Actionable companion · Learning design & outcomes

Learning with agency: A Novel Tactile Learning Assistive Tool for the Visually and Hearing Impaired with 3D-CNN and Bidirectional LSTM Leveraging Morse Code Technology

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

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

Abstract Educating deafblind children is a highly specialized field that requires computer-assisted learning tools to address the challenges of auditory and visual impairments. The objective is to reduce their difficulties in communication with their peers and to empower them to learn independently in a classroom environment. Braille and assistive tools have become profoundly beneficial for deafblind children, serving as an essential means of communication and knowledge acquisition, enabling them to live independently. This study aims to develop an assistive tool that bridges the limitations of conventional tactile methodologies by incorporating the latest artificial intelligence techniques, enabling children to learn with greater ease. The research leverages Morse code technology to facilitate communication with deafblind children. The speaker’s lip movements are converted into text using the deep learning techniques of a 3D convolutional neural network and a bidirectional long short-term memory neural network. Experimental evaluations of this text conversion model show a word error rate of 2% and an accuracy rate of 98%. The text is then converted into Morse code and communicated to the deafblind child through a wearable device. The significance of this assistive tool lies in its discreet design, resembling a smartwatch. Adolescents can wear the proposed wearable device confidently without feeling self-conscious or embarrassed. Keywords: learning assistive tool, human–computer interaction, hearing and visually impaired, tactile learning, 3D convolutional neural network, LSTM, word error rate, morse code

A 90-day district pilot

1. 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.
2. Days 16–30 · Design. Ask learners to attempt a problem unaided, compare an agent hint, explain a revision and verify the final reasoning against a trusted source. Make the permitted use explicit for each assignment. Name an accountable owner and set the baseline, success threshold and budget before implementation.
3. 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.
4. Days 61–90 · Decide. Measure unaided transfer and delayed retention, calibration of confidence and the ability to identify an incorrect AI explanation. Record whether help-seeking becomes answer copying. 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

Shabana Ziyad Puthu Vedu; May Altulyan; Pradeep Kumar Singh. A Novel Tactile Learning Assistive Tool for the Visually and Hearing Impaired with 3D-CNN and Bidirectional LSTM Leveraging Morse Code Technology. Bioengineering (2025-03-03). https://doi.org/10.3390/bioengineering12030253

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.