GenAI-LA-LD Survey Dataset – Educator Perceptions on Integration of Generative AI, Learning Analytics, and Learning Design

Overview:
This dataset was collected as part of a quantitative survey-based study conducted in higher education institutions in the UAE and India. The study explores educators' and instructional designers’ perceptions of integrating Generative AI (GenAI), Learning Analytics (LA), and Learning Design (LD) to inform educational decision-making and instructional improvement.

Purpose:
To validate the GenAI-LA-LD Synergy Framework and identify key predictors of adoption intent, usability, and institutional readiness for integrating GenAI, LA, and LD in higher education pedagogy.

📋 Data Collection
Method: Online structured survey

Participants: 312 respondents (university faculty and instructional designers)

Countries: United Arab Emirates and India

Timeframe: March–May 2025

Format: CSV/Excel compatible with Jamovi, SPSS, and R

📁 Variables Included
Variable Name Description Type Scale
Respondent_ID Unique anonymized ID per respondent Categorical Nominal
Country Country of respondent (UAE or India) Categorical Nominal
DigitalConfidence Confidence in using digital tools Numeric 1–5 Likert
InstitutionalTraining Perception of institutional training and support Numeric 1–5 Likert
PedagogicalAlignment Degree of alignment between GenAI/LA tools and pedagogy Numeric 1–5 Likert
PerceivedUsefulness Usefulness of GenAI and LA tools for instruction Numeric 1–5 Likert
EaseOfIntegration Ease of integrating these tools in teaching workflows Numeric 1–5 Likert
EthicalConcerns Concern about ethics, privacy, and responsible AI use Numeric 1–5 Likert
IntentionToAdopt Likelihood of adopting GenAI-LA-LD integration in future practices Numeric 1–5 Likert

📊 Use Cases for Analysis
Descriptive statistics and visualization

Reliability analysis (Cronbach’s alpha)

Correlation and multiple regression

One-way ANOVA for country-based comparisons

Factor analysis and validation of the GenAI-LA-LD Synergy model

🧪 Potential Applications
Policy research on AI in education

Learning design innovation tracking

Faculty development and digital readiness assessment

Comparative studies between countries or institutions