Sapere Digital Literacy Program: Designing an Adult Training Program on AI & Digital Discernment

Audience: Parents (ages 25–45) and early-years/primary educators of children ages 2–8 Format: Blended adult training — 6 modules, 12 hours synchronous + 6–8 hours applied individual work Grounded in: DigComp 2.2 (EU Digital Competence Framework), Connected Learning, Andragogy (Knowles), Experiential Learning (Kolb) Note: A collaborative graduate project (4 co-authors, 25% individual contribution each) — needs analysis, program structure, and evaluation design were built as a team.


The Challenge

Public debate about children and technology almost always centers on screen time. The needs analysis behind this program argued that's the wrong target: children aged 2–8 don't yet have the cognitive capacity to critically evaluate what they see online — they learn by observing and imitating the adults around them. The real gap isn't how much technology children use, it's whether the parents and teachers mediating that exposure have the digital discernment to guide it. Sapere Learning Hub needed a program that shifted the conversation from restricting access to building adult judgment — critical thinking, media literacy, AI literacy, and digital safety — for an audience that already has basic digital skills but no systematic training in discernment.


The Learners

Two groups, addressed together rather than separately, because they share the same daily reality with the same child:


  • Parents (primary group):     ages 25–45, urban/peri-urban, tertiary-educated, already engaged in their child's development — pilot target of 50–100 participants
  • Educators (secondary group): early-years and primary teachers who interact with the same children daily — pilot target of 20–30 participants

Needs Analysis

Rather than assuming what adults needed, the team ran semi-structured interviews — one with a parent of a 5-year-old, one with a preschool teacher — probing daily technology use, a recent real incident involving their child and online content, what worried them most, what they'd already tried that hadn't worked, and what kind of support they'd actually want. Both independently asked for the same things: concrete examples, practical guides, strategies for setting limits, and interactive rather than purely theoretical sessions. That consistency across two independent interviews shaped the entire program design toward applicability over abstract theory.


The team also benchmarked existing programs (Common Sense Media, Parent Zone, Internet Matters) and found the same industry-wide shift: away from controlling technology access, toward building adult competence to guide it consciously.


Design Approach

The program is explicitly andragogical — Knowles' adult-learning principles (relevance, applicability, active involvement) and Kolb's experiential learning cycle both named directly as the pedagogical foundation, not just referenced loosely. This shows up structurally: every module pairs synchronous instruction with case studies, real-world scenarios, and hands-on tools, rather than lecture-only content.


Six modules, each 2 hours, sequenced from context to action:


  1. The digital environment children grow up in — where children encounter technology, risks and opportunities, the adult's role as digital mediator
  2. Algorithms, recommendations, and their influence on digital behavior — personalization, information bubbles
  3. Recognizing misinformation, manipulation, and AI-generated content — deepfakes, clickbait, source verification (using real tools: Deepware Scanner, Content Authenticity Initiative)
  4. Developing critical thinking in children — the questions that build reflection and curiosity
  5. Protecting children in the digital environment — data privacy, digital footprint, cyberbullying
  6. Building healthy digital habits at home — routines, limits, parental modeling


Between live modules, participants complete 6–8 hours of applied work: analyzing real online content, trying a strategy with their own child, and keeping a short reflection journal — building in spaced practice rather than concentrating everything into the live hours.


Evaluation — Full Kirkpatrick Model with Quantified Targets

  • Level 1 (Reaction): Feedback questionnaires and written reflections; success defined as 85%+ rating the program useful and relevant, 80%+ willing to recommend it.
  • Level 2 (Learning): Pre/post-test plus applied case studies; success defined as 70%+ improving their post-test result and 80%+ correctly identifying misinformation or AI-generated content in a test exercise.
  • Level 3 (Behavior): Follow-up questionnaires, self-assessments, and analysis of each participant's own "digital plan"; success defined as 60%+ implementing at least two strategies at home or in the classroom and regularly using a fact-checking method.
  • Level 4 (Results): Before/after comparison plus follow-up interviews, tracking growth in digital discernment and critical thinking, increased parent/educator confidence handling digital challenges, adoption of safer digital practices, and stronger family–school collaboration.


Post-program sustainability was built in deliberately: an ongoing practice community and periodically updated resources, since digital discernment isn't a one-time skill in a fast-changing information environment.


Outcome & Reflection

This project demonstrates something the other four case studies don't: a fully external-facing institutional training program, designed from scratch for a named (if illustrative) organization, with real needs-analysis interviews driving every subsequent design decision — not retrofitted evaluation criteria, but targets set before the program existed. It's also the clearest example in my portfolio of explicitly named adult-learning theory (Knowles, Kolb) shaping structure, not just informing it in the background.


As a collaborative project, this also reflects an ability to co-design under shared ownership — dividing needs analysis, structure, and evaluation design across a team while keeping the whole coherent. AI use (ChatGPT) is disclosed transparently in the original document, used for idea generation, literature search, and editing support, with the team retaining full responsibility for accuracy and final content.


If extended into a real pilot, the natural next step is exactly what the design anticipates: running the actual pre/post-test and follow-up interviews against the quantified targets already set, rather than treating evaluation as an afterthought.