Actionable companion · Self-regulation & metacognition

Learning with agency: A Technology-Driven Assistive Learning Tool and Framework for Personalized Dyscalculia Interventions

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.

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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): Children · stage unspecified. Verify the participant population and task before transferring the findings.

Original authors’ abstract, reproduced under the linked source license:

Recognizing the impact of mathematical learning difficulties on student achievement, this research focuses on developing adaptive, technology-based solutions for those struggling with learning mathematics, including individuals with dyscalculia. Dyscalculia, a difficulty in understanding numbers and mathematics, can profoundly affect a child’s academic progress and self-confidence. Many interventions aim for broad effectiveness but often struggle to address individual learning differences. This research addresses this gap by employing Dynamic Bayesian Networks (DBNs) within intelligent tutoring systems to develop a personalized, gamified approach for improving mathematical skills in children with dyscalculia. We assessed 158 children aged 6–10 years using the Smartick Dyscalculia Assessment Tool to identify specific numerical cognition deficits. Based on these assessments, we have developed EDSense (Early Detection and Intervention for Insufficient Number Sense), an adaptive web-based learning tool. EDSense provides personalized support and targets skill refinement in mathematics learning. A pre-test and post-test design evaluates EDSense’s effectiveness and demonstrates significant improvements in numerical abilities. The findings highlight the crucial role of adaptive learning platforms in addressing dyscalculia. The EDSense platform demonstrates gamified, self-directed learning environments to enhance both engagement and learning outcomes by accommodating individual cognitive differences. We have proposed a technology-driven framework for personalized dyscalculia interventions, emphasizing early detection to support mathematical skill development.

What kind of evidence is this?

Intelligent tutoring & adaptive learning · Comparative / pre-post study reported

Stage: Children · stage unspecified. 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.

Participant ages · source passage

We assessed 158 children aged 6–10 years using the Smartick Dyscalculia Assessment Tool to identify specific numerical cognition deficits.

Study methods · source passage

As shown in Figure 3, the research involved N = 158 students, aged 6 to 10 years, identified by their teachers as having low mathematics performance. The primary criterion for selection was a performance level below 50%, as determined by the teachers, using convenience sampling. These students were assessed using the Smartick Dyscalculia Assessment Tool, an audio-visual web-based test administered on PCs. This initial assessment revealed that students’ performance was significantly influenced by their proficiency in using the mouse interface. Inexperienced students faced challenges related to precise cursor control, accurate button/item selection, and navigation within the test interface. Factors such as age, previous mouse use experience, and fine motor skills emerged as potential contributors to these difficulties.

Limitations and uncertainty · source passage

The EDSense tool provides valuable insights into student performance by tracking response times, accuracy rates, and cognitive challenges. Its adaptive approach allows educators to customize learning experiences to meet individual student needs. Future research should explore its long-term impact on mathematics proficiency and possible modifications to enhance its effectiveness for students with LDs. Further investigations should examine the role of instructional interventions in mitigating speed-accuracy trade-offs, the effectiveness of personalized support strategies for dyscalculia-affected students, and longitudinal studies assessing the sustained impact of digital learning tools like EDSense on mathematical development.

Design the system before the pilot

Document the learner model, mastery rule, hint sequence and adaptation signal. Compare adaptive routing with the same content in a non-adaptive sequence. Check whether early errors lead to persistent low-expectation pathways.

Use plan–attempt–check–reflect prompts and compare confidence with actual performance. Keep learner choice and reflection visible.
Measure engagement separately from achievement. Check whether novelty-driven participation persists and whether it translates to independent performance.
Use common rubrics, blind scoring where feasible and a fresh unaided reasoning task. Document permissible assistance and avoid treating AI-detector scores as proof of misconduct.

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. Establish the participants’ ages, developmental needs and educational setting from the full paper before selecting an intervention. Use adult-mediated activity and preserve hands-on and peer learning. 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. Evaluate an age-appropriate independent task after the interaction. Do not infer a preschool or primary-school effect when the paper does not establish that population. 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

Dipti Jadhav; Sarat Kumar Chettri; Amiya Kumar Tripathy; Manob Jyoti Saikia. A Technology-Driven Assistive Learning Tool and Framework for Personalized Dyscalculia Interventions. European Journal of Investigation in Health, Psychology and Education (2025-05-15). https://doi.org/10.3390/ejihpe15050085

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.