Case ReportPublished quantitative study2024
AAB-CASE-2025-RV-056
Investigating pre-service teachers’ artificial intelligence perception from the perspective of planned behavior theory
CAEAI; UEF Finland / UJ South Africa / KSU Nigeria; large-scale SEM.
This page documents an AI literacy or AI education case for registry purposes. It is descriptive and does not imply AAB endorsement of any specific tool, provider, or intervention.
01
Implementation
Multi-university
02
Learning context
Private program
03
AI role
Tutor
04
Outcome signal
Attitudes
Registry Facets
0
Education Level
- Higher education
Subject Area
- Teacher professional development
- AI literacy
Use Case Type
- Survey research
Stakeholder Group
- Teachers
AI Capability Type
- Foundational AI concepts
Implementation Model
- System-level guidance
Evidence Type
- Post assessment
Outcomes Domain
- Attitudes
- Intentions
Implementing Organization
1
Organization Type
Multi-university
Location
Nigeria (survey site) + international authors
Primary Facilitator Role
Researchers
Learning Context
2
Setting Type
- Private program
Session Format
Validated questionnaire + SEM
Duration
Cross-sectional
Group Size
796 pre-service teachers
Devices
N/A
Constraints
- National higher-ed context
- Self-report
Learner Profile
3
Age Range
Pre-service teachers
Prior AI Exposure Assumed
Heterogeneous
Prior Programming Background Assumed
Varies
Educational Intent
4
Primary Learning Goals
- Model determinants of intention to learn AI
Secondary Learning Goals
- Guide design of AI teacher education programs
What This Was Not
- Not classroom implementation study
AI Tool Description
5
Tool Type
N/A (beliefs and intentions toward learning AI)
AI Role
- Tutor
Languages
Nigeria
User Interaction Model
Safeguards
- Address anxiety and norms in program design
Activity Design
6
Activity Flow
- Survey
- SEM path analysis
Human Vs AI Responsibilities
Scaffolding Strategies
- Emphasize basic AI knowledge and subjective norms in curricula
Observed Challenges
7
Educators Reported
- Some TPB extension paths unsupported (per abstract)
- Need programs that convert intention to actual learning behavior
Design Adaptations
8
Adaptations
- Large-N evidence for program design priorities in LMIC context
Reported Outcomes
9
Engagement
Learning Signals
- ~79% variance explained in intention; key predictors identified
Educators Reflection
Actionable levers for AI in teacher preparation policy.
Ethical & Privacy Considerations
10
Privacy
- Survey ethics
- Institutional permissions
Evidence Type
11
Evidence
- Post assessment
- Practitioner observation
Relevance to Research
12
Potential Research Use
- Longitudinal behavior follow-up
- Intervention experiments
Relevant Research Domains
- Theory of planned behavior
- Teacher AI education
Case Status
13
Case Status
- Completed
AAB Classification Tags
14
Age
Pre-service adults
Setting
Nigeria universities
AI Function
Intent to learn AI
Pedagogy
SEM survey
Risk Level
Low
Data Sensitivity
Medium
Registry Metadata
15
Case ID
AAB-CASE-2025-RV-056
Publication Status
Published quantitative study
Tags
caseHigher educationNigeria (survey site) + international authorsSystem-level guidanceFoundational AI conceptsTeacher professional developmentAI literacySurvey research