Taxonomy of AI-Driven Micro-Educational Startups in Primary Education: An Analysis of 120 Digital Lean Canvases

Hossein Talebzadeh

No um9kg_v1, EdArXiv from Center for Open Science

Abstract: The aim of the present study is to develop a taxonomy and pedagogical evaluation of AI-driven micro-educational startups in primary education, based on the analysis of 120 digital business model canvases. This research was conducted using a qualitative approach and the directed content analysis method. The research population comprised all business model canvases generated by 120 female elementary-level student-teachers within the framework of a digital micro-entrepreneurship workshop, developed under the supervision of generative artificial intelligence. For data analysis, open, axial, and selective coding procedures were employed. The findings revealed that micro-educational startups can be categorized into five taxonomic levels: (1) digital educational content production (51.6%), (2) online educational services (23.3%), (3) interactive educational tool production (11.7%), (4) educational consulting and planning (8.3%), and (5) hybrid/multidimensional startups (5%). The dominant value propositions included time-saving (78%), enhanced learning appeal (65%), and personalized education (42%). Furthermore, pedagogical evaluation indicated that the process of designing and developing business models under AI supervision successfully transformed 92% of student-teachers' perspectives from "teacher as consumer" to "teacher as value-creator," while also enhancing their financial resilience in the face of inflation. By proposing the theory of "AI-Augmented Entrepreneurship" and a five-level taxonomy, this research demonstrates that digital micro-entrepreneurship supported by generative AI can serve as an effective pedagogical strategy for economically empowering future teachers and contributing to the development of the educational entrepreneurship ecosystem.

Date: 2026-07-26
References: Add references at CitEc
Citations:

Downloads: (external link)
https://osf.io/download/6a67ba325b44de6240135b9b/

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:osf:edarxi:um9kg_v1

DOI: 10.31219/osf.io/um9kg_v1

Access Statistics for this paper

More papers in EdArXiv from Center for Open Science
Bibliographic data for series maintained by OSF ().