Transformative Inclusive Education: A Case Study of an AI Game-Based Deep Learning Model for Literacy among Elementary School Students with Specific Learning Disorder (SLD)

<ns7:p>Background Inclusive students with Specific Learning Disorder (SLD) often experience persistent challenges in acquiring foundational literacy skills due to the lack of adaptive and personalized instructional approaches. Artificial Intelligence (AI) game-based deep learning models offer a promising alternative by enabling individualized learning pathways and continuous feedback. However, the integration of such models within inclusive primary school settings remains underexplored, particularly in Indonesia. Methods This study employed a descriptive qualitative approach using a case study design to investigate the relevance and potential of AI game-based deep learning for literacy development among students with SLD. Data were collected through document analysis, review of scientific publications, and examination of related empirical studies. The collected data were analyzed using qualitative content analysis to identify key themes, conceptual patterns, and implications for inclusive literacy instruction. Results The findings reveal three central insights. First, the adoption of AI game-based deep learning models is urgently needed to address the persistent literacy gaps among inclusive students with SLD. Second, such models demonstrate strong potential in supporting individualized literacy learning, particularly through features such as adaptive difficulty levels, automated feedback, and multimodal engagement. Third, the approach aligns well with the context of Indonesian primary schools, offering a feasible and pedagogically relevant tool for inclusive classrooms. The study also highlights the novelty of conceptualizing AI game-based deep learning not merely as a technological innovation but as a context-sensitive pedagogical model tailored to learners’ needs. Conclusions AI game-based deep learning models hold significant promise for enhancing literacy outcomes among primary school students with SLD. Their adaptive and personalized nature provides meaningful support for inclusive education, helping to reduce learning barriers and promote equitable literacy development. Further research and pilot implementation are recommended to strengthen evidence-based adoption in schools.</ns7:p>

Authors

Institutions

Publication Details

Journal
F1000Research
Published
2026-03-11
DOI
https://doi.org/10.12688/f1000research.171885.1
Primary Topic
E-Learning and COVID-19
Type
article
Field-Weighted Citation Impact
0.00

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Transformative Inclusive Education: A Case Study of an AI Game-Based Deep Learning Model for Literacy among Elementary School Students with Specific Learning Disorder (SLD)

Ratnasari Diah Utami, Saiful Afandi, Hanum Salsabila, Alfi Laila et al.
F1000Research
E-Learning and COVID-19
article

Transformative Inclusive Education: A Case Study of an AI Game-Based Deep Learning Model for Literacy among Elementary School Students with Specific Learning Disorder (SLD)

Ratnasari Diah Utami, Saiful Afandi, Hanum Salsabila, Alfi Laila, Minsih ., Coiriyah Widyasari, Imam Mujahid
article en

Abstract

<ns7:p>Background Inclusive students with Specific Learning Disorder (SLD) often experience persistent challenges in acquiring foundational literacy skills due to the lack of adaptive and personalized instructional approaches. Artificial Intelligence (AI) game-based deep learning models offer a promising alternative by enabling individualized learning pathways and continuous feedback. However, the integration of such models within inclusive primary school settings remains underexplored, particularly in Indonesia. Methods This study employed a descriptive qualitative approach using a case study design to investigate the relevance and potential of AI game-based deep learning for literacy development among students with SLD. Data were collected through document analysis, review of scientific publications, and examination of related empirical studies. The collected data were analyzed using qualitative content analysis to identify key themes, conceptual patterns, and implications for inclusive literacy instruction. Results The findings reveal three central insights. First, the adoption of AI game-based deep learning models is urgently needed to address the persistent literacy gaps among inclusive students with SLD. Second, such models demonstrate strong potential in supporting individualized literacy learning, particularly through features such as adaptive difficulty levels, automated feedback, and multimodal engagement. Third, the approach aligns well with the context of Indonesian primary schools, offering a feasible and pedagogically relevant tool for inclusive classrooms. The study also highlights the novelty of conceptualizing AI game-based deep learning not merely as a technological innovation but as a context-sensitive pedagogical model tailored to learners’ needs. Conclusions AI game-based deep learning models hold significant promise for enhancing literacy outcomes among primary school students with SLD. Their adaptive and personalized nature provides meaningful support for inclusive education, helping to reduce learning barriers and promote equitable literacy development. Further research and pilot implementation are recommended to strengthen evidence-based adoption in schools.</ns7:p>

F1000ResearchVol. 15
Muhammadiyah University of Surakarta (ID), Universitas Islam Batik (ID), Universitas Kadiri (ID)
Direktorat Riset dan Pengabdian Masyarakat, Direcció General de Recerca, Generalitat de Catalunya
Quality Education
Openalex Percentile: Top 26%
E-Learning and COVID-19
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.