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# Artificial Intelligence in ECG Education: A Scoping Review

## Abstract
Artificial intelligence has the potential to revolutionise ECG education by making feedback more immediate and adaptable [1]. This educational draft maps how AI-supported tools are described in undergraduate ECG learning.

## Introduction
ECG interpretation requires deliberate practice, pattern recognition, and clear feedback. Digital tools may help learners revisit concepts at the point of confusion [1, 3].

## Research question
What educational uses of artificial intelligence in ECG learning are described in the literature, and which learner outcomes are reported?

## Methods
This scoping review will search MEDLINE, Embase, and ERIC for English-language educational studies published from 2018 onward.

## Discussion
AI-supported feedback may complement educator-led teaching when its limits are visible and learners can verify claims against source material.

## Limitations
The review may be limited by language restrictions and variation in how educational outcomes are reported.

## References
[1] Patel R, Green M. Digital feedback in ECG education. Medical Education Review. 2022;18(2):44-51.
[2] Lin J. Artificial intelligence and clinical learning. Journal of Medical Learning.

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Clarity

The opening claim is broad. Define the specific learning task before describing impact.

Proposed revision

may support structured, timely feedback

Structure

Methods names databases but not the planned screening approach.

Proposed revision

published from 2018 onward. Two reviewers will screen titles, abstracts, and full texts against predefined eligibility criteria.

Evidence coverage

The Discussion makes a claim about feedback without identifying evidence boundaries.

Proposed revision

against source material. Reported outcomes will be mapped by study design and measurement method.

Academic tone

Clarify that the tool complements educator-led teaching rather than replacing it.

Proposed revision

may complement, rather than replace, educator-led teaching

Missing limitations

Add heterogeneity and publication bias to the limitations section.

Proposed revision

outcomes are reported. Heterogeneity in intervention design and publication bias may also limit comparisons.

Citation consistency

[3] appears in the Introduction but is missing from References.

[2] is a seeded sample reference with incomplete bibliographic details.

Educational writing support. Always verify sources, claims, and citations before submission.