The Mediating Impact of Student Engagement on the Association between Generative AI-Based Feedback and Academic Performance in Higher Education

This research assesses the student engagement mediating effect on the interaction between feedback offered through generative AI and academic performance in higher education. Understanding the impact of AI-generated feedback on student performance is increasingly important as technology is integrated into educational systems. Information was gathered using a structured questionnaire administered to 432 students, of which 311 provided usable responses. Hypothesized relationships were tested using regression analysis. Findings indicate that the use of AI-fed-back especially increases academic performance and student engagement has a mediating effect. Increased engagement due to prompt and tailored AI responses leads to improved motivation, enhanced learning, and academic performance. The active involvement of students during the application of AI systems into education is crucial as per the findings of this research. The study offers additional evidence paving the way for policy guidelines about the application of AI technology in education to support students’ performance. The study discusses the need to design AI feedback systems for active users emphasizing the role of generative AI on the higher education landscape.

Authors

Institutions

Publication Details

Journal
Journal of Research Innovation and Strategies for Education (RISE)
Published
2025-06-07
DOI
https://doi.org/10.70148/rise.18
Citations
2
Primary Topic
Online Learning and Analytics
Type
article
Field-Weighted Citation Impact
3.39
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

The Mediating Impact of Student Engagement on the Association between Generative AI-Based Feedback and Academic Performance in Higher Education

Abdul Qadeer
2 citations
Journal of Research Innovation and Strategies for Education (RISE)
Online Learning and Analytics
3.39
article

The Mediating Impact of Student Engagement on the Association between Generative AI-Based Feedback and Academic Performance in Higher Education

Abdul Qadeer
article en
2 citations

Abstract

This research assesses the student engagement mediating effect on the interaction between feedback offered through generative AI and academic performance in higher education. Understanding the impact of AI-generated feedback on student performance is increasingly important as technology is integrated into educational systems. Information was gathered using a structured questionnaire administered to 432 students, of which 311 provided usable responses. Hypothesized relationships were tested using regression analysis. Findings indicate that the use of AI-fed-back especially increases academic performance and student engagement has a mediating effect. Increased engagement due to prompt and tailored AI responses leads to improved motivation, enhanced learning, and academic performance. The active involvement of students during the application of AI systems into education is crucial as per the findings of this research. The study offers additional evidence paving the way for policy guidelines about the application of AI technology in education to support students’ performance. The study discusses the need to design AI feedback systems for active users emphasizing the role of generative AI on the higher education landscape.

Journal of Research Innovation and Strategies for Education (RISE)Vol. 2(1)
Shaheed Benazir Bhutto University (PK)
Openalex Percentile: Top 7%
Online Learning and Analytics
3.39
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.