Actionable companion · Feedback & scaffolding
Learning with agency: 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)
AI-assisted editorial implementation guide, separate from the original research. Proposed actions are not claims or recommendations attributed to the source authors. No independent peer review of this guide is asserted.
Evidence and context
Title and abstract screening; stage labels describe reported settings, not verified numerical ages. Design labels require full-text appraisal. Reported stage(s): Primary school. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
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.
What kind of evidence is this?
Supporting AI learning science · Qualitative / mixed methods
Stage: Primary school. Screening labels describe reported context and study-design language. They are not a risk-of-bias rating or proof of effectiveness for an age band.
Study methods · source passage
This research uses a descriptive qualitative approach with a literature study method to analyze the urgency and potential of using an AI game-based deep learning model in improving the literacy skills of inclusive students with Specific Learning Disorder (SLD) in elementary schools. This approach was chosen because it is suitable for studying phenomena based on secondary data in the form of published literature and scientific documents. 23
Limitations and uncertainty · source passage
The integration of gamification makes literacy learning more engaging, interactive, and pressure-free, while deep learning mechanisms enable real-time personalization based on students’ interaction data. Nevertheless, several limitations remain, such as the risk of student overreliance on games, potential bias in AI systems, and challenges related to infrastructure and teacher competence. Addressing these limitations requires a practical roadmap, including: a) the development of teacher training modules that enhance educators’ technological literacy for AI-supported instruction, b) multi-stakeholder collaboration among schools, government agencies, and educational technology startups, and c) gradual pilot implementation in selected inclusive schools prior to large-scale adoption.
Design the system before the pilot
Use this study to inform a learning mechanism or implementation question. It is not direct evidence that autonomous agents improve learning. Keep the proposed agent behavior separate from what the source actually tested.
- Give feedback on the learner’s reasoning, then fade hints. Record whether the learner can complete the next task without that scaffold.
- Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
- Co-design accommodations with learners and specialists. Check access and error burden across groups without inferring subgroup effectiveness from small or unreported samples.
A 90-day district pilot
- Days 1–15 · Appraise. Have the curriculum lead and a teacher read the methods and limitations. Record the study design, sample, comparator, outcomes and evidence gaps. Define a local question that the study can help inform.
- Days 16–30 · Design. Pilot one teacher-led unit with short guided turns. The tutor asks for an explanation and offers a graduated hint before any worked solution. Use concrete examples and an equivalent task without AI. Name an accountable owner and set the baseline, success threshold and budget before implementation.
- Days 31–60 · Test. Start with a small voluntary cohort, preserve an accessible non-AI route and require teacher review. Use synthetic or de-identified examples; approve data handling and tool terms through district processes.
- Days 61–90 · Decide. Use an age-appropriate independent post-task and a delayed parallel task. Score explanation quality and transfer, not the number of agent messages or completion rate alone. Compare with baseline, document uncertainty and stop or revise if learning worsens, access is unequal or critical errors persist.
The board decision brief
Report the learning problem, evidence quality, participating population, total cost including review time, measured benefit, errors, access differences and remaining uncertainties. Make a dated recommendation to stop, revise or expand. A positive perception survey alone does not establish learning improvement.
Attribution and reuse
Minsih; Coiriyah Widyasari; Ratnasari Diah Utami; Imam Mujahid; Alfi Laila; Saiful Afandi; Hanum Salsabila. 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). F1000Research (2026-03-11). https://doi.org/10.12688/f1000research.171885.1
Companion prepared 2026-10-30. Original authorship and DOI are preserved. The abstract is an attributed source extract; the implementation plan is new AI-assisted editorial material, offered under the same CC BY 4.0 license. This guide has not undergone independent peer review.