Changes in homework submission patterns with the advent of AI tools: a high school perspective

The unprecedented availability of generative AI tools like ChatGPT has understandably raised some fundamental questions as to what that means for educational practices, let alone homework submission behaviors. This study investigates changes in high school students' homework completion patterns before and after the release of AI tools by analyzing data collected from multiple schools. Through questionnaires and interviews from students and instructors, we will be able to find out the changes in frequency, punctuality, and quality of homework submitted and what proportion of assignments students are completing with AI. Our results show that, although the overall volume of homework submitted remained unchanged, significant changes occurred regarding how students approached their work, such as an increasing reliance on AI instruments for generating ideas, editing content, and solving problems. The study also reveals mixed perceptions on the part of both students and educators when it comes to using AI in a morally appropriate way while performing academic work. These insights are likely to underpin the need for revised education policies and further AI literacy programs that ensure responsible and effective use of AI in learning, and recommendations are provided for educators on how homework practices might be refitted within this evolving technological landscape.

Authors

Publication Details

Journal
STUDIES IN EDUCATION SCIENCES
Published
2024-11-06
DOI
https://doi.org/10.54019/sesv5n4-001
Citations
1
Primary Topic
Teaching and Learning Programming
Type
article
Field-Weighted Citation Impact
0.41
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Changes in homework submission patterns with the advent of AI tools: a high school perspective

Mustapha Bouakkaz, Houda Boumediene
1 citations
STUDIES IN EDUCATION SCIENCES
Teaching and Learning Programming
0.41
article

Changes in homework submission patterns with the advent of AI tools: a high school perspective

Mustapha Bouakkaz, Houda Boumediene
article en
1 citations

Abstract

The unprecedented availability of generative AI tools like ChatGPT has understandably raised some fundamental questions as to what that means for educational practices, let alone homework submission behaviors. This study investigates changes in high school students' homework completion patterns before and after the release of AI tools by analyzing data collected from multiple schools. Through questionnaires and interviews from students and instructors, we will be able to find out the changes in frequency, punctuality, and quality of homework submitted and what proportion of assignments students are completing with AI. Our results show that, although the overall volume of homework submitted remained unchanged, significant changes occurred regarding how students approached their work, such as an increasing reliance on AI instruments for generating ideas, editing content, and solving problems. The study also reveals mixed perceptions on the part of both students and educators when it comes to using AI in a morally appropriate way while performing academic work. These insights are likely to underpin the need for revised education policies and further AI literacy programs that ensure responsible and effective use of AI in learning, and recommendations are provided for educators on how homework practices might be refitted within this evolving technological landscape.

STUDIES IN EDUCATION SCIENCESVol. 5(4)
Openalex Percentile: Top 25%
Teaching and Learning Programming
0.41
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.