicates that this assumption has eroded: the market now interrogates how much learning effectiveness has demonstrably improved, what gains in student achievement and engagement have been documented, and whether those gains can sustain a repeatable business model. Samil PwC Management Research, cited in the report, predicts the EdTech sector will reorganize around educational outcomes and operational efficiency rather than technology stack superiority. The hardware layer is moving in parallel—Samsung's reported launch of AI-powered educational tools for displays signals that infrastructure vendors are consolidating around the same pedagogical premises, which compresses the differentiation window further for software-only platforms.

The Pedagogical Ground Truth

At the education session of the June 2026 Jeju Forum, OECD Director General for Education Andreas Schleicher stated that "students must learn how to think before they learn prompts," framing generative AI as a tool to expand cognitive capacity rather than a substitute for instructional design. At the 13th The Forum, Kim Nam-ju of Gachon University's Startup College argued that university education should be architected around unique human experiences and problem-solving capacity rather than knowledge transmission. Both positions converge on a single operational claim: cognitive load management, scaffolding quality, and retention rate mechanics now determine product value more directly than the underlying model architecture. The gamification loop, the adaptive sequencing of difficulty, and the latency of corrective feedback become the variables worth measuring.

Selection Criteria Under the New Standard

For parents, educators, and curriculum designers evaluating learning apps in this environment, the practical filter has narrowed to three verifiable inputs. First, demand the outcome data: pre/post assessments, retention intervals measured in weeks rather than sessions, and engagement duration metrics are the new minimum disclosure. Second, examine the scaffolding structure—how the system sequences concepts, how it handles error states, and whether its metacognitive prompts transfer reasoning patterns rather than reward correct guesses. Third, track repeatability indicators: cohort retention across curriculum arcs, session return rates after novelty decay, and cross-skill transfer evidence. Platforms that cannot furnish these metrics should be treated as unverified, regardless of the AI branding attached to their product pages. The headline question has inverted: it is no longer "does this platform use AI" but "does this platform produce measurable, repeatable learning gains that justify its retention cost."