No Cover Image

Journal article 104 views 13 downloads

Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education

Ryan J. Brunton Orcid Logo, Soukaina Rhazzafe Orcid Logo, Raymond Moodley Orcid Logo, Stefan Kuhn Orcid Logo, Fabio Caraffini Orcid Logo, Sara Wilford Orcid Logo, Rachel Higginbottom Orcid Logo, Simon Colreavy-Donnelly Orcid Logo, Mario Gongora Orcid Logo

Social Sciences & Humanities Open, Volume: 12, Start page: 102110

Swansea University Author: Fabio Caraffini Orcid Logo

  • 1-s2.0-S259029112500840X-main.pdf

    PDF | Version of Record

    © 2025 The Author(s). This is an open access article under the CC BY license.

    Download (2.47MB)

Check full text

DOI (Published version): 10.1016/j.ssaho.2025.102110

Abstract

We show that generative AI can support disadvantaged students, improve grades, and help close the attainment gap between pupil premium (PP) and students with special education needs (SEN). It can also alleviate teacher workload, especially for PP and SEN students, by minimising marking and feedback...

Full description

Published in: Social Sciences & Humanities Open
ISSN: 2590-2911
Published: Elsevier BV 2025
Online Access: Check full text

URI: https://cronfa.swan.ac.uk/Record/cronfa70809
first_indexed 2025-11-01T07:06:41Z
last_indexed 2025-11-21T09:53:20Z
id cronfa70809
recordtype SURis
fullrecord <?xml version="1.0"?><rfc1807><datestamp>2025-11-19T10:45:08.7629234</datestamp><bib-version>v2</bib-version><id>70809</id><entry>2025-10-31</entry><title>Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education</title><swanseaauthors><author><sid>d0b8d4e63d512d4d67a02a23dd20dfdb</sid><ORCID>0000-0001-9199-7368</ORCID><firstname>Fabio</firstname><surname>Caraffini</surname><name>Fabio Caraffini</name><active>true</active><ethesisStudent>false</ethesisStudent></author></swanseaauthors><date>2025-10-31</date><deptcode>MACS</deptcode><abstract>We show that generative AI can support disadvantaged students, improve grades, and help close the attainment gap between pupil premium (PP) and students with special education needs (SEN). It can also alleviate teacher workload, especially for PP and SEN students, by minimising marking and feedback time, enabling better lesson planning and interventions, which can enhance teacher retention and staffing. We focus on disadvantaged students with SEN and low-income families and use AI for personalised feedback and lesson planning in arts and humanities. This enables school leaders and parents to view the qualitative and quantitative student progress. The results of this study demonstrate the potential of using AI-based systems to help close the attainment gap between disadvantaged students and their peers. The intervention given to these pupils would have been an unreasonable demand on the current teacher workload in the UK.</abstract><type>Journal Article</type><journal>Social Sciences &amp;amp; Humanities Open</journal><volume>12</volume><journalNumber/><paginationStart>102110</paginationStart><paginationEnd/><publisher>Elsevier BV</publisher><placeOfPublication/><isbnPrint/><isbnElectronic/><issnPrint>2590-2911</issnPrint><issnElectronic/><keywords>Disadvantaged pupils; Pupil premium; Feedback; Education; Large language models; Artificial intelligence; Generative AI</keywords><publishedDay>29</publishedDay><publishedMonth>10</publishedMonth><publishedYear>2025</publishedYear><publishedDate>2025-10-29</publishedDate><doi>10.1016/j.ssaho.2025.102110</doi><url/><notes/><college>COLLEGE NANME</college><department>Mathematics and Computer Science School</department><CollegeCode>COLLEGE CODE</CollegeCode><DepartmentCode>MACS</DepartmentCode><institution>Swansea University</institution><apcterm>Another institution paid the OA fee</apcterm><funders>SR acknowledges financial support by Taighde &#xC9;ireann &#x2013; Research Ireland, under Grant Number 18/CRT/6223.</funders><projectreference/><lastEdited>2025-11-19T10:45:08.7629234</lastEdited><Created>2025-10-31T23:55:35.6829780</Created><path><level id="1">Faculty of Science and Engineering</level><level id="2">School of Mathematics and Computer Science - Computer Science</level></path><authors><author><firstname>Ryan J.</firstname><surname>Brunton</surname><orcid>0009-0009-6199-2764</orcid><order>1</order></author><author><firstname>Soukaina</firstname><surname>Rhazzafe</surname><orcid>0009-0006-5846-4897</orcid><order>2</order></author><author><firstname>Raymond</firstname><surname>Moodley</surname><orcid>0000-0003-4471-2272</orcid><order>3</order></author><author><firstname>Stefan</firstname><surname>Kuhn</surname><orcid>0000-0002-5990-4157</orcid><order>4</order></author><author><firstname>Fabio</firstname><surname>Caraffini</surname><orcid>0000-0001-9199-7368</orcid><order>5</order></author><author><firstname>Sara</firstname><surname>Wilford</surname><orcid>0000-0001-8562-870x</orcid><order>6</order></author><author><firstname>Rachel</firstname><surname>Higginbottom</surname><orcid>0009-0007-9120-7769</orcid><order>7</order></author><author><firstname>Simon</firstname><surname>Colreavy-Donnelly</surname><orcid>0000-0002-1795-6995</orcid><order>8</order></author><author><firstname>Mario</firstname><surname>Gongora</surname><orcid>0000-0002-7135-2092</orcid><order>9</order></author></authors><documents><document><filename>70809__35528__7ac738f5fc5248d884b90e5af7904a72.pdf</filename><originalFilename>1-s2.0-S259029112500840X-main.pdf</originalFilename><uploaded>2025-11-01T18:48:45.0659729</uploaded><type>Output</type><contentLength>2591647</contentLength><contentType>application/pdf</contentType><version>Version of Record</version><cronfaStatus>true</cronfaStatus><documentNotes>&#xA9; 2025 The Author(s). This is an open access article under the CC BY license.</documentNotes><copyrightCorrect>true</copyrightCorrect><language>eng</language><licence>http://creativecommons.org/licenses/by/4.0/</licence></document></documents><OutputDurs/></rfc1807>
