---
title: "Learning About AI: A Systematic Review of Reviews on AI Literacy"
authors:
  - Shan Zhang
  - Priyadharshini Ganapathy Prasad
  - Noah L. Schroeder
year: 2024
type: journalArticle
doi: 10.1177/07356331251342081
url: "https://journals.sagepub.com/doi/10.1177/07356331251342081"
tags:
  - paper
llm_decision: Exclude
llm_confidence: 0.95
llm_categories:
  - AI_Literacies
human_decision: Exclude
human_categories:
  - AI_Literacies
  - KI_Sonstige
agreement: agree
---

# Learning About AI: A Systematic Review of Reviews on AI Literacy

## Transformation Trail

### Stufe 1: Extraktion & Klassifikation (LLM)

**Extrahierte Kategorien:** AI_Literacies, KI_Sonstige, Bias_Ungleichheit, Diversitaet, Fairness
**Argumente:** 3 extrahiert

### Stufe 3: Verifikation (LLM)

| Metrik | Score |
|--------|-------|
| Completeness | 88 |
| Correctness | 92 |
| Category Validation | 87 |
| **Overall Confidence** | **89** |

### Stufe 4: Assessment

**LLM:** Exclude (Confidence: 0.95)
**Human:** Exclude

## Key Concepts

- [[AI Competency Framework]]
- [[AI Literacy]]
- [[Algorithmic Fairness]]
- [[Ethical AI Integration]]

## Wissensdokument

# Learning About AI: A Systematic Review of Reviews on AI Literacy

## Kernbefund

The field shows consensus on AI literacy definition and availability of effective teaching tools and pedagogical approaches, but significant gaps remain in standardized assessment, integration of ethical considerations, interdisciplinary approaches, and policy guidance.

## Forschungsfrage

What is AI literacy and how should learners develop AI literacy competencies across different educational levels?

## Methodik

Umbrella Review / Systematic Review of Systematic Reviews: Critical examination of 17 reviews focusing on AI literacy, teaching and learning of AI concepts, and AI education across educational levels
**Datenbasis:** 17 systematic reviews and scoping reviews analyzed; synthesis of literature on AI literacy frameworks, curricula, assessment instruments, and pedagogical approaches

## Hauptargumente

- AI literacy, defined as a set of competencies enabling individuals to critically evaluate, communicate with, and ethically use AI technologies, is increasingly essential for all learners from preschool through higher education and professional development.
- Multiple effective pedagogical approaches exist including project-based learning, problem-based learning, collaborative projects, and gamification, with freely accessible tools enabling AI learning without prior programming experience.
- Critical gaps persist in standardized, theoretically-grounded, content-validated assessments; integration of ethical considerations and policy guidance; interdisciplinary curriculum approaches; and research on undergraduate and graduate AI literacy instruction.

## Kategorie-Evidenz

### Evidenz 1

Core focus: 'to empower students to become creators, designers, and producers of AI technologies' and 'acquire knowledge and skills about AI concepts and methods to identify AI-powered tools and platforms.' Comprehensive framework analysis of six AI literacy constructs: 'recognize, know & understand, use & apply, evaluate, create, and navigate ethically.'

### Evidenz 2

Covers machine learning concepts, algorithms, object detection, speech synthesis, computer vision, predictive systems: 'freely accessible online tools that help students learn AI concepts without prior programming experience, particularly in areas such as machine learning, recognition, object detection, and speech synthesis.'

### Evidenz 3

Addresses digital divide and equitable access: 'democratized access to AI education' and 'the emergence of tools that allow students to learn AI without prior programming experience has democratized access to AI education.' Discussion of structured curriculum absence in K-12: 'the absence of structured AI literacy curricula in K12 education, underscoring the necessity of educational reforms that prioritize AI literacy from a young age.'

### Evidenz 4

Discusses diverse learner populations and educational contexts: 'integration of AI literacy across different educational contexts, from early childhood to professional education' and tailoring to 'different user groups—ranging from students to professionals—with each having distinct AI literacy needs.' Recognition of cultural differences: 'the development of standardized, theoretically-based, content-validated, and reliable assessments across different educational levels and cultures is needed.'

### Evidenz 5

Addresses algorithmic fairness and ethical AI evaluation: 'critically evaluating the influence of AI tools on everyday life, personal learning gains, and social impact' and integration of 'ethical considerations in AI education' including 'considerations for student privacy and algorithmic fairness.' Framework component: 'navigate ethically' as core AI literacy construct.

## Assessment-Relevanz

**Domain Fit:** This paper is highly relevant for understanding AI literacy development across educational systems but has limited direct connection to social work practice and pedagogy. The focus on general K-12 and higher education does not specifically address how social workers or social work clients engage with AI systems.

**Unique Contribution:** The umbrella review provides the first comprehensive synthesis of diverse AI literacy frameworks and empirical findings, establishing consensus on definitions while mapping critical research gaps in standardized assessment, ethical integration, and policy alignment.

**Limitations:** The review focuses on formal educational contexts and does not address AI literacy needs in professional social work practice, informal learning, or vulnerable populations who may be disproportionately affected by AI systems; limited discussion of how AI literacy connects to addressing algorithmic discrimination in social services.

**Target Group:** K-12 educators, higher education faculty, curriculum developers, educational policymakers, AI education researchers, and educational technologists. Secondarily relevant for social workers and social service administrators concerned with client engagement and digital equity in their practice contexts.

## Schlüsselreferenzen

- [[Long_Magerko_2020]] - Comprehensive AI literacy framework with five core topics and 17 competencies
- [[Ng_et_al_2021]] - AI literacy as 21st-century skill grounded in Bloom's Taxonomy and TPACK
- [[Kong_et_al_2024]] - Multidimensional AI literacy framework (cognitive, metacognitive, emotional, social dimensions)
- [[Pinski_Benlian_2024]] - Holistic AI literacy framework with Proficiency Dimensions and Subject Areas
- [[Chiu_et_al_2024]] - K-12 AI education model with five components (technology, impact, ethics, collaboration, self-reflection)
- [[UNESCO_2022]] - K-12 AI curricula mapping of government-endorsed curricula
- [[Touretzky_et_al_2019]] - AI4K12 Five Big Ideas framework for national curriculum standards
- [[Ng_et_al_2023]] - Systematic review of AI teaching and learning from 2000-2020
- [[Yue_et_al_2022]] - Pedagogical design of K-12 AI education systematic review
- [[Lintner_2024]] - Synthesis of 22 studies validating 16 AI literacy scales across populations
