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Co-designing AI-enhanced careers education: a design-based approach to sustainable career ecosystems

Jason Zagami

Education and Training2026https://doi.org/10.1108/et-05-2025-0286article
AJG 1
Weight
0.50

What the paper says

Purpose This paper reports the first cycle of a design-based research (DBR) project exploring how generative artificial intelligence (AI) can support the participatory design of educational programmes preparing students for future work. Rather than focussing narrowly on career education, it examines AI as a catalyst for reimagining curriculum co-design that integrates stakeholder expertise, ethics, and pedagogy. The study produces the Sustainable AI Career Ecosystem Model (SAICEM), conceptualising AI as a socio-technical actor within educational ecosystems. Design/methodology/approach Using a DBR methodology, teachers, industry partners and academic experts collaboratively designed a short course addressing emerging skills and ethical challenges. Cycle 1 focused on co-design, documenting collaboration and conceptual model development. Implementation and evaluation are planned for later cycles. Findings The co-design process demonstrated that AI could act as both design partner and pedagogical actor. Tensions emerged between automation and teacher agency, curriculum legitimacy and ethics of representation. SAICEM synthesises these insights into principles for inclusive, transparent and sustainable AI integration. Practical implications As Cycle 1 of a multi-phase project, this study establishes design principles for AI-enabled curricula that balance innovation, professional agency and ethical responsibility, supporting equitable and future-ready education. Social implications The study promotes equitable and ethical AI education. Originality/value SAICEM contributes a methodological and conceptual framework for participatory AI design that extends beyond career education to general educational innovation. It advances understanding of AI as a socio-technical actor while offering practical guidance for educators, policymakers and industry.

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https://doi.org/https://doi.org/10.1108/et-05-2025-0286

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@article{jason2026,
  title        = {{Co-designing AI-enhanced careers education: a design-based approach to sustainable career ecosystems}},
  author       = {Jason Zagami},
  journal      = {Education and Training},
  year         = {2026},
  doi          = {https://doi.org/https://doi.org/10.1108/et-05-2025-0286},
}

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Co-designing AI-enhanced careers education: a design-based approach to sustainable career ecosystems

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Evidence weight

0.50

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.50 × 0.4 = 0.20
M · momentum0.50 × 0.15 = 0.07
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.