spelling 2025-11-19T10:45:08.7629234 v2 70809 2025-10-31 Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education d0b8d4e63d512d4d67a02a23dd20dfdb 0000-0001-9199-7368 Fabio Caraffini Fabio Caraffini true false 2025-10-31 MACS We show that generative AI can support disadvantaged students, improve grades, and help close the attainment gap between pupil premium (PP) and students with special education needs (SEN). It can also alleviate teacher workload, especially for PP and SEN students, by minimising marking and feedback time, enabling better lesson planning and interventions, which can enhance teacher retention and staffing. We focus on disadvantaged students with SEN and low-income families and use AI for personalised feedback and lesson planning in arts and humanities. This enables school leaders and parents to view the qualitative and quantitative student progress. The results of this study demonstrate the potential of using AI-based systems to help close the attainment gap between disadvantaged students and their peers. The intervention given to these pupils would have been an unreasonable demand on the current teacher workload in the UK. Journal Article Social Sciences &amp; Humanities Open 12 102110 Elsevier BV 2590-2911 Disadvantaged pupils; Pupil premium; Feedback; Education; Large language models; Artificial intelligence; Generative AI 29 10 2025 2025-10-29 10.1016/j.ssaho.2025.102110 COLLEGE NANME Mathematics and Computer Science School COLLEGE CODE MACS Swansea University Another institution paid the OA fee SR acknowledges financial support by Taighde Éireann – Research Ireland, under Grant Number 18/CRT/6223. 2025-11-19T10:45:08.7629234 2025-10-31T23:55:35.6829780 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science Ryan J. Brunton 0009-0009-6199-2764 1 Soukaina Rhazzafe 0009-0006-5846-4897 2 Raymond Moodley 0000-0003-4471-2272 3 Stefan Kuhn 0000-0002-5990-4157 4 Fabio Caraffini 0000-0001-9199-7368 5 Sara Wilford 0000-0001-8562-870x 6 Rachel Higginbottom 0009-0007-9120-7769 7 Simon Colreavy-Donnelly 0000-0002-1795-6995 8 Mario Gongora 0000-0002-7135-2092 9 70809__35528__7ac738f5fc5248d884b90e5af7904a72.pdf 1-s2.0-S259029112500840X-main.pdf 2025-11-01T18:48:45.0659729 Output 2591647 application/pdf Version of Record true © 2025 The Author(s). This is an open access article under the CC BY license. true eng http://creativecommons.org/licenses/by/4.0/
title Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education
spellingShingle Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education
Fabio Caraffini
title_short Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education
title_full Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education
title_fullStr Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education
title_full_unstemmed Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education
title_sort Using generative artificial intelligence to enhance the performance of disadvantaged students in secondary education
author_id_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb
author_id_fullname_str_mv d0b8d4e63d512d4d67a02a23dd20dfdb_***_Fabio Caraffini
author Fabio Caraffini
author2 Ryan J. Brunton
Soukaina Rhazzafe
Raymond Moodley
Stefan Kuhn
Fabio Caraffini
Sara Wilford
Rachel Higginbottom
Simon Colreavy-Donnelly
Mario Gongora
format Journal article
container_title Social Sciences &amp; Humanities Open
container_volume 12
container_start_page 102110
publishDate 2025
institution Swansea University
issn 2590-2911
doi_str_mv 10.1016/j.ssaho.2025.102110
publisher Elsevier BV
college_str Faculty of Science and Engineering
hierarchytype
hierarchy_top_id facultyofscienceandengineering
hierarchy_top_title Faculty of Science and Engineering
hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
document_store_str 1
active_str 0
description We show that generative AI can support disadvantaged students, improve grades, and help close the attainment gap between pupil premium (PP) and students with special education needs (SEN). It can also alleviate teacher workload, especially for PP and SEN students, by minimising marking and feedback time, enabling better lesson planning and interventions, which can enhance teacher retention and staffing. We focus on disadvantaged students with SEN and low-income families and use AI for personalised feedback and lesson planning in arts and humanities. This enables school leaders and parents to view the qualitative and quantitative student progress. The results of this study demonstrate the potential of using AI-based systems to help close the attainment gap between disadvantaged students and their peers. The intervention given to these pupils would have been an unreasonable demand on the current teacher workload in the UK.
published_date 2025-10-29T12:45:42Z
_version_ 1850853601191133184
score 11.08895

Similar Items