Actionable companion · Learning design & outcomes
Learning with agency: Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan
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): Early childhood · preschool, Primary school, K–12 · mixed / unspecified, Children · stage unspecified. Verify the participant population and task before transferring the findings.
Original authors’ abstract, reproduced under the linked source license:
Abstract Universal primary education is critical for individual academic growth and overall adult productivity of nations. Estimates indicate that 25% of 59 million primary age out of school children drop out and early grade failure is one of the factors. An objective and feasible screening measure to identify at-risk children in the early grades can help to design appropriate interventions. The objective of this study was to use a Machine Learning algorithm to evaluate the power of Electroencephalogram (EEG) data collected at age 4 in predicting academic achievement at age 8 among rural children in Pakistan. Demographic and EEG data from 96 children of a cohort along with their academic achievement in grade 1–2 measured using an academic achievement test of Math and language at the age of 7–8 years was used to develop the machine learning algorithm. K- Nearest Neighbor (KNN) classifier was used on different model combinations of EEG, sociodemographic and home environment variables. KNN model was evaluated using 5 Stratified Folds based on the sensitivity and specificity. In the current dataset, 55% and 74% failed in the mathematics and language test respectively. On testing data across each fold, the mean sensitivity and specificity was calculated. Sensitivity was similar when EEG variables were combined with sociodemographic, and home environment (Math = 58.7%, Language = 66.3%) variables but specificity improved (Math = 43.4% to 50.6% and Language = 32% to 60%). The model requires further validation for EEG to be used as a screening measure with adequate sensitivity and specificity to identify children in their preschool age who may be at high risk of failure in early grades.
What kind of evidence is this?
Supporting AI learning science · Design requires full-text appraisal
Stage: Early childhood · preschool; Primary school; K–12 · mixed / unspecified; 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
The objective of this study was to use a Machine Learning algorithm to evaluate the power of Electroencephalogram (EEG) data collected at age 4 in predicting academic achievement at age 8 among rural children in Pakistan.
Study methods · source passage
The study sample included 219 children whose EEG data were collected to examine the links between EEG gamma power with cognition at age 4 years in an earlier study [13]. These 219 children were a sub-sample of a large trial birth cohort (n = 1489) exposed to early interventions between birth to 2 years of age [19]. The sub-sample had been randomly selected from the full cohort, stratified by the respective intervention groups at a prospective follow-up at age 4 years [13]. In the previous study, EEG at 4 years was associated with executive functioning at the same age and in the current study we sought to examine if similar association continued with academic achievement at age 8 years. An analysis of the sample included indicated no significant differences on the academic achievement scores when compared to the full sample at 4 years. The included sample had slightly higher IQ (t = 6.1, p = 0.013) and was younger (t = 6, p = 0.014) compared to the rest of the sample. EEG was recorded using a 64-channel high-density Geodesic sensor net (Electrical Geodesics, Inc.; Eugene, OR) and a Net Amps 300 high input amplifier. Continuous EEG was recorded for four blocks of one minute each with a total of four minutes. A central fixation cross was presented on a gray background and a brief silent video with bubbles popping up played between blocks to keep the child engaged [13].
Limitations and uncertainty · source passage
Factors that predict school performance have been an area of key interest for educationists and policy makers [32]. In order to improve outcomes of school performance, identification of high-risk students is critical so that appropriate interventions can be planned to decrease school dropout rate. In this regard, several standardized intelligence tests have been used to assess physical, cognitive, communication, social, emotional, and/or adaptive development in children mostly developed and normed in the US. A significant effort is required to not just adapt the test culturally but also run psychometric analyses to interpret the scores [33]. The test for IQ scores used the WPPSI III [34] and went through extensive adaptation efforts [35]. Another challenge is that although useful, these measures of cognition for preschoolers have shown limited predictive validity regarding academic performance [34]. This can be a major limitation for use of these cognitive tests in low resource settings. An EEG can also be as resource intensive as the recording also takes at least 20 minutes and a lot of post-processing time. However, the advantage is that the EEG could be a more objective biomarker then tests because the psychological tests might be biased by human factors like subjectivity and induce bias of the assessor or compliance and motivation at the level of the child whereas rest-EEG is only biased by artefacts. Additionally, we believe automation of EEG interpretation using artificial intelligence will be a reality soon thus alleviating the need of highly skilled individuals to interpret EEG waveform abnormalities.
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.
- Specify a measurable learning objective, a comparator and a task without tool assistance before adopting a system.
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. Use a brief adult-mediated story, turn-taking or language activity. An educator controls every interaction; the agent never becomes an unsupervised companion. Preserve hands-on play and human conversation. 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. Measure independent naming, explanation or turn-taking in a human-led activity after the tool is removed. Record frustration, overstimulation and the amount of adult help. 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
Muneera A Rasheed; Prem Chand; Saad Ahmed; Hamza Sharif; Zahra Hoodbhoy; Ayat Siddiqui; Babar S Hasan. Use of artificial intelligence on Electroencephalogram (EEG) waveforms to predict failure in early school grades in children from a rural cohort in Pakistan. PLoS ONE (2021-02-08). https://doi.org/10.1371/journal.pone.0246236
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.