Addressing the Ethical, Legal, and Social Issues of Healthtech in Education: Insights From Japan
Bibliographic record
Abstract
The increasing application of health technology (healthtech) in educational settings, particularly for monitoring students' mental health, has garnered significant attention. These technologies, which range from wearable devices to digital mental health screenings, offer new opportunities for enhancing student well-being and strengthening support systems. Numerous studies have explored the ethical, legal, and social issues (ELSIs) of healthtech in the field of psychiatry, highlighting its potential benefits while also acknowledging the inherent complexities and risks that demand careful consideration. However, the ELSIs related to the use of healthtech in educational settings remain largely overlooked and insufficiently addressed. This study provides an overview of items that should be considered by researchers, teachers, and education boards or committees to promote healthtech in the educational context. By adapting existing ELSI frameworks from educational technology and digital health, this study systematically reviews ethical concerns surrounding healthtech in schools. Expert consultations were conducted through a project consisting of members with expertise related to healthtech, including developers, a teacher, a school counselor, and university researchers, leading to the identification of 52 ELSI concerns categorized into 8 domains: consent, rights and privacy, algorithms, information management, evaluation, use, role of public institutions, and relationships with private companies. Using Japan as a case study, we examine regulatory and cultural factors affecting healthtech adoption in schools. The findings reveal critical challenges, such as ensuring informed consent for minors, protecting student privacy, preventing biased algorithmic decision-making, and maintaining transparency in data management. In addition, institutional factors, including the role of public education policies and private-sector involvement, shape the ethical landscape of healthtech implementation. This study highlights the need for multistakeholder collaboration to establish guidelines that balance innovation with ethical responsibility. The study underscores the need for a multifaceted approach to mitigate risks such as data misuse, inequitable access, and algorithmic bias, ensuring the ethical and effective use of healthtech in education. The fundamental ELSI framework for healthtech, including privacy, consent, and algorithms, can be applied to educational systems worldwide, while aspects related to public education policies should be considered in accordance with the specific context of each country and culture. Incorporating healthtech into the educational system helps address the barriers associated with traditional approaches, including limited resources, cost constraints, and logistical challenges. Researchers from universities and healthtech companies, along with educators and other stakeholders, should ensure that healthtech projects consider diverse ELSI concerns at every stage before and during implementation.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.019 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.014 | 0.016 |
| Scholarly communication | 0.008 | 0.006 |
| Open science | 0.001 | 0.010 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".