Expert Survey on AI-Based Adaptive E-Learning Framework for Jordanian Universities

200 faculty members from three public universities in Jordan responded to a 65-item questionnaire assessing a proposed AI-driven adaptive e-learning framework. The results indicate a high overall suitability score (M=3.18, SD=0.46) from the experts' perspective. The dataset, authored by Sanaa Banat and last updated in 2026, presents the survey instrument and findings.

Use Cases

• Validate a theoretical framework for adaptive e-learning based on constructivist, self-regulated, and connectivist learning theories.
• Analyze expert consensus on AI personalization mechanisms in education based on survey responses.
• Investigate demographic factors like gender and teaching experience on perceptions of educational technology suitability.
• Inform the development of adaptive e-learning environments for Jordanian universities based on expert feedback.

Strengths

• Questionnaire validity and reliability were verified and secured according to the description.
• Survey includes responses from 200 faculty members, a purposive sample from a population of 315.
• Results provide specific statistical measures, including mean (M=3.18) and standard deviation (SD=0.46).

Limitations

• The dataset is a 23.4 KB DOCX file; its scope is limited to the survey instrument and results, not raw response data.
• Column-level documentation is absent; field semantics must be inferred from the document.
• Data reflects the context of three public universities in Jordan, which may limit generalizability.

Provenance

Source

figshare, author Sanaa Banat.

Collection Method

Descriptive–analytical research method using a purposive sample survey.

Freshness

Last updated 2026-05-12 05:44:22.

Geography

Three public universities in Jordan.