A 24-component consensus framework ranks hands-on AI practice and curricular linkage as the top priorities for extracurricular AI literacy, accounting for 57.2% of evaluative weight; the blueprint, validated via Delphi and AHP, records a content-validity index of 0.84 and is proposed as scalable for low-resource settings.

Main Finding

A consensus framework of 24 components across seven domains was developed and empirically validated to guide curriculum-linked AI/data extracurricular programs. Using Delphi + Analytic Hierarchy Process (AHP), the study found that AI practice integration and curricular linkage together account for 57.2% of the program-evaluation weight. Three program types—AI tool workshops, AI-based problem-solving challenges, and data analytics projects—display distinct strategic priorities. The final framework achieved a content validity index of 0.84, suggesting it is a reliable, scalable design blueprint for sustainable AI literacy education, including in resource-constrained settings.

Key Points

• Method: Sequential mixed-methods integrating Delphi technique (2 rounds) and AHP pairwise comparisons, with content validity assessment (CVR/CVI).
• Participants: 10 domain experts (AI education, curriculum development, extracurricular administration, industry).
• Outcome: Consensus framework with 24 components organized into seven domains. Six domains were used as AHP Level 1 evaluation criteria; the seventh domain (program type) served as Level 2 alternatives.
• Weighting: AI practice integration + curricular linkage = 57.2% of total criterion weight (highest joint priority).
• Program types evaluated: AI tool workshops, AI-based problem-solving challenges, data analytics projects—each shows different strategic emphasis when weighted against criteria.
• Validation: Content validity index = 0.84, indicating good content validity.
• Applicability: Framework intended to inform sustainable AI literacy programming and is potentially applicable in low-resource environments.

Data & Methods

• Design: Sequential mixed-methods
  • Delphi rounds: Two iterative rounds to generate and refine components and reach expert consensus.
  • Analytic Hierarchy Process (AHP): Pairwise comparisons among Level 1 criteria (6 domains) and Level 2 alternatives (program types) to produce priority weights.
  • Content Validity: Content Validity Ratio (CVR) and Content Validity Index (CVI) used to quantify expert agreement on items; overall CVI reported as 0.84.
• Sample: 10 experts selected for heterogeneity across relevant fields (education, curriculum, extracurricular management, industry).
• Outputs: 24 discrete curricular/extracurricular components; quantitative weights for domains and program type priorities.
• Limitations to note: Small expert panel (n=10) may constrain generalizability; framework reflects expert judgment and weighting rather than experimentally measured program impacts.

Implications for AI Economics

• Human capital formation
  • Emphasizing AI practice integration and curricular linkage implies these are the highest-return components for building applied AI skills—relevant for estimates of returns to education and skill-specific productivity.
  • Extracurricular programs that are closely tied to curriculum may improve the match between supply of AI skills and labor-market demand.
• Policy and investment prioritization
  • Resource allocation should prioritize program designs that integrate hands-on AI practice and formal curricular connections, since these carry most of the evaluative weight.
  • For low-resource settings, the validated framework can guide cost-effective program design (e.g., prioritizing workshops or project formats that maximize practical skill uptake).
• Program targeting and labor-market outcomes
  • Different program types imply different pathways to labor-market outcomes: short workshops may boost tool familiarity (lower-cost, broad reach), challenges may enhance problem-solving and signaling, and analytics projects may develop deeper applied competencies that are more directly productive.
  • Economists should measure heterogeneity in wage and employment returns by program type, controlling for participant selection.
• Evaluation and scaling
  • Next research steps: randomized or quasi-experimental evaluation of program variants to estimate causal impacts on skills, employability, earnings, and innovation outcomes.
  • Cost-effectiveness analyses should combine AHP-informed prioritization with empirical impact estimates to guide scaling decisions.
• Complementarities and spillovers
  • Investigate complementarities between extracurricular AI programs and formal education, as well as spillovers across peers, firms, and local innovation ecosystems.
  • Consider distributional effects: whether prioritized program designs reduce or exacerbate inequalities in AI skill access.
• Measurement recommendations for researchers/policymakers
  • Track short- and medium-term outcomes: skill test scores, project portfolios, employment outcomes, wages, job-matching quality, and employer satisfaction.
  • Collect cost data to compute cost-per-skill-gained and cost-per-employment outcome for each program type.

Brief takeaway: For policymakers and economists concerned with AI-driven human capital and labor-market impacts, this validated framework identifies practical curricular linkages and hands-on practice as highest priorities and offers a structured basis for designing, costing, and empirically evaluating extracurricular AI literacy programs.