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Two-day intensive training agenda sequenced to move learners from Stage 0 through Stage 4 transitions

Design · All designs
Evidence · 1 claim (1 for) · 1 study (1 design), q2 · 0 of 1 report an effect size · 1 claim rests on one study

Description

Table 5 maps each activity of the 2-day hands-on AI/LLM training to the stage transition it facilitates: demystification of platforms moves Stage 0 to 1; hands-on API use, local model deployment, and RAG-based verification move Stage 1 to 2; tool evaluation, fine-tuning concepts, and bias detection move Stage 2 to 3; advanced fine-tuning, auto-coding agents, and building a small language model from scratch move Stage 3 to 4. The article reports this as "a practical pathway from Stage 0 (Not Yet Engaged) through Stage 4 (Improvement) within an intensive 2-day format."

Design Implications

Context

Requirements

  • Well-structured sequencing of activities aligned to intended stage transitions

Constraints

  • Evidence for resulting stage movement is observational and practice-based, not from validated pre/post measurement

Target Learners

  • university students and staff
  • graduate students
  • high school and college students

Learning Goals

  • moving from AI avoidance to informed use
  • verification of AI outputs
  • AI system improvement skills

Claims

Examples

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Key Sources

  • Liu, J. P., & Levy, R. (2026). Beyond Tool Adoption: A Practical Five-Stage Developmental Continuum for AI Literacy in Higher Education. North Carolina State University. doi:10.48550/arxiv.2606.00038
Page metadata
Field Value
type design
id two-day-ai-training-stage-transition-agenda
title Two-day intensive training agenda sequenced to move learners from Stage 0 through Stage 4 transitions
description Table 5 maps each activity of the 2-day hands-on AI/LLM training to the stage transition it facilitates: demystification of platforms moves Stage 0 to 1; hands-on API use, local model deployment, and RAG-based verific...
status review
generated by process:settle-candidates on 2026-10-10
trust unverified — no one has confirmed this page's content
sources 1 — liu-2026

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